From d8a9e6435fb14ee67336f5d597c0472d5bbf3b16 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sat, 27 Apr 2024 18:46:36 +0300 Subject: [PATCH 01/53] Update student.ipynb --- student.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index d3bb34af..d4ba3217 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,7 +7,7 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: \n", + "* Student name: Solphine Joseph\n", "* Student pace: self paced / part time / full time\n", "* Scheduled project review date/time: \n", "* Instructor name: \n", From edf85ba829ba11bb5049eb9a34db1a33f06081cf Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sat, 27 Apr 2024 19:08:04 +0300 Subject: [PATCH 02/53] Update student.ipynb --- student.ipynb | 41 ++++++++++++++++++++++++++++++++++------- 1 file changed, 34 insertions(+), 7 deletions(-) diff --git a/student.ipynb b/student.ipynb index d4ba3217..a39a5a52 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,20 +7,47 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: Solphine Joseph\n", - "* Student pace: self paced / part time / full time\n", + "* Student name: Solphine Joseph, Grace Rotich, Mather Rotich, Hilary Simiyu, Clyde Ochieng.\n", + "* Student pace: full time\n", "* Scheduled project review date/time: \n", - "* Instructor name: \n", + "* Instructor name: Nikita \n", "* Blog post URL:\n" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Problem\n", + "\n", + "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Your code here - remember to use markdown cells for comments as well!" + "### Objectives\n", + "* To determine the key factors influencing house prices.\n", + "* To develop multilinear regression models to predict house prices based on relevant features.\n", + "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" ] } ], From 9a914828148112c36789f9fdad61f7b00a90897b Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sat, 27 Apr 2024 19:08:08 +0300 Subject: [PATCH 03/53] Update README.md --- README.md | 288 ++---------------------------------------------------- 1 file changed, 8 insertions(+), 280 deletions(-) diff --git a/README.md b/README.md index 5dd0f84d..39359350 100644 --- a/README.md +++ b/README.md @@ -1,285 +1,13 @@ # Phase 2 Project Description +## Overview -Another module down - you're almost half way there! -![awesome](https://raw.githubusercontent.com/learn-co-curriculum/dsc-phase-2-project-v2-3/main/halfway-there.gif) +A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services. +## Business Problem -All that remains in Phase 2 is to put your newfound data science skills to use with a large project! +In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment. +### Objectives -In this project description, we will cover: - -* Project Overview: the project goal, audience, and dataset -* Deliverables: the specific items you are required to produce for this project -* Grading: how your project will be scored -* Getting Started: guidance for how to begin working - -## Project Overview - -For this project, you will use multiple linear regression modeling to analyze house sales in a northwestern county. - -### Business Problem - -It is up to you to define a stakeholder and business problem appropriate to this dataset. - -If you are struggling to define a stakeholder, we recommend you complete a project for a real estate agency that helps homeowners buy and/or sell homes. A business problem you could focus on for this stakeholder is the need to provide advice to homeowners about how home renovations might increase the estimated value of their homes, and by what amount. - -### The Data - -This project uses the King County House Sales dataset, which can be found in `kc_house_data.csv` in the data folder in this assignment's GitHub repository. The description of the column names can be found in `column_names.md` in the same folder. As with most real world data sets, the column names are not perfectly described, so you'll have to do some research or use your best judgment if you have questions about what the data means. - -It is up to you to decide what data from this dataset to use and how to use it. If you are feeling overwhelmed or behind, we recommend you **ignore** some or all of the following features: - -* `date` -* `view` -* `sqft_above` -* `sqft_basement` -* `yr_renovated` -* `zipcode` -* `lat` -* `long` -* `sqft_living15` -* `sqft_lot15` - -### Key Points - -* **Your goal in regression modeling is to yield findings to support relevant recommendations. Those findings should include a metric describing overall model performance as well as at least two regression model coefficients.** As you explore the data and refine your stakeholder and business problem definitions, make sure you are also thinking about how a linear regression model adds value to your analysis. "The assignment was to use linear regression" is not an acceptable answer! You can also use additional statistical techniques other than linear regression, so long as you clearly explain why you are using each technique. - -* **You should demonstrate an iterative approach to modeling.** This means that you must build multiple models. Begin with a basic model, evaluate it, and then provide justification for and proceed to a new model. After you finish refining your models, you should provide 1-3 paragraphs in the notebook discussing your final model. - -* **Data visualization and analysis are no longer explicit project requirements, but they are still very important.** In Phase 1, your project stopped earlier in the CRISP-DM process. Now you are going a step further, to modeling. Data visualization and analysis will help you build better models and tell a better story to your stakeholders. - -## Deliverables - -There are three deliverables for this project: - -* A **non-technical presentation** -* A **Jupyter Notebook** -* A **GitHub repository** - -The deliverables requirements are almost the same as in the Phase 1 Project, and you can review those extended descriptions [here](https://github.com/learn-co-curriculum/dsc-phase-1-project-v2-3#deliverables). In general, everything is the same except the "Data Visualization" and "Data Analysis" requirements have been replaced by "Modeling" and "Regression Results" requirements. - -### Non-Technical Presentation - -Recall that the non-technical presentation is a slide deck presenting your analysis to ***business stakeholders***, and should be presented live as well as submitted in PDF form on Canvas. - -We recommend that you follow this structure, although the slide titles should be specific to your project: - -1. Beginning - - Overview - - Business and Data Understanding -2. Middle - - **Modeling** - - **Regression Results** -3. End - - Recommendations - - Next Steps - - Thank you - -Make sure that your discussion of modeling and regression results is geared towards a non-technical audience! Assume that their prior knowledge of regression modeling is minimal. You don't need to explain how linear regression works, but you should explain why linear regression is useful for the problem context. Make sure you translate any metrics or coefficients into their plain language implications. - -The graded elements for the non-technical presentation are the same as in [Phase 1](https://github.com/learn-co-curriculum/dsc-phase-1-project-v2-3#deliverables). - -### Jupyter Notebook - -Recall that the Jupyter Notebook is a notebook that uses Python and Markdown to present your analysis to a ***data science audience***. You will submit the notebook in PDF format on Canvas as well as in `.ipynb` format in your GitHub repository. - -The graded elements for the Jupyter Notebook are: - -* Business Understanding -* Data Understanding -* Data Preparation -* **Modeling** -* **Regression Results** -* Code Quality - -### GitHub Repository - -Recall that the GitHub repository is the cloud-hosted directory containing all of your project files as well as their version history. - -The requirements are the same as in [Phase 1](https://github.com/learn-co-curriculum/dsc-phase-1-project-v2-3#github-repository), except for the required sections in the `README.md`. - -For this project, the `README.md` file should contain: - -* Overview -* Business and Data Understanding - * Explain your stakeholder audience here -* **Modeling** -* **Regression Results** -* Conclusion - -Just like in Phase 1, the `README.md` file should be the bridge between your non technical presentation and the Jupyter Notebook. It should not contain the code used to develop your analysis, but should provide a more in-depth explanation of your methodology and analysis than what is described in your presentation slides. - -## Grading - -***To pass this project, you must pass each project rubric objective.*** The project rubric objectives for Phase 2 are: - -1. Attention to Detail -2. Statistical Communication -3. Data Preparation Fundamentals -4. Linear Modeling - -### Attention to Detail - -Just like in Phase 1, this rubric objective is based on your completion of checklist items. ***In Phase 2, you need to complete 70% (7 out of 10) or more of the checklist elements in order to pass the Attention to Detail objective.*** - -**NOTE THAT THE PASSING BAR IS HIGHER IN PHASE 2 THAN IT WAS IN PHASE 1!** - -The standard will increase with each Phase, until you will be required to complete all elements to pass Phase 5 (Capstone). - -#### Exceeds Objective - -80% or more of the project checklist items are complete - -#### Meets Objective (Passing Bar) - -70% of the project checklist items are complete - -#### Approaching Objective - -60% of the project checklist items are complete - -#### Does Not Meet Objective - -50% or fewer of the project checklist items are complete - -### Statistical Communication - -Recall that communication is one of the key data science "soft skills". In Phase 2, we are specifically focused on Statistical Communication. We define Statistical Communication as: - -> Communicating **results of statistical analyses** to diverse audiences via writing and live presentation - -Note that this is the same as in Phase 1, except we are replacing "basic data analysis" with "statistical analyses". - -High-quality Statistical Communication includes rationale, results, limitations, and recommendations: - -* **Rationale:** Explaining why you are using statistical analyses rather than basic data analysis - * For example, why are you using regression coefficients rather than just a graph? - * What about the problem or data is suitable for this form of analysis? - * For a data science audience, this includes your reasoning for the changes you applied while iterating between models. -* **Results:** Describing the overall model metrics and feature coefficients - * You need at least one overall model metric (e.g. r-squared or RMSE) and at least two feature coefficients. - * For a business audience, make sure you connect any metrics to real-world implications. You do not need to get into the details of how linear regression works. - * For a data science audience, you don't need to explain what a metric is, but make sure you explain why you chose that particular one. -* **Limitations:** Identifying the limitations and/or uncertainty present in your analysis - * This could include p-values/alpha values, confidence intervals, assumptions of linear regression, missing data, etc. - * In general, this should be more in-depth for a data science audience and more surface-level for a business audience. -* **Recommendations:** Interpreting the model results and limitations in the context of the business problem - * What should stakeholders _do_ with this information? - -#### Exceeds Objective - -Communicates the rationale, results, limitations, and specific recommendations of statistical analyses - -> See above for extended explanations of these terms. - -#### Meets Objective (Passing Bar) - -Successfully communicates the results of statistical analyses without any major errors - -> The minimum requirement is to communicate the _results_, meaning at least one overall model metric (e.g. r-squared or RMSE) as well as at least two feature coefficients. See the Approaching Objective section for an explanation of what a "major error" means. - -#### Approaching Objective - -Communicates the results of statistical analyses with at least one major error - -> A major error means that some aspect of your explanation is fundamentally incorrect. For example, if a feature coefficient is negative and you say that an increase in that feature results in an increase of the target, that would be a major error. Another example would be if you say that the feature with the highest coefficient is the "most statistically significant" while ignoring the p-value. One more example would be reporting a coefficient that is not statistically significant, rather than saying "no statistically significant linear relationship was found" - -> "**If a coefficient's t-statistic is not significant, don't interpret it at all.** You can't be sure that the value of the corresponding parameter in the underlying regression model isn't really zero." _DeVeaux, Velleman, and Bock (2012), Stats: Data and Models, 3rd edition, pg. 801_. Check out [this website](https://web.ma.utexas.edu/users/mks/statmistakes/TOC.html) for extensive additional examples of mistakes using statistics. - -> The easiest way to avoid making a major error is to have someone double-check your work. Reach out to peers on Slack and ask them to confirm whether your interpretation makes sense! - -#### Does Not Meet Objective - -Does not communicate the results of statistical analyses - -> It is not sufficient to just display the entire results summary. You need to pull out at least one overall model metric (e.g. r-squared, RMSE) and at least two feature coefficients, and explain what those numbers mean. - -### Data Preparation Fundamentals - -We define this objective as: - -> Applying appropriate **preprocessing** and feature engineering steps to tabular data in preparation for statistical modeling - -The two most important components of preprocessing for the Phase 2 project are: - -* **Handling Missing Values:** Missing values may be present in the features you want to use, either encoded as `NaN` or as some other value such as `"?"`. Before you can build a linear regression model, make sure you identify and address any missing values using techniques such as dropping or replacing data. -* **Handling Non-Numeric Data:** A linear regression model needs all of the features to be numeric, not categorical. For this project, ***be sure to pick at least one non-numeric feature and try including it in a model.*** You can identify that a feature is currently non-numeric if the type is `object` when you run `.info()` on your dataframe. Once you have identified the non-numeric features, address them using techniques such as ordinal or one-hot (dummy) encoding. - -There is no single correct way to handle either of these situations! Use your best judgement to decide what to do, and be sure to explain your rationale in the Markdown of your notebook. - -Feature engineering is encouraged but not required for this project. - -#### Exceeds Objective - -Goes above and beyond with data preparation, such as feature engineering or merging in outside datasets - -> One example of feature engineering could be using the `date` feature to create a new feature called `season`, which represents whether the home was sold in Spring, Summer, Fall, or Winter. - -> One example of merging in outside datasets could be finding data based on ZIP Code, such as household income or walkability, and joining that data with the provided CSV. - -#### Meets Objective (Passing Bar) - -Successfully prepares data for modeling, including converting at least one non-numeric feature into ordinal or binary data and handling missing data as needed - -> As a reminder, you can identify the non-numeric features by calling `.info()` on the dataframe and looking for type `object`. - -> Your final model does not necessarily need to include any features that were originally non-numeric, but you need to demonstrate your ability to handle this type of data. - -#### Approaching Objective - -Prepares some data successfully, but is unable to utilize non-numeric data - -> If you simply subset the dataframe to only columns with type `int64` or `float64`, your model will run, but you will not pass this objective. - -#### Does Not Meet Objective - -Does not prepare data for modeling - -### Linear Modeling - -According to [Kaggle's 2020 State of Data Science and Machine Learning Survey](https://www.kaggle.com/kaggle-survey-2020), linear and logistic regression are the most popular machine learning algorithms, used by 83.7% of data scientists. They are small, fast models compared to some of the models you will learn later, but have limitations in the kinds of relationships they are able to learn. - -In this project you are required to use linear regression as the primary statistical analysis, although you are free to use additional statistical techniques as appropriate. - -#### Exceeds Objective - -Goes above and beyond in the modeling process, such as recursive feature selection - -#### Meets Objective (Passing Bar) - -Successfully builds a baseline model as well as at least one iterated model, and correctly extracts insights from a final model without any major errors - -> We are looking for you to (1) create a baseline model, (2) iterate on that model, making adjustments that are supported by regression theory or by descriptive analysis of the data, and (3) select a final model and report on its metrics and coefficients - -> Ideally you would include written justifications for each model iteration, but at minimum the iterations must be _justifiable_ - -> For an explanation of "major errors", see the description below - -#### Approaching Objective - -Builds multiple models with at least one major error - -> The number one major error to avoid is including the target as one of your features. For example, if the target is `price` you should NOT make a "price per square foot" feature, because that feature would not be available if you didn't already know the price. - -> Other examples of major errors include: using a target other than `price`, attempting only simple linear regression (not multiple linear regression), dropping multiple one-hot encoded columns without explaining the resulting baseline, or using a unique identifier (`id` in this dataset) as a feature. - -#### Does Not Meet Objective - -Does not build multiple linear regression models - -## Getting Started - -Please start by reviewing the contents of this project description. If you have any questions, please ask your instructor ASAP. - -Next, you will need to complete the [***Project Proposal***](#project_proposal) which must be reviewed by your instructor before you can continue with the project. - -Here are some suggestions for creating your GitHub repository: - -1. Fork the [Phase 2 Project Repository](https://github.com/learn-co-curriculum/dsc-phase-2-project-v2-3), clone it locally, and work in the `student.ipynb` file. Make sure to also add and commit a PDF of your presentation to your repository with a file name of `presentation.pdf`. -2. Or, create a new repository from scratch by going to [github.com/new](https://github.com/new) and copying the data files from the Phase 2 Project Repository into your new repository. - - Recall that you can refer to the [Phase 1 Project Template](https://github.com/learn-co-curriculum/dsc-project-template) as an example structure - - This option will result in the most professional-looking portfolio repository, but can be more complicated to use. So if you are getting stuck with this option, try forking the project repository instead - -## Summary - -This is your first modeling project! Take what you have learned in Phase 2 to create a project with a more sophisticated analysis than you completed in Phase 1. You will build on these skills as we move into the predictive machine learning mindset in Phase 3. You've got this! +* To determine the key factors influencing house prices. +* To develop multilinear regression models to predict house prices based on relevant features. +* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties. From 44945a8b49c270aaa7febb792115f4b93f5d9c61 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sat, 27 Apr 2024 23:50:20 +0300 Subject: [PATCH 04/53] Update student.ipynb --- student.ipynb | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/student.ipynb b/student.ipynb index a39a5a52..1b57076a 100644 --- a/student.ipynb +++ b/student.ipynb @@ -49,6 +49,44 @@ "* To develop multilinear regression models to predict house prices based on relevant features.\n", "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data Understanding:\n", + "\n", + "The real estate agency in Kingsway is analyzing a dataset to determine the factors affecting house prices. The dataset likely includes features such as property size, location, age, and market trends. Key steps include assessing data quality, exploring relationships between features and prices, and preprocessing data for multilinear regression analysis. Multilinear regression will be used to model how these features collectively influence house prices, with evaluation metrics used to assess predictive accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### King County Housing Data Columns \n", + "\n", + "* `id` - Unique identifier for a house\n", + "* `date` - Date house was sold\n", + "* `price` - Sale price (prediction target)\n", + "* `bedrooms` - Number of bedrooms\n", + "* `bathrooms` - Number of bathrooms\n", + "* `sqft_living` - Square footage of living space in the home\n", + "* `sqft_lot` - Square footage of the lot\n", + "* `floors` - Number of floors (levels) in house\n", + "* `waterfront` - Whether the house is on a waterfront\n", + "* `view` - Quality of view from house\n", + "* `condition` - How good the overall condition of the house is. \n", + "* `grade` - Overall grade of the house. \n", + "* `sqft_above` - Square footage of house apart from basement \n", + "* `sqft_basement` - Square footage of the basement – (Ignored)\n", + "* `yr_built` - Year when house was built\n", + "* `yr_renovated` - Year when house was renovated – (Ignored)\n", + "* `zipcode` - ZIP Code used by the United States Postal Service \n", + "* `lat` - Latitude coordinate\n", + "* `long` - Longitude coordinate\n", + "* `sqft_living15` - The square footage of interior housing living space for the nearest 15 neighbors\n", + "* `sqft_lot15` - The square footage of the land lots of the nearest 15 neighbors" + ] } ], "metadata": { From cc69bf6eaebae6771f6177b5754a3335140cdc07 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sat, 27 Apr 2024 23:50:27 +0300 Subject: [PATCH 05/53] Update README.md --- README.md | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 39359350..7f581e17 100644 --- a/README.md +++ b/README.md @@ -2,12 +2,6 @@ ## Overview -A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services. -## Business Problem +Data Understanding: -In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment. -### Objectives - -* To determine the key factors influencing house prices. -* To develop multilinear regression models to predict house prices based on relevant features. -* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties. +The real estate agency in Kingsway is analyzing a dataset to determine the factors affecting house prices. The dataset likely includes features such as property size, location, age, and market trends. Key steps include assessing data quality, exploring relationships between features and prices, and preprocessing data for multilinear regression analysis. Multilinear regression will be used to model how these features collectively influence house prices, with evaluation metrics used to assess predictive accuracy. \ No newline at end of file From 69d9b53e29fe61e9d1838e8fbe203b883e713f09 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sat, 27 Apr 2024 23:54:15 +0300 Subject: [PATCH 06/53] Update student.ipynb --- student.ipynb | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 1b57076a..567e978d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -87,6 +87,32 @@ "* `sqft_living15` - The square footage of interior housing living space for the nearest 15 neighbors\n", "* `sqft_lot15` - The square footage of the land lots of the nearest 15 neighbors" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explotory Data Analyis\n", + "\n", + "Importing data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#load necessary modules \n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import scipy.stats as stats\n", + "import statsmodels.formula.api as smf\n", + "import statsmodels.stats.api as sms\n", + "import statsmodels.api as sm" + ] } ], "metadata": { @@ -105,7 +131,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.10.13" } }, "nbformat": 4, From 47f9a238257ff095bf0edf0818244fa726a9fbe4 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sun, 28 Apr 2024 18:27:02 +0300 Subject: [PATCH 07/53] Update student.ipynb --- student.ipynb | 394 +++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 386 insertions(+), 8 deletions(-) diff --git a/student.ipynb b/student.ipynb index 567e978d..013a05d1 100644 --- a/student.ipynb +++ b/student.ipynb @@ -99,19 +99,397 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "#load necessary modules \n", - "import numpy as np\n", + "#import libraries\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", + "import numpy as np\n", "import scipy.stats as stats\n", - "import statsmodels.formula.api as smf\n", - "import statsmodels.stats.api as sms\n", - "import statsmodels.api as sm" + "import math\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.impute import MissingIndicator\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Removing irrelvant columns" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontview...gradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538000.032.25257072422.0NONONE...7 Average2170400.019511991.09812547.7210-122.31916907639
256315004002/25/2015180000.021.00770100001.0NONONE...6 Low Average7700.01933NaN9802847.7379-122.23327208062
3248720087512/9/2014604000.043.00196050001.0NONONE...7 Average1050910.019650.09813647.5208-122.39313605000
419544005102/18/2015510000.032.00168080801.0NONONE...8 Good16800.019870.09807447.6168-122.04518007503
\n", + "

5 rows × 21 columns

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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#load and preiview data\n", + "df = pd.read_csv('data/kc_house_data.csv')\n", + "df.head(5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before conducting regression analysis, six non-numeric columns in the data—\"date\", \"waterfront\", \"view\", \"condition\", \"grade\", and \"sqft_basement\"—will require manipulation or removal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Cleaning\n", + "Drop irrelevant columns, address missing values and manipulate data into desired forms" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "#drop irrelevant columns\n", + "df.drop(['id', 'date', 'zipcode', 'lat', 'long', 'yr_renovated', 'view'],\n", + " axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#fill in missing sqft_basement values\n", + "df.loc[df.sqft_basement == '?', 'sqft_basement'] = (\n", + " df[df.sqft_basement == '?'].sqft_living - \n", + " df[df.sqft_basement == '?'].sqft_above\n", + ")\n", + "\n", + "#convert into numeric\n", + "df['sqft_basement'] = df.sqft_basement.astype('float64')\n", + "\n", + "#sqft_basement is a zero inflated variable, so I convert it into \n", + "#a categorical variable\n", + "df['is_basement'] = df.sqft_basement.map(lambda x: 0 if x == 0 else 1)\n", + "df.drop('sqft_basement', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#convert condition and grade into numeric values\n", + "df['condition'] = df.condition.map(lambda x: 0 if x=='Poor' \n", + " else (1 if x=='Fair'\n", + " else (2 if x=='Average'\n", + " else (3 if x=='Good' else 4))))\n", + "\n", + "df['grade'] = df.grade.map(lambda x: int(x[0:2]) - 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#convert waterfront strings to 0 and 1\n", + "df['waterfront'] = df.waterfront.map(lambda x: 0 if x==\"NO\" \n", + " else (1 if x==\"YES\" else None))\n", + "\n", + "#create new column indicating if waterfront value is missing\n", + "waterfront = df[[\"waterfront\"]]\n", + "missing_indicator = MissingIndicator()\n", + "missing_indicator.fit(waterfront)\n", + "waterfront_missing = missing_indicator.transform(waterfront)\n", + "\n", + "#add waterfront missing to dataframe and convert to binary\n", + "df['waterfront_missing'] = waterfront_missing\n", + "\n", + "df['waterfront_missing'] = df.waterfront_missing.map(lambda x: 0 if x==False\n", + " else 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#fill in missing waterfront values with median\n", + "imputer = SimpleImputer(strategy=\"median\")\n", + "\n", + "imputer.fit(waterfront)\n", + "waterfront_imputed = imputer.transform(waterfront)\n", + "\n", + "df['waterfront'] = waterfront_imputed" ] } ], From f7904674776620894a789f559ddd99ba88078fdf Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sun, 28 Apr 2024 18:27:52 +0300 Subject: [PATCH 08/53] Update student.ipynb --- student.ipynb | 159 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) diff --git a/student.ipynb b/student.ipynb index 013a05d1..11c2db8d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -491,6 +491,165 @@ "\n", "df['waterfront'] = waterfront_imputed" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Removing irrelvant columns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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07129300520221900.031.00118056501.0NaNAverage7 Average1955
16414100192538000.032.25257072422.0NOAverage7 Average1951
25631500400180000.021.00770100001.0NOAverage6 Low Average1933
32487200875604000.043.00196050001.0NOVery Good7 Average1965
41954400510510000.032.00168080801.0NOAverage8 Good1987
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" + ], + "text/plain": [ + " id price bedrooms bathrooms sqft_living sqft_lot floors \\\n", + "0 7129300520 221900.0 3 1.00 1180 5650 1.0 \n", + "1 6414100192 538000.0 3 2.25 2570 7242 2.0 \n", + "2 5631500400 180000.0 2 1.00 770 10000 1.0 \n", + "3 2487200875 604000.0 4 3.00 1960 5000 1.0 \n", + "4 1954400510 510000.0 3 2.00 1680 8080 1.0 \n", + "\n", + " waterfront condition grade yr_built \n", + "0 NaN Average 7 Average 1955 \n", + "1 NO Average 7 Average 1951 \n", + "2 NO Average 6 Low Average 1933 \n", + "3 NO Very Good 7 Average 1965 \n", + "4 NO Average 8 Good 1987 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reading the csv file\n", + "# drop irrelevant columns\n", + "df = pd.read_csv('data/kc_house_data.csv').drop(['date',\n", + " 'view', \n", + " 'sqft_above', \n", + " 'sqft_basement', \n", + " 'yr_renovated',\n", + " 'zipcode', \n", + " 'lat', \n", + " 'long', \n", + " 'sqft_living15',\n", + " 'sqft_lot15'], axis = 1)\n", + "# previewing the DataFrame\n", + "df.head()" + ] } ], "metadata": { From d0245ea30107cb1beee26f9fa15cc74814b104f5 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sun, 28 Apr 2024 19:54:11 +0300 Subject: [PATCH 09/53] Update student.ipynb --- student.ipynb | 341 ++++++++++++++++++++------------------------------ 1 file changed, 134 insertions(+), 207 deletions(-) diff --git a/student.ipynb b/student.ipynb index 11c2db8d..52ce98fd 100644 --- a/student.ipynb +++ b/student.ipynb @@ -54,7 +54,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Data Understanding:\n", + "### Data Understanding:\n", "\n", "The real estate agency in Kingsway is analyzing a dataset to determine the factors affecting house prices. The dataset likely includes features such as property size, location, age, and market trends. Key steps include assessing data quality, exploring relationships between features and prices, and preprocessing data for multilinear regression analysis. Multilinear regression will be used to model how these features collectively influence house prices, with evaluation metrics used to assess predictive accuracy." ] @@ -99,21 +99,26 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ - "#import libraries\n", + "#importing libraries \n", "import pandas as pd\n", "import numpy as np\n", - "import scipy.stats as stats\n", - "import math\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.impute import MissingIndicator\n", - "from sklearn.impute import SimpleImputer\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn import metrics\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.linear_model import LinearRegression\n", + "import statsmodels.api as sm\n", + "from statsmodels.formula.api import ols\n", + "from scipy import stats\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn import tree\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn import preprocessing" + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.dummy import DummyRegressor" ] }, { @@ -125,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -177,12 +182,12 @@ " 0\n", " 7129300520\n", " 10/13/2014\n", - " 221900.0\n", + " 221900.00000\n", " 3\n", - " 1.00\n", + " 1.00000\n", " 1180\n", " 5650\n", - " 1.0\n", + " 1.00000\n", " NaN\n", " NONE\n", " ...\n", @@ -190,10 +195,10 @@ " 1180\n", " 0.0\n", " 1955\n", - " 0.0\n", + " 0.00000\n", " 98178\n", - " 47.5112\n", - " -122.257\n", + " 47.51120\n", + " -122.25700\n", " 1340\n", " 5650\n", " \n", @@ -201,12 +206,12 @@ " 1\n", " 6414100192\n", " 12/9/2014\n", - " 538000.0\n", + " 538000.00000\n", " 3\n", - " 2.25\n", + " 2.25000\n", " 2570\n", " 7242\n", - " 2.0\n", + " 2.00000\n", " NO\n", " NONE\n", " ...\n", @@ -214,10 +219,10 @@ " 2170\n", " 400.0\n", " 1951\n", - " 1991.0\n", + " 1991.00000\n", " 98125\n", - " 47.7210\n", - " -122.319\n", + " 47.72100\n", + " -122.31900\n", " 1690\n", " 7639\n", " \n", @@ -225,12 +230,12 @@ " 2\n", " 5631500400\n", " 2/25/2015\n", - " 180000.0\n", + " 180000.00000\n", " 2\n", - " 1.00\n", + " 1.00000\n", " 770\n", " 10000\n", - " 1.0\n", + " 1.00000\n", " NO\n", " NONE\n", " ...\n", @@ -240,8 +245,8 @@ " 1933\n", " NaN\n", " 98028\n", - " 47.7379\n", - " -122.233\n", + " 47.73790\n", + " -122.23300\n", " 2720\n", " 8062\n", " \n", @@ -249,12 +254,12 @@ " 3\n", " 2487200875\n", " 12/9/2014\n", - " 604000.0\n", + " 604000.00000\n", " 4\n", - " 3.00\n", + " 3.00000\n", " 1960\n", " 5000\n", - " 1.0\n", + " 1.00000\n", " NO\n", " NONE\n", " ...\n", @@ -262,10 +267,10 @@ " 1050\n", " 910.0\n", " 1965\n", - " 0.0\n", + " 0.00000\n", " 98136\n", - " 47.5208\n", - " -122.393\n", + " 47.52080\n", + " -122.39300\n", " 1360\n", " 5000\n", " \n", @@ -273,12 +278,12 @@ " 4\n", " 1954400510\n", " 2/18/2015\n", - " 510000.0\n", + " 510000.00000\n", " 3\n", - " 2.00\n", + " 2.00000\n", " 1680\n", " 8080\n", - " 1.0\n", + " 1.00000\n", " NO\n", " NONE\n", " ...\n", @@ -286,10 +291,10 @@ " 1680\n", " 0.0\n", " 1987\n", - " 0.0\n", + " 0.00000\n", " 98074\n", - " 47.6168\n", - " -122.045\n", + " 47.61680\n", + " -122.04500\n", " 1800\n", " 7503\n", " \n", @@ -299,26 +304,26 @@ "" ], "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.00000 3 1.00000 1180 \n", + "1 6414100192 12/9/2014 538000.00000 3 2.25000 2570 \n", + "2 5631500400 2/25/2015 180000.00000 2 1.00000 770 \n", + "3 2487200875 12/9/2014 604000.00000 4 3.00000 1960 \n", + "4 1954400510 2/18/2015 510000.00000 3 2.00000 1680 \n", "\n", " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "0 5650 1.00000 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.00000 NO NONE ... 7 Average 2170 \n", + "2 10000 1.00000 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.00000 NO NONE ... 7 Average 1050 \n", + "4 8080 1.00000 NO NONE ... 8 Good 1680 \n", "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.00000 98178 47.51120 -122.25700 \n", + "1 400.0 1951 1991.00000 98125 47.72100 -122.31900 \n", + "2 0.0 1933 NaN 98028 47.73790 -122.23300 \n", + "3 910.0 1965 0.00000 98136 47.52080 -122.39300 \n", + "4 0.0 1987 0.00000 98074 47.61680 -122.04500 \n", "\n", " sqft_living15 sqft_lot15 \n", "0 1340 5650 \n", @@ -330,7 +335,7 @@ "[5 rows x 21 columns]" ] }, - "execution_count": 14, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -338,12 +343,12 @@ "source": [ "#load and preiview data\n", "df = pd.read_csv('data/kc_house_data.csv')\n", - "df.head(5)\n" + "df.head()\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -389,7 +394,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before conducting regression analysis, six non-numeric columns in the data—\"date\", \"waterfront\", \"view\", \"condition\", \"grade\", and \"sqft_basement\"—will require manipulation or removal." + "We see that waterfrront is missing about 11% of its values, year renovated is missing about 17% of its values and view is missing a few values as well. " ] }, { @@ -403,13 +408,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Data Cleaning\n", - "Drop irrelevant columns, address missing values and manipulate data into desired forms" + "### Data Cleaning\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -420,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -474,12 +478,12 @@ "df['waterfront_missing'] = waterfront_missing\n", "\n", "df['waterfront_missing'] = df.waterfront_missing.map(lambda x: 0 if x==False\n", - " else 1)" + " else 1)\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -493,162 +497,85 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 58, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 19479 entries, 0 to 21596\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 price 19479 non-null float64\n", + " 1 bedrooms 19479 non-null float64\n", + " 2 bathrooms 19479 non-null float64\n", + " 3 sqft_living 19479 non-null float64\n", + " 4 sqft_lot 19479 non-null float64\n", + " 5 floors 19479 non-null float64\n", + " 6 sqft_above 19479 non-null float64\n", + " 7 sqft_living15 19479 non-null float64\n", + " 8 sqft_lot15 19479 non-null float64\n", + " 9 grade_num 19479 non-null float64\n", + " 10 bed_bath_ratio 19479 non-null float64\n", + " 11 mean_price 19479 non-null float64\n", + "dtypes: float64(12)\n", + "memory usage: 1.9 MB\n" + ] + } + ], "source": [ - "##### Removing irrelvant columns" + "#removing outliers \n", + "\n", + "#make a copy of the clean dataframe \n", + "no_out = main_df.copy()\n", + "\n", + "#drop columns that we cannot use \n", + "no_out = no_out.drop(columns= ['id', 'zip_city', 'Waterfront'], axis=1)\n", + "\n", + "#change data type so that we can math \n", + "no_out = no_out.astype('float')\n", + "\n", + "#pull out the columns \n", + "columns = no_out.columns\n", + "\n", + "#for each column in the dataframe, get the mean and standard deviation \n", + "#then get the z-score for within 3 standard devaitions\n", + "for col in columns:\n", + " \n", + " mean = no_out[col].mean()\n", + " sd = no_out[col].std()\n", + " \n", + " no_out = no_out[(no_out[col] <= mean+(3*sd))]\n", + " \n", + "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n", + "no_out.info()" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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16414100192538000.032.25257072422.0NOAverage7 Average1951
25631500400180000.021.00770100001.0NOAverage6 Low Average1933
32487200875604000.043.00196050001.0NOVery Good7 Average1965
41954400510510000.032.00168080801.0NOAverage8 Good1987
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"text/plain": [ - " id price bedrooms bathrooms sqft_living sqft_lot floors \\\n", - "0 7129300520 221900.0 3 1.00 1180 5650 1.0 \n", - "1 6414100192 538000.0 3 2.25 2570 7242 2.0 \n", - "2 5631500400 180000.0 2 1.00 770 10000 1.0 \n", - "3 2487200875 604000.0 4 3.00 1960 5000 1.0 \n", - "4 1954400510 510000.0 3 2.00 1680 8080 1.0 \n", - "\n", - " waterfront condition grade yr_built \n", - "0 NaN Average 7 Average 1955 \n", - "1 NO Average 7 Average 1951 \n", - "2 NO Average 6 Low Average 1933 \n", - "3 NO Very Good 7 Average 1965 \n", - "4 NO Average 8 Good 1987 " + "
" ] }, - "execution_count": 2, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# reading the csv file\n", - "# drop irrelevant columns\n", - "df = pd.read_csv('data/kc_house_data.csv').drop(['date',\n", - " 'view', \n", - " 'sqft_above', \n", - " 'sqft_basement', \n", - " 'yr_renovated',\n", - " 'zipcode', \n", - " 'lat', \n", - " 'long', \n", - " 'sqft_living15',\n", - " 'sqft_lot15'], axis = 1)\n", - "# previewing the DataFrame\n", - "df.head()" + "#visual of the clean_df in a heatmap to look at correlations\n", + "plt.figure(figsize=(20,20))\n", + "sns.heatmap(df.corr().abs(), annot=True)\n", + "plt.show()" ] } ], From a6a8120921d49b77194c6a5cf9ec8939d4bff3e7 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Sun, 28 Apr 2024 22:20:02 +0300 Subject: [PATCH 10/53] Update student.ipynb --- student.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 1b57076a..b3e32a00 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,7 +7,7 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: Solphine Joseph, Grace Rotich, Mather Rotich, Hilary Simiyu, Clyde Ochieng.\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng.\n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", From 5d27220b4b36466d5c1fd2aab29b08806fda503b Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Mon, 29 Apr 2024 16:47:13 +0300 Subject: [PATCH 11/53] Update student.ipynb --- student.ipynb | 1655 +++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 1395 insertions(+), 260 deletions(-) diff --git a/student.ipynb b/student.ipynb index 52ce98fd..1beeaa73 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,7 +7,7 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: Solphine Joseph, Grace Rotich, Mather Rotich, Hilary Simiyu, Clyde Ochieng.\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng.\n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -59,6 +59,15 @@ "The real estate agency in Kingsway is analyzing a dataset to determine the factors affecting house prices. The dataset likely includes features such as property size, location, age, and market trends. Key steps include assessing data quality, exploring relationships between features and prices, and preprocessing data for multilinear regression analysis. Multilinear regression will be used to model how these features collectively influence house prices, with evaluation metrics used to assess predictive accuracy." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset utilized in this analysis is the King County Housing dataset, encompassing details on over 21,000 homes within King County. Each entry in the dataset includes information on various features such as bedroom/bathroom/floor counts, living space and lot square footage, zip code, building grade, condition, and more.\n", + "\n", + "The King County Housing Dataset comprises multiple features contributing to the final sale price of homes in King County. Descriptions of these features are provided below." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -92,45 +101,1110 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Explotory Data Analyis\n", + "### Data Preparation\n", + "\n", + "Importing data." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "#importing libraries \n", + "import pandas as pd\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "import statsmodels.api as sm\n", + "from statsmodels.formula.api import ols\n", + "from scipy import stats\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21597, 21)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21,597 houses with 21 features." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idpricebedroomsbathroomssqft_livingsqft_lotfloorssqft_aboveyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
count21597.0000021597.0000021597.0000021597.0000021597.0000021597.0000021597.0000021597.0000021597.0000017755.0000021597.0000021597.0000021597.0000021597.0000021597.00000
mean4580474287.77099540296.573513.373202.115832080.3218515099.408761.494101788.596841970.9996883.6367898077.9518547.56009-122.213981986.6203212758.28351
std2876735715.74778367368.140100.926300.76898918.1061341412.636880.53968827.7597629.37523399.9464153.513070.138550.14072685.2304727274.44195
min1000102.0000078000.000001.000000.50000370.00000520.000001.00000370.000001900.000000.0000098001.0000047.15590-122.51900399.00000651.00000
25%2123049175.00000322000.000003.000001.750001430.000005040.000001.000001190.000001951.000000.0000098033.0000047.47110-122.328001490.000005100.00000
50%3904930410.00000450000.000003.000002.250001910.000007618.000001.500001560.000001975.000000.0000098065.0000047.57180-122.231001840.000007620.00000
75%7308900490.00000645000.000004.000002.500002550.0000010685.000002.000002210.000001997.000000.0000098118.0000047.67800-122.125002360.0000010083.00000
max9900000190.000007700000.0000033.000008.0000013540.000001651359.000003.500009410.000002015.000002015.0000098199.0000047.77760-121.315006210.00000871200.00000
\n", + "
" + ], + "text/plain": [ + " id price bedrooms bathrooms sqft_living \\\n", + "count 21597.00000 21597.00000 21597.00000 21597.00000 21597.00000 \n", + "mean 4580474287.77099 540296.57351 3.37320 2.11583 2080.32185 \n", + "std 2876735715.74778 367368.14010 0.92630 0.76898 918.10613 \n", + "min 1000102.00000 78000.00000 1.00000 0.50000 370.00000 \n", + "25% 2123049175.00000 322000.00000 3.00000 1.75000 1430.00000 \n", + "50% 3904930410.00000 450000.00000 3.00000 2.25000 1910.00000 \n", + "75% 7308900490.00000 645000.00000 4.00000 2.50000 2550.00000 \n", + "max 9900000190.00000 7700000.00000 33.00000 8.00000 13540.00000 \n", + "\n", + " sqft_lot floors sqft_above yr_built yr_renovated \\\n", + "count 21597.00000 21597.00000 21597.00000 21597.00000 17755.00000 \n", + "mean 15099.40876 1.49410 1788.59684 1970.99968 83.63678 \n", + "std 41412.63688 0.53968 827.75976 29.37523 399.94641 \n", + "min 520.00000 1.00000 370.00000 1900.00000 0.00000 \n", + "25% 5040.00000 1.00000 1190.00000 1951.00000 0.00000 \n", + "50% 7618.00000 1.50000 1560.00000 1975.00000 0.00000 \n", + "75% 10685.00000 2.00000 2210.00000 1997.00000 0.00000 \n", + "max 1651359.00000 3.50000 9410.00000 2015.00000 2015.00000 \n", + "\n", + " zipcode lat long sqft_living15 sqft_lot15 \n", + "count 21597.00000 21597.00000 21597.00000 21597.00000 21597.00000 \n", + "mean 98077.95185 47.56009 -122.21398 1986.62032 12758.28351 \n", + "std 53.51307 0.13855 0.14072 685.23047 27274.44195 \n", + "min 98001.00000 47.15590 -122.51900 399.00000 651.00000 \n", + "25% 98033.00000 47.47110 -122.32800 1490.00000 5100.00000 \n", + "50% 98065.00000 47.57180 -122.23100 1840.00000 7620.00000 \n", + "75% 98118.00000 47.67800 -122.12500 2360.00000 10083.00000 \n", + "max 98199.00000 47.77760 -121.31500 6210.00000 871200.00000 " + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 21597.00000\n", + "mean 540296.57351\n", + "std 367368.14010\n", + "min 78000.00000\n", + "25% 322000.00000\n", + "50% 450000.00000\n", + "75% 645000.00000\n", + "max 7700000.00000\n", + "Name: price, dtype: float64" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# descriptive statistics for our target price.\n", + "df['price'].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The average price of homes in the data set is 540,297 dollars. \n", + "The prices ranges from 78,000 to 8,000,000 dollars and\n", + "the median house price is 450,000 dollars" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 21597.00000\n", + "mean 2080.32185\n", + "std 918.10613\n", + "min 370.00000\n", + "25% 1430.00000\n", + "50% 1910.00000\n", + "75% 2550.00000\n", + "max 13540.00000\n", + "Name: sqft_living, dtype: float64" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# descriptive statistics for square footage\n", + "df['sqft_living'].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mean square-feet of living space is 2,080 sq-ft and the range of living space ranges from 370 sq-ft to 13,540 sq-ft. The median sq footage is 1,910." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bedrooms\n", + "3 9824\n", + "4 6882\n", + "2 2760\n", + "5 1601\n", + "6 272\n", + "1 196\n", + "7 38\n", + "8 13\n", + "9 6\n", + "10 3\n", + "11 1\n", + "33 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['bedrooms'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The bedroom counts range from 1 bedroom to 33" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bathrooms\n", + "2.50000 5377\n", + "1.00000 3851\n", + "1.75000 3048\n", + "2.25000 2047\n", + "2.00000 1930\n", + "1.50000 1445\n", + "2.75000 1185\n", + "3.00000 753\n", + "3.50000 731\n", + "3.25000 589\n", + "3.75000 155\n", + "4.00000 136\n", + "4.50000 100\n", + "4.25000 79\n", + "0.75000 71\n", + "4.75000 23\n", + "5.00000 21\n", + "5.25000 13\n", + "5.50000 10\n", + "1.25000 9\n", + "6.00000 6\n", + "0.50000 4\n", + "5.75000 4\n", + "6.75000 2\n", + "8.00000 2\n", + "6.25000 2\n", + "6.50000 2\n", + "7.50000 1\n", + "7.75000 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['bathrooms'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "floors\n", + "1.00000 10673\n", + "2.00000 8235\n", + "1.50000 1910\n", + "3.00000 611\n", + "2.50000 161\n", + "3.50000 7\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['floors'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sqft_lot\n", + "5000 358\n", + "6000 290\n", + "4000 251\n", + "7200 220\n", + "4800 119\n", + " ... \n", + "22605 1\n", + "25248 1\n", + "9934 1\n", + "9142 1\n", + "1076 1\n", + "Name: count, Length: 9776, dtype: int64" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['sqft_lot'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 21597.00000\n", + "mean 15099.40876\n", + "std 41412.63688\n", + "min 520.00000\n", + "25% 5040.00000\n", + "50% 7618.00000\n", + "75% 10685.00000\n", + "max 1651359.00000\n", + "Name: sqft_lot, dtype: float64" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['sqft_lot'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "yr_built\n", + "2014 559\n", + "2006 453\n", + "2005 450\n", + "2004 433\n", + "2003 420\n", + " ... \n", + "1933 30\n", + "1901 29\n", + "1902 27\n", + "1935 24\n", + "1934 21\n", + "Name: count, Length: 116, dtype: int64" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['yr_built'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The year built ranges from 1934 to 2014." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "condition\n", + "Average 14020\n", + "Good 5677\n", + "Very Good 1701\n", + "Fair 170\n", + "Poor 29\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['condition'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "waterfront\n", + "NO 19075\n", + "YES 146\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['waterfront'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# examining the relationship between sqft_living and price\n", + "sns.jointplot(x='sqft_living', y='price', data=df, kind='reg')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(x='bathrooms', y='price', data=df, kind='reg')\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n", + "Data Preparation Fundamentals - Applying appropriate preprocessing and feature engineering steps to tabular data in preparation for statistical modeling\n", + "\n", + "Data Cleaning Steps\n", + "Handling Missing Values: Identify and address and missing values using techniques such as dropping or replacing data.\n", + "\n", + "Handling Non-Numeric Data: A Linear regression model needs all of the features to be numeric, not categorical. Identify the data type 'object' and address them using techniques such as ordinal or one-hot encoding.\n", + "\n", + "This notebook contains a breakdown of the step-by-step processes that we used to compile, scrub, and transform our data. It includes variations of narrowing our scope and explorations into the impacts that our different transformations have on the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocessing with Scikit-learn\n", + "Let explore and clean our data set to prep for our Linear Regression Model.\n", + "Preprocessing Steps.\n", + "\n", + "1. Handle Missing Values\n", + "2. Convert Categorical Features into Numbers\n", + "3. Find and Remove Outliers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Handling Missing Values\n", + "Below, let's check to see if there are any NaNs in our data" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id 0\n", + "date 0\n", + "price 0\n", + "bedrooms 0\n", + "bathrooms 0\n", + "sqft_living 0\n", + "sqft_lot 0\n", + "floors 0\n", + "waterfront 2376\n", + "view 63\n", + "condition 0\n", + "grade 0\n", + "sqft_above 0\n", + "sqft_basement 0\n", + "yr_built 0\n", + "yr_renovated 3842\n", + "zipcode 0\n", + "lat 0\n", + "long 0\n", + "sqft_living15 0\n", + "sqft_lot15 0\n", + "dtype: int64" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#locate missing values\n", + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id 0.0\n", + "date 0.0\n", + "price 0.0\n", + "bedrooms 0.0\n", + "bathrooms 0.0\n", + "sqft_living 0.0\n", + "sqft_lot 0.0\n", + "floors 0.0\n", + "waterfront 11.00152798999861\n", + "view 0.29170718155299347\n", + "condition 0.0\n", + "grade 0.0\n", + "sqft_above 0.0\n", + "sqft_basement 0.0\n", + "yr_built 0.0\n", + "yr_renovated 17.78950780200954\n", + "zipcode 0.0\n", + "lat 0.0\n", + "long 0.0\n", + "sqft_living15 0.0\n", + "sqft_lot15 0.0\n" + ] + } + ], + "source": [ + "#dealing with missing values\n", + "for column in df.columns:\n", + " percentage_of_nan = (sum(df[column].isnull())/len(df[column])) * 100 \n", + " print(column, percentage_of_nan)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The feature 'waterfront' is the only feature with missing values and about 11% of the values have NaNs. Lets investigate this feature to handle it's missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "waterfront\n", + "NO 19075\n", + "YES 146\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['waterfront'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the 'waterfront' feature only has two values, yes or no.\n", + "Thus NaN values can be considered no because they do not exist in their homes." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "df['waterfront'].fillna('NO', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "waterfront\n", + "NO 21451\n", + "YES 146\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['waterfront'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id 0\n", + "date 0\n", + "price 0\n", + "bedrooms 0\n", + "bathrooms 0\n", + "sqft_living 0\n", + "sqft_lot 0\n", + "floors 0\n", + "waterfront 0\n", + "view 63\n", + "condition 0\n", + "grade 0\n", + "sqft_above 0\n", + "sqft_basement 0\n", + "yr_built 0\n", + "yr_renovated 3842\n", + "zipcode 0\n", + "lat 0\n", + "long 0\n", + "sqft_living15 0\n", + "sqft_lot15 0\n", + "dtype: int64" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#recheck for missing values\n", + "df.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Convert Categorical Features into Numbers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our model would crash because some of the columns are non-numeric. Features with a numeric data type will work with our model, but these features need to be converted:\n", + "* waterfront (object)\n", + "* condition (object)\n", + "* grade (object)\n", + "\n", + "Let's inspect the value counts of the specified features:" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "waterfront\n", + "NO 21451\n", + "YES 146\n", + "Name: count, dtype: int64\n", + "\n", + "condition\n", + "Average 14020\n", + "Good 5677\n", + "Very Good 1701\n", + "Fair 170\n", + "Poor 29\n", + "Name: count, dtype: int64\n", + "\n", + "grade\n", + "7 Average 8974\n", + "8 Good 6065\n", + "9 Better 2615\n", + "6 Low Average 2038\n", + "10 Very Good 1134\n", + "11 Excellent 399\n", + "5 Fair 242\n", + "12 Luxury 89\n", + "4 Low 27\n", + "13 Mansion 13\n", + "3 Poor 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "print(df['waterfront'].value_counts())\n", + "print()\n", + "print(df['condition'].value_counts())\n", + "print()\n", + "print(df['grade'].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Split function to seperate the numeric value of 'grade'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Grade feature is an object data type however the numeric grade is listed in the front. We will use a simple string split function to isolate the numeric part of the feature.\n", "\n", - "Importing data." + "Waterfront has only 2 categories and can be converted into binary in place, whereas Condition has more than 2 categories and will need to be expanded into multiple columns." ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ - "#importing libraries \n", - "import pandas as pd\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import seaborn as sns\n", - "from sklearn import metrics\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.linear_model import LinearRegression\n", - "import statsmodels.api as sm\n", - "from statsmodels.formula.api import ols\n", - "from scipy import stats\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn import tree\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.tree import DecisionTreeRegressor\n", - "from sklearn.dummy import DummyRegressor" + "df = df.assign(grade=df.grade.str.split(' ')).explode('grade')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False 46366\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.duplicated().value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(46366, 21)" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 126, "metadata": {}, + "outputs": [], "source": [ - "##### Removing irrelvant columns" + "df = df.drop_duplicates(subset='id')" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -188,10 +1262,10 @@ " 1180\n", " 5650\n", " 1.00000\n", - " NaN\n", + " NO\n", " NONE\n", " ...\n", - " 7 Average\n", + " 7\n", " 1180\n", " 0.0\n", " 1955\n", @@ -215,7 +1289,7 @@ " NO\n", " NONE\n", " ...\n", - " 7 Average\n", + " 7\n", " 2170\n", " 400.0\n", " 1951\n", @@ -227,30 +1301,6 @@ " 7639\n", " \n", " \n", - " 2\n", - " 5631500400\n", - " 2/25/2015\n", - " 180000.00000\n", - " 2\n", - " 1.00000\n", - " 770\n", - " 10000\n", - " 1.00000\n", - " NO\n", - " NONE\n", - " ...\n", - " 6 Low Average\n", - " 770\n", - " 0.0\n", - " 1933\n", - " NaN\n", - " 98028\n", - " 47.73790\n", - " -122.23300\n", - " 2720\n", - " 8062\n", - " \n", - " \n", " 3\n", " 2487200875\n", " 12/9/2014\n", @@ -263,7 +1313,7 @@ " NO\n", " NONE\n", " ...\n", - " 7 Average\n", + " 7\n", " 1050\n", " 910.0\n", " 1965\n", @@ -287,7 +1337,7 @@ " NO\n", " NONE\n", " ...\n", - " 8 Good\n", + " 8\n", " 1680\n", " 0.0\n", " 1987\n", @@ -298,57 +1348,247 @@ " 1800\n", " 7503\n", " \n", + " \n", + " 5\n", + " 7237550310\n", + " 5/12/2014\n", + " 1230000.00000\n", + " 4\n", + " 4.50000\n", + " 5420\n", + " 101930\n", + " 1.00000\n", + " NO\n", + " NONE\n", + " ...\n", + " 11\n", + " 3890\n", + " 1530.0\n", + " 2001\n", + " 0.00000\n", + " 98053\n", + " 47.65610\n", + " -122.00500\n", + " 4760\n", + " 101930\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 21592\n", + " 263000018\n", + " 5/21/2014\n", + " 360000.00000\n", + " 3\n", + " 2.50000\n", + " 1530\n", + " 1131\n", + " 3.00000\n", + " NO\n", + " NONE\n", + " ...\n", + " 8\n", + " 1530\n", + " 0.0\n", + " 2009\n", + " 0.00000\n", + " 98103\n", + " 47.69930\n", + " -122.34600\n", + " 1530\n", + " 1509\n", + " \n", + " \n", + " 21593\n", + " 6600060120\n", + " 2/23/2015\n", + " 400000.00000\n", + " 4\n", + " 2.50000\n", + " 2310\n", + " 5813\n", + " 2.00000\n", + " NO\n", + " NONE\n", + " ...\n", + " 8\n", + " 2310\n", + " 0.0\n", + " 2014\n", + " 0.00000\n", + " 98146\n", + " 47.51070\n", + " -122.36200\n", + " 1830\n", + " 7200\n", + " \n", + " \n", + " 21594\n", + " 1523300141\n", + " 6/23/2014\n", + " 402101.00000\n", + " 2\n", + " 0.75000\n", + " 1020\n", + " 1350\n", + " 2.00000\n", + " NO\n", + " NONE\n", + " ...\n", + " 7\n", + " 1020\n", + " 0.0\n", + " 2009\n", + " 0.00000\n", + " 98144\n", + " 47.59440\n", + " -122.29900\n", + " 1020\n", + " 2007\n", + " \n", + " \n", + " 21595\n", + " 291310100\n", + " 1/16/2015\n", + " 400000.00000\n", + " 3\n", + " 2.50000\n", + " 1600\n", + " 2388\n", + " 2.00000\n", + " NO\n", + " NONE\n", + " ...\n", + " 8\n", + " 1600\n", + " 0.0\n", + " 2004\n", + " 0.00000\n", + " 98027\n", + " 47.53450\n", + " -122.06900\n", + " 1410\n", + " 1287\n", + " \n", + " \n", + " 21596\n", + " 1523300157\n", + " 10/15/2014\n", + " 325000.00000\n", + " 2\n", + " 0.75000\n", + " 1020\n", + " 1076\n", + " 2.00000\n", + " NO\n", + " NONE\n", + " ...\n", + " 7\n", + " 1020\n", + " 0.0\n", + " 2008\n", + " 0.00000\n", + " 98144\n", + " 47.59410\n", + " -122.29900\n", + " 1020\n", + " 1357\n", + " \n", " \n", "\n", - "

5 rows × 21 columns

\n", + "

17565 rows × 21 columns

\n", "" ], "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.00000 3 1.00000 1180 \n", - "1 6414100192 12/9/2014 538000.00000 3 2.25000 2570 \n", - "2 5631500400 2/25/2015 180000.00000 2 1.00000 770 \n", - "3 2487200875 12/9/2014 604000.00000 4 3.00000 1960 \n", - "4 1954400510 2/18/2015 510000.00000 3 2.00000 1680 \n", + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.00000 3 1.00000 1180 \n", + "1 6414100192 12/9/2014 538000.00000 3 2.25000 2570 \n", + "3 2487200875 12/9/2014 604000.00000 4 3.00000 1960 \n", + "4 1954400510 2/18/2015 510000.00000 3 2.00000 1680 \n", + "5 7237550310 5/12/2014 1230000.00000 4 4.50000 5420 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.00000 3 2.50000 1530 \n", + "21593 6600060120 2/23/2015 400000.00000 4 2.50000 2310 \n", + "21594 1523300141 6/23/2014 402101.00000 2 0.75000 1020 \n", + "21595 291310100 1/16/2015 400000.00000 3 2.50000 1600 \n", + "21596 1523300157 10/15/2014 325000.00000 2 0.75000 1020 \n", "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.00000 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.00000 NO NONE ... 7 Average 2170 \n", - "2 10000 1.00000 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.00000 NO NONE ... 7 Average 1050 \n", - "4 8080 1.00000 NO NONE ... 8 Good 1680 \n", + " sqft_lot floors waterfront view ... grade sqft_above sqft_basement \\\n", + "0 5650 1.00000 NO NONE ... 7 1180 0.0 \n", + "1 7242 2.00000 NO NONE ... 7 2170 400.0 \n", + "3 5000 1.00000 NO NONE ... 7 1050 910.0 \n", + "4 8080 1.00000 NO NONE ... 8 1680 0.0 \n", + "5 101930 1.00000 NO NONE ... 11 3890 1530.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "21592 1131 3.00000 NO NONE ... 8 1530 0.0 \n", + "21593 5813 2.00000 NO NONE ... 8 2310 0.0 \n", + "21594 1350 2.00000 NO NONE ... 7 1020 0.0 \n", + "21595 2388 2.00000 NO NONE ... 8 1600 0.0 \n", + "21596 1076 2.00000 NO NONE ... 7 1020 0.0 \n", "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.00000 98178 47.51120 -122.25700 \n", - "1 400.0 1951 1991.00000 98125 47.72100 -122.31900 \n", - "2 0.0 1933 NaN 98028 47.73790 -122.23300 \n", - "3 910.0 1965 0.00000 98136 47.52080 -122.39300 \n", - "4 0.0 1987 0.00000 98074 47.61680 -122.04500 \n", + " yr_built yr_renovated zipcode lat long sqft_living15 \\\n", + "0 1955 0.00000 98178 47.51120 -122.25700 1340 \n", + "1 1951 1991.00000 98125 47.72100 -122.31900 1690 \n", + "3 1965 0.00000 98136 47.52080 -122.39300 1360 \n", + "4 1987 0.00000 98074 47.61680 -122.04500 1800 \n", + "5 2001 0.00000 98053 47.65610 -122.00500 4760 \n", + "... ... ... ... ... ... ... \n", + "21592 2009 0.00000 98103 47.69930 -122.34600 1530 \n", + "21593 2014 0.00000 98146 47.51070 -122.36200 1830 \n", + "21594 2009 0.00000 98144 47.59440 -122.29900 1020 \n", + "21595 2004 0.00000 98027 47.53450 -122.06900 1410 \n", + "21596 2008 0.00000 98144 47.59410 -122.29900 1020 \n", "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", + " sqft_lot15 \n", + "0 5650 \n", + "1 7639 \n", + "3 5000 \n", + "4 7503 \n", + "5 101930 \n", + "... ... \n", + "21592 1509 \n", + "21593 7200 \n", + "21594 2007 \n", + "21595 1287 \n", + "21596 1357 \n", "\n", - "[5 rows x 21 columns]" + "[17565 rows x 21 columns]" ] }, - "execution_count": 51, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#load and preiview data\n", - "df = pd.read_csv('data/kc_house_data.csv')\n", - "df.head()\n" + "df.dropna()" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -356,33 +1596,33 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", + "Index: 21420 entries, 0 to 21596\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", + " 0 id 21420 non-null int64 \n", + " 1 date 21420 non-null object \n", + " 2 price 21420 non-null float64\n", + " 3 bedrooms 21420 non-null int64 \n", + " 4 bathrooms 21420 non-null float64\n", + " 5 sqft_living 21420 non-null int64 \n", + " 6 sqft_lot 21420 non-null int64 \n", + " 7 floors 21420 non-null float64\n", + " 8 waterfront 21420 non-null object \n", + " 9 view 21357 non-null object \n", + " 10 condition 21420 non-null object \n", + " 11 grade 21420 non-null object \n", + " 12 sqft_above 21420 non-null int64 \n", + " 13 sqft_basement 21420 non-null object \n", + " 14 yr_built 21420 non-null int64 \n", + " 15 yr_renovated 17616 non-null float64\n", + " 16 zipcode 21420 non-null int64 \n", + " 17 lat 21420 non-null float64\n", + " 18 long 21420 non-null float64\n", + " 19 sqft_living15 21420 non-null int64 \n", + " 20 sqft_lot15 21420 non-null int64 \n", "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" + "memory usage: 3.6+ MB\n" ] } ], @@ -390,181 +1630,65 @@ "df.info()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that waterfrront is missing about 11% of its values, year renovated is missing about 17% of its values and view is missing a few values as well. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Cleaning\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "#drop irrelevant columns\n", - "df.drop(['id', 'date', 'zipcode', 'lat', 'long', 'yr_renovated', 'view'],\n", - " axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "#fill in missing sqft_basement values\n", - "df.loc[df.sqft_basement == '?', 'sqft_basement'] = (\n", - " df[df.sqft_basement == '?'].sqft_living - \n", - " df[df.sqft_basement == '?'].sqft_above\n", - ")\n", - "\n", - "#convert into numeric\n", - "df['sqft_basement'] = df.sqft_basement.astype('float64')\n", - "\n", - "#sqft_basement is a zero inflated variable, so I convert it into \n", - "#a categorical variable\n", - "df['is_basement'] = df.sqft_basement.map(lambda x: 0 if x == 0 else 1)\n", - "df.drop('sqft_basement', axis=1, inplace=True)" - ] - }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 129, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "grade\n", + "7 8889\n", + "8 6041\n", + "9 2606\n", + "6 1995\n", + "10 1130\n", + "11 396\n", + "5 234\n", + "12 88\n", + "4 27\n", + "13 13\n", + "3 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#convert condition and grade into numeric values\n", - "df['condition'] = df.condition.map(lambda x: 0 if x=='Poor' \n", - " else (1 if x=='Fair'\n", - " else (2 if x=='Average'\n", - " else (3 if x=='Good' else 4))))\n", - "\n", - "df['grade'] = df.grade.map(lambda x: int(x[0:2]) - 3)" + "df['grade'].value_counts()" ] }, { - "cell_type": "code", - "execution_count": 56, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#convert waterfront strings to 0 and 1\n", - "df['waterfront'] = df.waterfront.map(lambda x: 0 if x==\"NO\" \n", - " else (1 if x==\"YES\" else None))\n", - "\n", - "#create new column indicating if waterfront value is missing\n", - "waterfront = df[[\"waterfront\"]]\n", - "missing_indicator = MissingIndicator()\n", - "missing_indicator.fit(waterfront)\n", - "waterfront_missing = missing_indicator.transform(waterfront)\n", - "\n", - "#add waterfront missing to dataframe and convert to binary\n", - "df['waterfront_missing'] = waterfront_missing\n", - "\n", - "df['waterfront_missing'] = df.waterfront_missing.map(lambda x: 0 if x==False\n", - " else 1)\n" + "The most common buiding grade is a 7" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 130, "metadata": {}, "outputs": [], "source": [ - "#fill in missing waterfront values with median\n", - "imputer = SimpleImputer(strategy=\"median\")\n", - "\n", - "imputer.fit(waterfront)\n", - "waterfront_imputed = imputer.transform(waterfront)\n", - "\n", - "df['waterfront'] = waterfront_imputed" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 19479 entries, 0 to 21596\n", - "Data columns (total 12 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 price 19479 non-null float64\n", - " 1 bedrooms 19479 non-null float64\n", - " 2 bathrooms 19479 non-null float64\n", - " 3 sqft_living 19479 non-null float64\n", - " 4 sqft_lot 19479 non-null float64\n", - " 5 floors 19479 non-null float64\n", - " 6 sqft_above 19479 non-null float64\n", - " 7 sqft_living15 19479 non-null float64\n", - " 8 sqft_lot15 19479 non-null float64\n", - " 9 grade_num 19479 non-null float64\n", - " 10 bed_bath_ratio 19479 non-null float64\n", - " 11 mean_price 19479 non-null float64\n", - "dtypes: float64(12)\n", - "memory usage: 1.9 MB\n" - ] - } - ], - "source": [ - "#removing outliers \n", - "\n", - "#make a copy of the clean dataframe \n", - "no_out = main_df.copy()\n", - "\n", - "#drop columns that we cannot use \n", - "no_out = no_out.drop(columns= ['id', 'zip_city', 'Waterfront'], axis=1)\n", - "\n", - "#change data type so that we can math \n", - "no_out = no_out.astype('float')\n", - "\n", - "#pull out the columns \n", - "columns = no_out.columns\n", - "\n", - "#for each column in the dataframe, get the mean and standard deviation \n", - "#then get the z-score for within 3 standard devaitions\n", - "for col in columns:\n", - " \n", - " mean = no_out[col].mean()\n", - " sd = no_out[col].std()\n", - " \n", - " no_out = no_out[(no_out[col] <= mean+(3*sd))]\n", - " \n", - "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n", - "no_out.info()" + "# Change the data type from object to int.\n", + "df['grade'] = df['grade'].astype(int)" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 131, "metadata": {}, "outputs": [ { "data": { - "image/png": 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QaimpVCpuvPHG+Mc//lHkkDKdvLy8uO++++L000/P6iEAfqug9+86depkPe7f/va3Ir1mTzjhhKyvtT4//fRTnHDCCXH77bcX+/1uzX3xqquuyuqe9t1338Vxxx0XDz/8cNb3xDVmz54dF110UfTr12+jfpASAID/R3gPAAAbkdtuuy0uv/zy9c7yLapRo0bFySefHD/99FOJjpukkSNHxiWXXJJ2hmlxzJo1K84999ys9hGeNm1anHzyyfHZZ5+VaE0jRoyI8847L9EAf9myZTF69Oi0fV26dIkqVaqUcUWla9WqVXHppZcW6WueKdiaNm1anHbaaTF+/PiSKm8dDz30UFazt+fPnx9nnnlmjB07ttg1LF26NM4777yYOHFisceK+CVQvvbaa+POO+8s1gza35s2bVqceuqp8cUXX5TYmIXVvXv3tO0jR46M2bNnF3v8yZMnp73/VKhQIY4++uiM591xxx3x0EMPFfv6v/fZZ5/Fn/70pxL9/m2Kvvzyy4x99erVy2rMV155JYYOHVro45s3bx577LFHVtdanylTpsTJJ58cX331VYmOO2TIkLj88suLdM7XX38dJ598cpFX+FifBx98sEgPgwEAUH4VvJEeAABQbtxzzz0xYMCAAo9p27ZtHHzwwdG+ffto0KBBVK9ePebMmROTJ0+ON954I15//fVYuHBh2nO///77OPPMM+OJJ56IWrVqlcanUGYWLVoUV111VcZAaMcdd4xDDz00dtppp2jevHlsttlmUalSpVi0aFFMnTo1xowZE0OHDs34x/mZM2fGLbfcEjfffHOha1qwYEGceeaZMWXKlIzHNGjQIA455JDYZ599omnTplGvXr1YuHBhzJw5M0aNGhUjRoyIH374Ie25o0aNir/85S9x1113RU5OTqHrKikffvhhxhnjHTt2LONqSt/DDz9cpGXgN9988zjssMPWaV+9enX06dOnwAdn2rVrFwceeGBsv/326/y8zp07N77++usYPXr0evdcvvHGG2PvvfeOqlWrFqrmVCoVvXv3jgkTJmQ8pkmTJnHUUUfF3nvvHVtuuWVUrVo1fvrppxg3blwMGzYsRo8e/ZvX4fz58+Pll18u1PXX5+abb46nnnoqY3+FChViv/32iy5dusTOO++8NsScOXNmjB8/Pl588cUYNWpUrFq1ap1zFy1aFGeffXY888wzZbo3+9577x0NGjRYJ6hfvXp1vPDCC9GzZ89ijZ9p1v0+++wTDRo0SNv3xRdfxD333JO2Lzc3Nzp37hz7779//OEPf4jGjRtHtWrVIuKXr+HEiRPjww8/jOeffz7jdgTvv/9+PPfccxkfXGD9ClrWfn1bIaSzZMmSIq/Wcfzxx5fKe8/ixYvj7LPPjmnTpmU8pm7dunHQQQdFly5domnTplG/fv3Iy8uLKVOmxMiRI+PJJ5/M+PDL8OHDo127dnHKKaest5YpU6bEGWecUeDWGk2bNo1DDz00OnfuHI0aNYratWvHvHnz4qeffop33nknXnrppZgxY0bGWmrXrh1XX331emsBAKD8Et4DAMBG4IMPPoj//Oc/Gfu33nrruPbaa6NTp07r9NWtWze22267OPDAA+Oqq66K2267LZ544om0s7smTZoUl19++XofEtjQ3XfffWn/OF6tWrW44YYbomvXrmnPq1WrVjRp0iR23333OPfcc+PRRx+Nfv36pZ29P3z48OjVq1c0a9asUDVdcsklGYP3SpUqxQUXXBBnnnnm2uBrjdq1a0fz5s2jY8eO0bt373j66afj5ptvjkWLFq0zzhtvvBEDBw6Mc889t1A1laSCZn526NChDCspG78Py2rXrh1nnnlmHHTQQdG0adNYuXJljB8/Pt5666145pln4vDDD0+7+sDjjz+ecYZ38+bN46abbsr49Vvz89q6des44YQT4v/+7//i1ltvjSeffDLt8TNmzIh33303DjrooEJ9joMHD46PPvoobV+lSpXivPPOi/PPP3+dfdrr1asXO+20Uxx//PExcuTIuOaaawp8aCUbL7zwQjz44IMZ+zt37hx//etf025TsMUWW0Tr1q2je/fuMWHChPjrX/+a9nuwYMGCuOiii+LZZ5+NypUrl2j9mVSoUCG6desWAwcOXKdv2LBhxQ7vhw0blra9oCXzb7rpprTLeTdr1ixuv/322GmnndKeV6dOnWjevHnsv//+0bt37+jXr188/fTTaY8dMGBAHHPMMZGbawHJopo2bVqMGDEibV+lSpWyenjq22+/Xadt7733jtNOOy3atm0b1apVi9mzZ8cnn3wSzz//fHz44YcF/gwVx3XXXVfge2fPnj3jggsuiM0222yd/oYNG0aHDh3i3HPPjX//+98xePDgtOP885//jH322afA9/OVK1fGRRddlDG4r1mzZvTp0ydOPPHEqFjxt3+OrV27dmy11VbRqVOnuOSSS+L++++P/v37p33g7eGHH462bdtm/D0FAIDyz796AACgnFuxYkVcffXVGZdS3WeffWLIkCFpg/vfq1WrVlx33XXRv3//jGHUm2++Gc8991yxak5SKpXKWP9tt91W6D+I5+TkxKmnnhr/+te/0vavXr260EsKP/PMM/Huu++m7atdu3YMGjQoLrroonWC+3SOP/74GDJkSDRu3Dht/5133lniy/kWRqal0CtWrBgtW7Ys22LK2DbbbBPDhw+P8847L7beeuuoXLly1KhRI9q3bx9/+ctf4rXXXovzzz9/nfNWrlyZcUZz48aN49FHHy3Sgw+1a9eO66+/vsBZm6+++mqhxpo3b17cfvvtafsqVaoUd955Z/Tu3Xud4P73OnXqFM8991zGgDcbc+fOjeuvvz5j/9lnnx333Xdf2uD+9/7whz/EE088kTF4HD9+fNx7771Z15qNTLV89dVXMWnSpKzH/eSTT2Ly5MnrtNepUye6dOmS9pzvvvsuPv3003XaN99883j44YcL/X2tUaNG3HDDDXHqqaem7Z88eXLGbTfIbPXq1fH3v/894/Ywu+66a1SvXr3Y17nqqqvivvvui3333Tc233zzqFy58tpVN+6///546aWXon79+sW+zu+NHj0644MJ1atXj3vvvTcuvfTStMH9r1WpUiX++te/xhVXXJG2f9myZXHfffcVOMbdd9+d9qGGiF9WIHn88cfj1FNPXSe4/72KFSvGeeedF48//njGVY5uuOGGmDt3boHjAABQfgnvAQAggwMOOCBatWpVqv8dcMABxa5z8ODBGZeL3WeffeK///1voZfB/vXnfvfdd0eFChXS9t96662xbNmyIte6IZg0aVLMmjVrnfbddtst9ttvvyKPd+ihh2bcC/rNN99c7/nLly+P2267LW1f5cqVY+DAgUWemd6sWbMYNGhQ2rBk5cqVBa7SUFoyhfdNmjRZb5hRnm2xxRbx4IMPxhZbbJHxmM022yzt9+qtt95K+7MaEfGPf/yjwDELctppp2X8mfrkk08KNcbgwYNj6dKlGWsrymupVq1a8eCDD8bWW29d6HMK8t///jfmz5+ftq9nz55x2WWXFWn57goVKsSNN96YdluDiF9W8vj555+zKTUrW2+9dbRr1y5tX6aZ84WRacn8I488MuPDXCNHjkzbfvrpp0ejRo2KXMMVV1wRLVq0SNtXmPsp/8/KlSvj2muvjbfffjvjMWeeeWaxr9OrV6/1rvhQ2BVoiurf//532vbc3NwYMGBA7LnnnkUa78wzz8z4Oh86dGjGwHzmzJlx//33p+2rXbt2PPTQQ/GHP/yhSLXsvPPOcd9996V9uGLevHlpV98AAGDjILwHAIByLC8vLx566KG0fXXq1Il+/fqtd+ZrJp07d46zzjorbd/s2bPjmWeeyWrcpGVaXneXXXbJeswzzjgjbfs333yz3occnnrqqZgzZ07avquuuirrulq0aBF///vf0/a9+uqrxZqhm41MD5hkE/CVJ+eff340bNgwq3NfeumltO277bZboVbSKMhxxx2Xtn3mzJm/2YM+nby8vHj88cfT9u27775x1FFHFbmezTffPPr27VvsPbF//vnnjNsCtG/fPi677LKsxs3NzY0bbrghttxyy3X6li1bFoMGDcpq3Gxl2v99+PDh6/3+pbNy5cp4+eWX0/YVtNx5pvtp69ati1xDxC8PLJ188slp+8aMGZPVmJuaVCoVI0eOjO7duxf4Pr3TTjvFvvvuW6xrNWzYMM4777xijZGtr776Kj777LO0fT169Ijdd989q3H/7//+L+3DKsuXL4///e9/ac954IEHYsWKFWn7brnllmjatGlWtbRp0yb+/Oc/p+17/PHHY8GCBVmNCwDAhk14DwAA5dibb74Zs2fPTtt35ZVXRr169Yo1fu/evTPOgnziiSeKNXZSlixZkra9ODNnd9hhh2jcuHFss802sd9++0WPHj3immuuiQEDBmRcvWCNTHs8b7vttnHiiSdmXVNExIEHHhjt27dfpz2VSsWzzz5brLGLIj8/P+Ms7fUtZ1ye1a5dO2NIvj6pVCpGjRqVtu+kk04qTlkR8UsolM6qVasyvkbWeP/99zPObL/qqquyrqlDhw7F3sd56NChafeJjvhlVndxVnmoUaNG9OrVK23fkCFD0u77XloOO+ywtNtoTJs2rdCrJ/zaW2+9lTYI3GGHHWKHHXbIeF6m13Vx7qedO3eOmjVrxk477RSHHnponHfeeXHDDTfE5ZdfnvWYG6P8/PxYvHhxzJw5M7766qsYMmRI3HjjjXHAAQdEz549Y/z48RnPrVChQlx55ZXFruH000/PuCpDacu0LU3t2rWjT58+WY/boEGDjPehDz74YJ22lStXZqylc+fOxX5A4rTTTku7Dc6yZcsybhkAAED5tvGuTQgAAJuA1157LW17nTp14vDDDy/2+JUrV46TTjop+vXrt07fxIkTY9KkSYXaN3pDkiksfvXVV+Oyyy7L+oGHbJZ0Hj9+fMaA5dxzz43c3OI/b33iiSem3ZP6pZdeKrMwbPHixRn70gWQG4u99torqlSpkvX5DzzwQEyePDkmT54cU6ZMicmTJ8eMGTNin332KXZtDRo0yNi3fPnyqFGjRsb+V155JW17u3btYquttipWXSeccEIMHz486/NfeOGFtO2dOnWKtm3bZj3uGkcccUT069dvnZ/p2bNnx+jRo2OPPfYo9jUKo0aNGnHIIYekDQ2HDRtW5K02MoWPBc26j8h8P33yySfjmGOOyWolhW233TY+/vjjIp9XnvXv3z/69+9fZte76KKLYrfddiv2OPvvv38JVJOd119/PW17165di7xV0O8deuih8dxzz0XEL6tuNG7cOLbeeuu0S98X9DDTBRdcUKw6In550OL444+P22+/fZ2+l156KU455ZRiXwMAgA2L8B4AAMqxdLPAIiKOPvroEpsNd8wxx8Stt96adjbru+++W+7C+0z7ai9ZsiTOPvvsuOeee7LeS7yo3n333bTtlSpVKrFQZJ999omcnJx1ltKePn16fPfddyW2z3hBMs3OLUkff/xxiYcYTZo0ybhMcmEUNUD9tZycnPXOei6OgsL5VatWFXju6NGj07YfccQRxaopIqJjx47RqFGjmDFjRpHPnTt3bowbNy5t30EHHVTc0iIionr16tGxY8e0D+u8//77ZRbeR/wSrKcL3V955ZW4+uqrC/0eMG/evHjnnXfWaa9UqVIceeSRBZ6b6f4xZsyYuOqqq+L6669PbGY26e23334lEirXq1evTN4/0pk6dWpMnz49bd/RRx9d7PH32GOPuO2222LrrbeOrbbaqsCHsDK9j9evXz923XXXYtcS8ct2JOnC+zFjxsSSJUs26hVsAAA2RZbNBwCAcmr69OkZ90rfa6+9Suw6tWvXjp133jlt39ixY0vsOmVlq622SrsEbUTEuHHj4pBDDonbbrstvvvuu1KvJd2M+IiItm3bRs2aNUvkGnXq1Ek7WzAiMu4XXNIKCu+WLVtWJjUkoVWrVkmXkFFBM6ILWv599uzZMWXKlLR97dq1K5G69txzz6zO/fTTTzPu914SqxWskWnGclm9nn5dR7NmzdZpnz9/ftowPpMXX3wx8vLy1mnff//9o06dOgWeW9D3asiQIXHooYfGY489Vqxl9Ck5Rx55ZNxxxx0lsqpLkve3TO+dVatWjZ122qnY41epUiUOP/zw2H777de7ekqmWjp37pzVyhPp7LDDDml/J8jLyyuXv4cBAFAwM+8BACCDN954I5o2bVqq15g6dWoccMABWZ377bffZuzLFLZnq3Xr1mn/QF1QDRuqnJycOO644+KOO+5I27906dIYMGBADBgwIFq0aBH77LNP7LPPPrHbbrsVeyne3/v666/Ttpf0bMatt9467fL8ZfX9K2hW4MYc3hd3CfmSNn/+/Pj8889j1KhRBa4okCkAj4iYMGFC2vbc3NzYdttti11jRMR2222X1XnffPNN2vYqVaqU6L0802ojBe0xXhpycnLimGOOSXsvGzZsWBx44IGFGuf5559P276+JfMjIpo3bx577LFHjBo1Km3/tGnT4u9//3v07ds3dt5559h3331j7733jtatW5dIgEzhNGnSJC655JISWR1jjZYtW5bYWEU1adKktO3bbbddVKhQoczqyMvLi4kTJ6btK8lViXJzc6Nly5Zpg/pvv/22TFf8AACg9AnvAQCgnMq0ZOyWW24ZdevWLdFr7bjjjmnbp02bVqLXKSs9evSIp556Kn766acCj/vxxx/j4YcfjocffjgqV64cHTt2jL322is6d+5c7FmHeXl5Gb+HDRs2jCVLlhRr/F/bcsst07ZPnjy5xK5RkCpVqkSlSpXSzu6dN29emdRQ1ipVqrTeWculZeHChTFlypT48ccfY9KkSTFhwoT49ttv48cffywwmC+MTLPumzVrVmIPt2T72vrhhx/Stjdq1KhEt26oX79+2vYFCxbEvHnzyvT7fuyxx0b//v0jPz//N+1vvvlmLFq0aL0rePzwww/x+eefr9PeoEGD2HvvvQtVwyWXXBInnXRSgdst5OfnxxdffBFffPFF3HnnnVG7du3o1KlTdO7cOTp37pzxHkX2KlasGLvttlscffTRccQRR0SlSpVKdPyy2l4mnUz3oWwf/MnW9OnT076vRfzyGirJ9/HGjRunDe/L6n0cAICyI7wHAIByaubMmWnbMwVLxVGvXr207UuXLi2X+63WqFEjbr311jjzzDNj+fLlhTpn5cqV8f7778f7778ft9xySzRq1Cj23Xff2H///WPPPfcscjAya9asjEHqHXfckXFlgJI0a9asUr/GGnXr1k37M/vjjz+WWQ1lqUaNGiW2ZHImU6ZMiS+//DImTJgQ33//fUyePDmmTp0a8+fPL7VrZtqLviQD60zbWqxPpnviDz/8EO3bty9OSYU2a9asMg3vGzVqFJ06dYr333//N+0rV66Ml19+OY4//vgCz8806/7oo48u9AzmXXbZJS677LK46aabCld0/LIKxEsvvRQvvfRSRERsv/320aVLlzjggANil112KfQ4m7KcnJyoXLlyVKlSJTbffPOoX79+NGnSJLbZZpvYaaedYtddd40aNWqU2vVr1apVamOvT6YH72rXrl2mdWS650REXHnllXHllVeWeg1l+T4OAEDZEN4DAEA5lWkmaWn8sb6g2ZvLly8vd+F9RMSuu+4aAwcOjD59+mS1H/OMGTPiiSeeiCeeeCI233zzOOigg6J79+6FDgkXLFhQ5GuWtNIMeX9v2223TRt0LF26NGbOnBkNGzYss1rKQklvsbDGpEmTYtiwYTF8+PBEVr7INJO0evXqJXaNbO8nm9prao3u3buvE95H/LJ0fkHhfSqVimHDhqXtK8yS+b/Ws2fPyM3NjVtuuSXjTOSCfPPNN/HNN9/EgAEDomnTptG1a9c47rjjolmzZkUeqzzq1atX9O7dO+kyimR9e8GXprL8/acgm+o9BwCA0iW8BwCAcirTjPH1LZOcjYLGXLlyZYlfr6zstttuMWzYsLjtttti6NChBS77XJAFCxbEM888E88880y0bds2Lr/88th1110LPKewM/5L04oVK8rsWn/4wx/SBowREePGjSt2eN+hQ4f49ttvszr3yiuvjCFDhhTr+r9X0vt5//TTT3HrrbfGsGHDir30fcQvIdfixYuLfF6mn9uSfIAn27HK8ud5Q6rhwAMPjFq1asXChQt/0z569OiYMWNGNGrUKO15n3zySUydOnWd9rZt22a1X/fpp58eHTp0iH79+sWHH35Y5PPXmDp1agwYMCDuvffeOOyww+Kyyy7L+DmQnJK+xxXFsmXL0raX9YOEm9r7OAAAZSO537Q3Yddcc020atUqbrvttjK75oQJE6Jv375x2GGHRbt27aJNmzZx+OGHx0033ZRx2UMAADZsmQK8kgj2fq+gmZRJzr4rCfXr148bb7wxXn/99ejdu3e0bNmyWOONGTMmTj311BgwYECBx/1+j+oklOWDFwXtY/7BBx+UWR3l0dtvvx2HHXZYPP/888V6fTdu3DiOPfbYuPPOO2PkyJFZjVEa95ffq1atWlZbDqxevboUqimaJB5mqlKlShxxxBHrtKdSqRg+fHjG8zItmX/MMcdkXcuOO+4YDz30UDzzzDNx4oknFmsZ8/z8/HjhhRfiyCOPjHfeeSfrcdj4bAiv9YgNo47y/AAlAADpmXlfxl577bV46qmnyvSa//3vf+Ouu+5aZxbRpEmTYtKkSfHMM8/ErbfeGvvuu2+Z1gUAQPFkCs0XLVpU4tcqaIZuaS0PXtYaNWoUvXr1il69esU333wTr7/+erz99tvx5ZdfFjloz8/Pj9tuuy1SqVRccMEFaY+pXLlySZRdLGURxK6x1157ZezLNCOfX5Y+v+qqq4q0KkRubm40btw4ttlmm2jVqlXstNNO0bp162jSpEmx66lWrVra9mxm8WeybNmyrH42N4TXVFK6d+8ejz/++Drtw4cPj3PPPXed9pUrV8bLL7+8TnvVqlXTPghQVK1bt47WrVvHNddcE6NHj4433ngj3nnnnfjxxx+LPNaiRYviwgsvjPvvvz923333YtdG+ZfptZ5pW4+yrqMsleX7OAAAZUN4X4befvvtuPjii8v0mv37948777wzIiLq1KkTZ555ZrRr1y5WrVoVL7/8cjz11FOxePHi+NOf/hTPPfdcVkvjAQCQjEx7u5ZkiLZGpgcCcnJyMoZ5pa00Z5ttv/32sf3220evXr1i3rx5MXLkyBg1alSMGjWqSOHTHXfcEXvssUe0a9dunb6Cvm7/+9//SiRo3ZA0bNgwtt9++/jmm2/W6Zs0aVJ88cUXscsuuyRQ2YZr0qRJcc011xQY3FeqVCl22WWXaNOmTey4446x7bbbxlZbbbXeh2qy3SIi07LUJXnfyXasTK+p3r17R69evYpT0gavdevWsd1228X48eN/0z5+/Pj45ptvYvvtt/9N+xtvvLHOMvsRvyzBX5Jbr1SsWDE6deoUnTp1ioiIKVOmxAcffLD2njpv3rxCjZOXlxd/+ctf4pVXXinzfc3Z8FSvXj1te1mH95nqqFixYnzxxRdRoUKFMq0HAICNg/C+jAwaNCj+9a9/FbjcaEn7+uuv4+67746IiCZNmsTgwYOjWbNma/s7deoUO+64Y1x77bWxfPnyuP322+OOO+4os/oAACieBg0apG2fPXt2iV/rp59+Sttev379xJa3Lqt9XuvUqROHH354HH744RHxS/j03nvvxf/+978YNWpUgQ8R5Ofnx6233hoPP/zwOn2Zvn8REdOmTdvowvuIiP322y9teB8R8eijjwrvfyWVSsXll1+ecU/lmjVrRq9eveLYY4+NWrVqFXn8bF8/jRs3Ttv+888/ZzVeOtmuHpLpNTVt2rTilFNudO/ePW666aZ12ocNG7ZOeJ9pyfzu3buXSm1rNGvWLP74xz/GH//4x0ilUvHVV1/Fe++9F2+88UaMHTu2wFnEc+bMiUGDBm30D2KwfvXr10/bPn/+/DKtI9M9Z9WqVfHTTz9tlO/jAACUPnvel7Iffvghzj///LjpppsiLy+vTJ+6veOOO2LVqlWRk5MT//nPf34T3K/xxz/+MbbbbruI+GV2T6Y/DAEAsOHJ9EfhWbNmlWiQFhExbty4tO1NmzbNaryS2O+9sDM2S1qzZs3ipJNOioEDB8b7778f1157bcZAMyJi9OjRMX369HXaa9eunXHW3tSpU0us3g1J9+7dIzc3/T9DX3jhhayW1N5YvfHGG/Hll1+m7WvatGm88MIL0bNnz6yC+4jsXz8tWrRI2z59+vRYsGBBVmP+3nfffZfVeZnuiRvr6+n3jjrqqKhUqdI67b9fHn/evHnx3nvvrXNc48aNY4899ii1+n4vJycndt555zj//PPj6aefjtdeey3OPffcjPfFiMwPHbBpyfSeO2HChBK7xpIlS9b7oGFB7/2byn0HAICSJ7wvRY8++mh07do13nzzzYiI2HbbbePvf/97mVx73rx58e6770ZExCGHHFLgDJazzjorTjjhhDjzzDNj6dKlZVIfAADF16pVq4x9mUK/bH311Vdp25s3b17geZmC2mXLlhW7plmzZhV7jOKqVatWnHLKKfH8889HmzZt0h6TSqVizJgxafsyfQ9Hjx5dUiVuUJo1axb7779/2r68vLy4/vrry7iiDdewYcPStufm5sbdd98dDRs2LNb4M2bMyNhX0OznP/zhDxlX28j0kE9Rff3111mdt+bB9N/74osvymyljiTVrVs3unTpsk77tGnTfnMPevHFF9OuCtitW7eM9+yy0KxZs7jkkkviySefjNq1a6c9ZvLkyTFnzpyyLYwNzh/+8Ie07ePHjy+xPeCvu+66aNOmTRx00EFxxhlnxLXXXhv33ntvTJw4ce0xderUyTj7fmN9HwcAoPQJ70vR2LFjIy8vLypXrhznnXdePPfcc+v94+bvrVy5Mh599NHo2bNn7LnnnrHzzjtHp06d4vTTT4+HH34440z5Dz74YO0/xrt27VrgNbp16xZ9+/aNv/zlL1G3bt0i1QcAQHKaNGkS9erVS9uXblZltmbNmhXffvtt2r62bdsWeG66WaARxd+XdunSpcVaCjs/Pz+mTJkSb7/9djz44IPx5JNPFqueWrVqxT//+c+MoWamGXg777xz2vaS/P5F/LJ3+sSJE0vkoYni6tmzZ8a+9957L5566qmyK2YDlin46dKlS8aQuigKesCnoJUx6tatmzE4GzVqVLHrioiMD7usT+vWrdO2L1++vESDtAULFsQXX3wRc+fOLbExS8qxxx6btv2ll15a+/ELL7ywTn9OTk4cc8wxWV1z5cqVMWHChHjllVfiv//9b4wcOTKrcdbYbrvt4rLLLsvYb0YzO+20U9r2pUuXxvjx40vkGl9++WXk5eXF5MmT44MPPognn3wy/v3vf6/zu0em+05Jv49//fXX8cMPPxS4VQ8AABsHe96XoipVqsTxxx8fF1xwQVb7XH3zzTdx0UUXrfMP07lz58aHH34YH374YTz44INxxx13rPNHv1/vI/nrWff5+fkxe/bsWLJkSTRs2DA222yzItcFAMCGY88994zhw4ev0z5s2LC47LLLonLlysW+xtChQ2PVqlVp+3bfffcCz820/HFxl7z//PPPs1p6/5NPPonrr78+fvjhh988CLtmH+biaNGiRWy77bZpl+3NtMJV586d4+GHH16nfdasWTFy5Mjo1KlTsWqKiFi9enWcc845awOHevXqRZMmTaJp06axzz77ZB3YZatjx46x//77x//+97+0/ddff300a9asRD738mrhwoUZg+H1PTBTWG+99VbGvkyv9zX23HPPtAHZ0KFD489//nOxZm/Pnj0764cAdthhh6hXr17abUOef/756Ny5c9Z1/doDDzwQAwYMiIhf7nGNGzeOpk2bRtOmTeOKK64okftutvbZZ59o0KBBzJ49+zftr732Wlx11VXx008/xaeffrrOeR07dizyZINHH300HnrooZgyZcpvlhfv3r17sV+/mVboiCiZlVso33beeefYfPPN027VMWLEiAJXJiqM6dOnxw8//LBOe05Ozjr34M6dO6d9P/v888/j+++/j6222qpYtURELFq0KE4++eRYunRp5OTkRIMGDda+jx9++OEFvl4AACh/zLwvRdddd13ccMMNWQX33333XZxyyikxderUqFSpUpx00klxzz33xNNPPx333HNP/PGPf4xKlSrFtGnTokePHjFp0qTfnL/mD4aVKlWKLbbYIubMmRPXXXdd7LHHHrHPPvvEYYcdFh07dowePXrExx9/XCKfLwAAZe/ggw9O2z5//vy0oX5RrVy5MuNM6KZNm8Y222xT4PmZlj5O90fxonjllVeyOq9Ro0bxzTffrLOC1ZQpU7LeZ/vXqlatmra9Zs2aadv32GOPjH3//e9/i11PxC9fq1/PFPz555/jiy++iBdffDGxbbOuueaajA925OXlxUUXXRRvv/12mdTy4osvxquvvlom1yqsgsLJzTffvNjjjxs3Lj788MOM/emWVP+1I488Mm37Tz/9tHbbuGwNGTJkvQ8PZJKTkxMHHnhg2r4XXnghfvzxx+KUFhG/rBry63vi0qVLY+LEifHWW2/F559/nmhwHxFRsWLFOOqoo9ZpnzZtWowfPz5effXVtMuKZ5qxX5DNNtssfvjhh3X2BX///ffXu1f4+lSrVi1jX40aNYo1NuVfbm5u7Lvvvmn7RowYkfU9ZI0XXngh7etku+22W+cefMABB6R9YCk/Pz/uueeeYtWxxhNPPLH2/TqVSsWsWbPis88+i+HDh0eFChVK5BoAAGw4hPelqDizDS677LJYvHhx1KxZMx577LH429/+Fl26dIlddtklunTpEtdff3089NBDUbly5Vi8eHFcffXVvzl//vz5EfHLP2rHjBkTRxxxRDzxxBO/eSp59erVMWrUqDj11FPjgQceyLpWAACSs99++2Xc+uiWW24p9t7At99+e0yZMiVtX2Fmqjdu3Dht+yeffJJxC6j1mT17dgwdOjSrcxs3bpzx4drHH388qzHXWLlyZcaHElq0aJG2vWrVqhmD0I8++ijt8tZFsWLFivj3v/+dtq9ixYpx2GGHFWv8bDVu3Dj+/Oc/Z+xfsmRJXHDBBfHAAw9ktcJCYUydOjV69eoVF198cbG3cShptWrVytiX6fVYWHl5efH3v/+9wH2h17cs88477xw77rhj2r4bb7wx64dCZs2aFQMHDszq3DWOO+64tO2rV6+OG264odj7YQ8YMCDjqgjr27KurHTv3j1t+1tvvRWvvfbaOu3Vq1ePQw45pMjX2W233dK2l8RDHJmWPs/Jycl4P2XTkumBk+nTp8egQYOyHnfp0qXx0EMPpe1Lt1LNlltuGXvvvXfa44cNG1bsCTM///xzxocAateuHXvttVexxgcAYMMjvN8AjRw5cu3+gxdeeOFvlr3/tfbt20ePHj0iIuLTTz+NL774Ym3fmj8+rVixIs4///xYsGBBnHbaafHCCy/E2LFj4+23344rr7wyqlevHqlUKm6++eZ48cUXS/kzAwCgpFWqVCnOOOOMtH3z58+PK664Iuv9Ud9///148MEH0/ZVr149Y0j2a5n2gl26dGk8/fTTRa4pPz8//vrXvxZr2eRDDz00bfvjjz+edsn7who2bFgsWrRonfaKFStG+/btM5535plnRqVKldL2XX311cXav/fGG2/MuD/0EUcckfHBj7LQs2fPOProozP2r169Om6++ebo3r17fPLJJyV23e+//z6uvfbaOPTQQ9MGmRuCatWqZVyR4dVXX816VmkqlYq//e1v691TfsWKFesd69xzz03bPm3atLjhhhuKXFt+fn5ce+21sXDhwiKf+2u77LJL7LHHHmn73nnnnbjzzjuzHvvjjz/OeE+sWbNmdOvWLeuxS9I222yTdnuF4cOHp10y/9BDD824EkZBGjdunPEef/PNN2f9gFbEL0vyp7P99tsX+HALm4499tgjtt9++7R9/fv3X2eFysK68847Y9asWeu0V6lSJeM2M5nuh6tXr44+ffrEzJkzs6olPz8/rrzyyrS/W0REnHzyyVGxoh1RAQA2NsL7DdCvn1Bf3xO0v14m7Nf7Aq75Y+bSpUtj/vz50bdv37j66qtj2223jcqVK8eWW24ZZ5xxRjz44INr/1DYr1+/Qv2RBgCADcupp54ajRo1Stv33nvvxYUXXljkEOXNN9+M888/P+PSx3/6058KFfx27NgxNttss7R9//nPf4q0VP3KlSvjiiuuiHfeeafQ56Rz8sknp11mNi8vLy644IKs/sg+YcKEuOWWW9L27bPPPgV+rZo1axYnnnhi2r6lS5fGaaedltXMvbvuuiuefPLJtH2VKlWKP/3pT0Ues6TdcMMN0bFjxwKPGTduXJx88slx4oknxogRI7J6cGPJkiUxfPjwOOuss+Kwww6LJ598cr1Lw+fm5sYJJ5xQ5GuVlA4dOqRtnzx58tr91oti0aJF0adPn3jmmWfWe2xhViI47LDDMj5o/uyzzxYpwF+9enVcfvnlxZ6tvcall16acSnpu+66K/71r38VeQb+l19+Gb179874c3POOedk3CYkCelmJY8fPz7tgx+ZZuoXxqmnnpq2ffLkydGnT5/1vs7SGT58eDz//PNp+wr7gMRpp50WrVq1SvtfQVtGUH7k5OTExRdfnLZv2bJl0aNHj5g4cWKRxnz66aczrkx5wgknZHyNd+jQIeO+87Nnz46TTz65yA8Hrl69Oq677rqMv/PUqVMnzjrrrCKNCQBA+SC83wCNGzdu7cdHHXVUxn9wtmrV6jf/UJ48efLaj3+912anTp3i+OOPT3uttm3brp0xNXPmzPjggw9K+tMBAKCUVa9ePfr27Rs5OTlp+99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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -572,10 +1696,21 @@ } ], "source": [ - "#visual of the clean_df in a heatmap to look at correlations\n", - "plt.figure(figsize=(20,20))\n", - "sns.heatmap(df.corr().abs(), annot=True)\n", - "plt.show()" + "#grade\n", + "plt.figure(figsize=(25,15))\n", + "sns.set(font_scale=2)\n", + "ax = sns.boxplot(x=\"grade\", y=\"price\", data=df)\n", + "ax.set_title('House Grade vs. Price', fontsize=50)\n", + "ax.set_ylabel('Price', fontsize=30)\n", + "ax.set_xlabel('Grade', fontsize=30)\n", + "ax.set_ylim(bottom=0, top=6000000);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we look at grade, we can see that as the categorical building grade designation improves, the house price does indeed rise as well. " ] } ], From 56175458cde6fe9fd8e82672efce97783115b1fa Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 18:53:40 +0300 Subject: [PATCH 12/53] Update student.ipynb --- student.ipynb | 1925 ++++++++++++++++--------------------------------- 1 file changed, 622 insertions(+), 1303 deletions(-) diff --git a/student.ipynb b/student.ipynb index 1beeaa73..03ba5c12 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,7 +7,7 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng.\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Derrick Kiprotich, Clyde Ochieng. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -54,18 +54,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Data Understanding:\n", + "### Hypothesis\n", + "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", "\n", - "The real estate agency in Kingsway is analyzing a dataset to determine the factors affecting house prices. The dataset likely includes features such as property size, location, age, and market trends. Key steps include assessing data quality, exploring relationships between features and prices, and preprocessing data for multilinear regression analysis. Multilinear regression will be used to model how these features collectively influence house prices, with evaluation metrics used to assess predictive accuracy." + "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The dataset utilized in this analysis is the King County Housing dataset, encompassing details on over 21,000 homes within King County. Each entry in the dataset includes information on various features such as bedroom/bathroom/floor counts, living space and lot square footage, zip code, building grade, condition, and more.\n", + "### Data Understanding:\n", "\n", - "The King County Housing Dataset comprises multiple features contributing to the final sale price of homes in King County. Descriptions of these features are provided below." + "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." ] }, { @@ -74,1139 +75,110 @@ "source": [ "##### King County Housing Data Columns \n", "\n", - "* `id` - Unique identifier for a house\n", - "* `date` - Date house was sold\n", - "* `price` - Sale price (prediction target)\n", - "* `bedrooms` - Number of bedrooms\n", - "* `bathrooms` - Number of bathrooms\n", - "* `sqft_living` - Square footage of living space in the home\n", - "* `sqft_lot` - Square footage of the lot\n", - "* `floors` - Number of floors (levels) in house\n", - "* `waterfront` - Whether the house is on a waterfront\n", - "* `view` - Quality of view from house\n", - "* `condition` - How good the overall condition of the house is. \n", - "* `grade` - Overall grade of the house. \n", - "* `sqft_above` - Square footage of house apart from basement \n", - "* `sqft_basement` - Square footage of the basement – (Ignored)\n", - "* `yr_built` - Year when house was built\n", - "* `yr_renovated` - Year when house was renovated – (Ignored)\n", - "* `zipcode` - ZIP Code used by the United States Postal Service \n", - "* `lat` - Latitude coordinate\n", - "* `long` - Longitude coordinate\n", - "* `sqft_living15` - The square footage of interior housing living space for the nearest 15 neighbors\n", - "* `sqft_lot15` - The square footage of the land lots of the nearest 15 neighbors" + "The column names contained in column_names.md are:\n", + "* `id`: A unique identifier for each house sale.\n", + "* `date`: The date when the house was sold.\n", + "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", + "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", + "* `floors`: The number of floors in the house.\n", + "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", + "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", + "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", + "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Data Preparation\n", + "## Data Loading\n", "\n", - "Importing data." + "#### Import Necessary Libraries" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "#importing libraries \n", - "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", "import numpy as np\n", - "from matplotlib import pyplot as plt\n", + "import pandas as pd\n", + "import scipy.stats as stats\n", "import seaborn as sns\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.linear_model import LinearRegression\n", - "import statsmodels.api as sm\n", - "from statsmodels.formula.api import ols\n", - "from scipy import stats\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(21597, 21)" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21,597 houses with 21 features." - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idpricebedroomsbathroomssqft_livingsqft_lotfloorssqft_aboveyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
count21597.0000021597.0000021597.0000021597.0000021597.0000021597.0000021597.0000021597.0000021597.0000017755.0000021597.0000021597.0000021597.0000021597.0000021597.00000
mean4580474287.77099540296.573513.373202.115832080.3218515099.408761.494101788.596841970.9996883.6367898077.9518547.56009-122.213981986.6203212758.28351
std2876735715.74778367368.140100.926300.76898918.1061341412.636880.53968827.7597629.37523399.9464153.513070.138550.14072685.2304727274.44195
min1000102.0000078000.000001.000000.50000370.00000520.000001.00000370.000001900.000000.0000098001.0000047.15590-122.51900399.00000651.00000
25%2123049175.00000322000.000003.000001.750001430.000005040.000001.000001190.000001951.000000.0000098033.0000047.47110-122.328001490.000005100.00000
50%3904930410.00000450000.000003.000002.250001910.000007618.000001.500001560.000001975.000000.0000098065.0000047.57180-122.231001840.000007620.00000
75%7308900490.00000645000.000004.000002.500002550.0000010685.000002.000002210.000001997.000000.0000098118.0000047.67800-122.125002360.0000010083.00000
max9900000190.000007700000.0000033.000008.0000013540.000001651359.000003.500009410.000002015.000002015.0000098199.0000047.77760-121.315006210.00000871200.00000
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" - ], - "text/plain": [ - " id price bedrooms bathrooms sqft_living \\\n", - "count 21597.00000 21597.00000 21597.00000 21597.00000 21597.00000 \n", - "mean 4580474287.77099 540296.57351 3.37320 2.11583 2080.32185 \n", - "std 2876735715.74778 367368.14010 0.92630 0.76898 918.10613 \n", - "min 1000102.00000 78000.00000 1.00000 0.50000 370.00000 \n", - "25% 2123049175.00000 322000.00000 3.00000 1.75000 1430.00000 \n", - "50% 3904930410.00000 450000.00000 3.00000 2.25000 1910.00000 \n", - "75% 7308900490.00000 645000.00000 4.00000 2.50000 2550.00000 \n", - "max 9900000190.00000 7700000.00000 33.00000 8.00000 13540.00000 \n", - "\n", - " sqft_lot floors sqft_above yr_built yr_renovated \\\n", - "count 21597.00000 21597.00000 21597.00000 21597.00000 17755.00000 \n", - "mean 15099.40876 1.49410 1788.59684 1970.99968 83.63678 \n", - "std 41412.63688 0.53968 827.75976 29.37523 399.94641 \n", - "min 520.00000 1.00000 370.00000 1900.00000 0.00000 \n", - "25% 5040.00000 1.00000 1190.00000 1951.00000 0.00000 \n", - "50% 7618.00000 1.50000 1560.00000 1975.00000 0.00000 \n", - "75% 10685.00000 2.00000 2210.00000 1997.00000 0.00000 \n", - "max 1651359.00000 3.50000 9410.00000 2015.00000 2015.00000 \n", - "\n", - " zipcode lat long sqft_living15 sqft_lot15 \n", - "count 21597.00000 21597.00000 21597.00000 21597.00000 21597.00000 \n", - "mean 98077.95185 47.56009 -122.21398 1986.62032 12758.28351 \n", - "std 53.51307 0.13855 0.14072 685.23047 27274.44195 \n", - "min 98001.00000 47.15590 -122.51900 399.00000 651.00000 \n", - "25% 98033.00000 47.47110 -122.32800 1490.00000 5100.00000 \n", - "50% 98065.00000 47.57180 -122.23100 1840.00000 7620.00000 \n", - "75% 98118.00000 47.67800 -122.12500 2360.00000 10083.00000 \n", - "max 98199.00000 47.77760 -121.31500 6210.00000 871200.00000 " - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 21597.00000\n", - "mean 540296.57351\n", - "std 367368.14010\n", - "min 78000.00000\n", - "25% 322000.00000\n", - "50% 450000.00000\n", - "75% 645000.00000\n", - "max 7700000.00000\n", - "Name: price, dtype: float64" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive statistics for our target price.\n", - "df['price'].describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The average price of homes in the data set is 540,297 dollars. \n", - "The prices ranges from 78,000 to 8,000,000 dollars and\n", - "the median house price is 450,000 dollars" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 21597.00000\n", - "mean 2080.32185\n", - "std 918.10613\n", - "min 370.00000\n", - "25% 1430.00000\n", - "50% 1910.00000\n", - "75% 2550.00000\n", - "max 13540.00000\n", - "Name: sqft_living, dtype: float64" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# descriptive statistics for square footage\n", - "df['sqft_living'].describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The mean square-feet of living space is 2,080 sq-ft and the range of living space ranges from 370 sq-ft to 13,540 sq-ft. The median sq footage is 1,910." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bedrooms\n", - "3 9824\n", - "4 6882\n", - "2 2760\n", - "5 1601\n", - "6 272\n", - "1 196\n", - "7 38\n", - "8 13\n", - "9 6\n", - "10 3\n", - "11 1\n", - "33 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['bedrooms'].value_counts()" + "import statsmodels.api as sm\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The bedroom counts range from 1 bedroom to 33" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bathrooms\n", - "2.50000 5377\n", - "1.00000 3851\n", - "1.75000 3048\n", - "2.25000 2047\n", - "2.00000 1930\n", - "1.50000 1445\n", - "2.75000 1185\n", - "3.00000 753\n", - "3.50000 731\n", - "3.25000 589\n", - "3.75000 155\n", - "4.00000 136\n", - "4.50000 100\n", - "4.25000 79\n", - "0.75000 71\n", - "4.75000 23\n", - "5.00000 21\n", - "5.25000 13\n", - "5.50000 10\n", - "1.25000 9\n", - "6.00000 6\n", - "0.50000 4\n", - "5.75000 4\n", - "6.75000 2\n", - "8.00000 2\n", - "6.25000 2\n", - "6.50000 2\n", - "7.50000 1\n", - "7.75000 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['bathrooms'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "floors\n", - "1.00000 10673\n", - "2.00000 8235\n", - "1.50000 1910\n", - "3.00000 611\n", - "2.50000 161\n", - "3.50000 7\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['floors'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sqft_lot\n", - "5000 358\n", - "6000 290\n", - "4000 251\n", - "7200 220\n", - "4800 119\n", - " ... \n", - "22605 1\n", - "25248 1\n", - "9934 1\n", - "9142 1\n", - "1076 1\n", - "Name: count, Length: 9776, dtype: int64" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['sqft_lot'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 21597.00000\n", - "mean 15099.40876\n", - "std 41412.63688\n", - "min 520.00000\n", - "25% 5040.00000\n", - "50% 7618.00000\n", - "75% 10685.00000\n", - "max 1651359.00000\n", - "Name: sqft_lot, dtype: float64" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['sqft_lot'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "yr_built\n", - "2014 559\n", - "2006 453\n", - "2005 450\n", - "2004 433\n", - "2003 420\n", - " ... \n", - "1933 30\n", - "1901 29\n", - "1902 27\n", - "1935 24\n", - "1934 21\n", - "Name: count, Length: 116, dtype: int64" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['yr_built'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The year built ranges from 1934 to 2014." - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "condition\n", - "Average 14020\n", - "Good 5677\n", - "Very Good 1701\n", - "Fair 170\n", - "Poor 29\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['condition'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "waterfront\n", - "NO 19075\n", - "YES 146\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['waterfront'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# examining the relationship between sqft_living and price\n", - "sns.jointplot(x='sqft_living', y='price', data=df, kind='reg')\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.jointplot(x='bathrooms', y='price', data=df, kind='reg')\n", - "plt.tight_layout()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n", - "Data Preparation Fundamentals - Applying appropriate preprocessing and feature engineering steps to tabular data in preparation for statistical modeling\n", - "\n", - "Data Cleaning Steps\n", - "Handling Missing Values: Identify and address and missing values using techniques such as dropping or replacing data.\n", - "\n", - "Handling Non-Numeric Data: A Linear regression model needs all of the features to be numeric, not categorical. Identify the data type 'object' and address them using techniques such as ordinal or one-hot encoding.\n", - "\n", - "This notebook contains a breakdown of the step-by-step processes that we used to compile, scrub, and transform our data. It includes variations of narrowing our scope and explorations into the impacts that our different transformations have on the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preprocessing with Scikit-learn\n", - "Let explore and clean our data set to prep for our Linear Regression Model.\n", - "Preprocessing Steps.\n", - "\n", - "1. Handle Missing Values\n", - "2. Convert Categorical Features into Numbers\n", - "3. Find and Remove Outliers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Handling Missing Values\n", - "Below, let's check to see if there are any NaNs in our data" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "date 0\n", - "price 0\n", - "bedrooms 0\n", - "bathrooms 0\n", - "sqft_living 0\n", - "sqft_lot 0\n", - "floors 0\n", - "waterfront 2376\n", - "view 63\n", - "condition 0\n", - "grade 0\n", - "sqft_above 0\n", - "sqft_basement 0\n", - "yr_built 0\n", - "yr_renovated 3842\n", - "zipcode 0\n", - "lat 0\n", - "long 0\n", - "sqft_living15 0\n", - "sqft_lot15 0\n", - "dtype: int64" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#locate missing values\n", - "df.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id 0.0\n", - "date 0.0\n", - "price 0.0\n", - "bedrooms 0.0\n", - "bathrooms 0.0\n", - "sqft_living 0.0\n", - "sqft_lot 0.0\n", - "floors 0.0\n", - "waterfront 11.00152798999861\n", - "view 0.29170718155299347\n", - "condition 0.0\n", - "grade 0.0\n", - "sqft_above 0.0\n", - "sqft_basement 0.0\n", - "yr_built 0.0\n", - "yr_renovated 17.78950780200954\n", - "zipcode 0.0\n", - "lat 0.0\n", - "long 0.0\n", - "sqft_living15 0.0\n", - "sqft_lot15 0.0\n" - ] - } - ], - "source": [ - "#dealing with missing values\n", - "for column in df.columns:\n", - " percentage_of_nan = (sum(df[column].isnull())/len(df[column])) * 100 \n", - " print(column, percentage_of_nan)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The feature 'waterfront' is the only feature with missing values and about 11% of the values have NaNs. Lets investigate this feature to handle it's missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "waterfront\n", - "NO 19075\n", - "YES 146\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['waterfront'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the 'waterfront' feature only has two values, yes or no.\n", - "Thus NaN values can be considered no because they do not exist in their homes." - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "df['waterfront'].fillna('NO', inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "waterfront\n", - "NO 21451\n", - "YES 146\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['waterfront'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "date 0\n", - "price 0\n", - "bedrooms 0\n", - "bathrooms 0\n", - "sqft_living 0\n", - "sqft_lot 0\n", - "floors 0\n", - "waterfront 0\n", - "view 63\n", - "condition 0\n", - "grade 0\n", - "sqft_above 0\n", - "sqft_basement 0\n", - "yr_built 0\n", - "yr_renovated 3842\n", - "zipcode 0\n", - "lat 0\n", - "long 0\n", - "sqft_living15 0\n", - "sqft_lot15 0\n", - "dtype: int64" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#recheck for missing values\n", - "df.isna().sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Convert Categorical Features into Numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our model would crash because some of the columns are non-numeric. Features with a numeric data type will work with our model, but these features need to be converted:\n", - "* waterfront (object)\n", - "* condition (object)\n", - "* grade (object)\n", - "\n", - "Let's inspect the value counts of the specified features:" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "waterfront\n", - "NO 21451\n", - "YES 146\n", - "Name: count, dtype: int64\n", - "\n", - "condition\n", - "Average 14020\n", - "Good 5677\n", - "Very Good 1701\n", - "Fair 170\n", - "Poor 29\n", - "Name: count, dtype: int64\n", - "\n", - "grade\n", - "7 Average 8974\n", - "8 Good 6065\n", - "9 Better 2615\n", - "6 Low Average 2038\n", - "10 Very Good 1134\n", - "11 Excellent 399\n", - "5 Fair 242\n", - "12 Luxury 89\n", - "4 Low 27\n", - "13 Mansion 13\n", - "3 Poor 1\n", - "Name: count, dtype: int64\n" - ] - } - ], - "source": [ - "print(df['waterfront'].value_counts())\n", - "print()\n", - "print(df['condition'].value_counts())\n", - "print()\n", - "print(df['grade'].value_counts())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Split function to seperate the numeric value of 'grade'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Grade feature is an object data type however the numeric grade is listed in the front. We will use a simple string split function to isolate the numeric part of the feature.\n", - "\n", - "Waterfront has only 2 categories and can be converted into binary in place, whereas Condition has more than 2 categories and will need to be expanded into multiple columns." - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.assign(grade=df.grade.str.split(' ')).explode('grade')" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "False 46366\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.duplicated().value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(46366, 21)" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" + "### Loading Data" ] }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "df = df.drop_duplicates(subset='id')" + "# Creating a function that loads data and return it in a dataframe\n", + "def load_data(file_path):\n", + " house_data = pd.read_csv(file_path)\n", + "\n", + " #shape\n", + " shape = house_data.shape\n", + " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", + " print()\n", + " \n", + " #Data Types\n", + " data_types = house_data.dtypes\n", + " print(\"Columns and their data types:\")\n", + " for column, dtype in data_types.items():\n", + " print(f\"{column}: {dtype}\")\n", + " print()\n", + "\n", + " return house_data\n" ] }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 17, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + }, { "data": { "text/html": [ @@ -1256,23 +228,23 @@ " 0\n", " 7129300520\n", " 10/13/2014\n", - " 221900.00000\n", + " 221900.0\n", " 3\n", - " 1.00000\n", + " 1.00\n", " 1180\n", " 5650\n", - " 1.00000\n", - " NO\n", + " 1.0\n", + " NaN\n", " NONE\n", " ...\n", - " 7\n", + " 7 Average\n", " 1180\n", " 0.0\n", " 1955\n", - " 0.00000\n", + " 0.0\n", " 98178\n", - " 47.51120\n", - " -122.25700\n", + " 47.5112\n", + " -122.257\n", " 1340\n", " 5650\n", " \n", @@ -1280,47 +252,71 @@ " 1\n", " 6414100192\n", " 12/9/2014\n", - " 538000.00000\n", + " 538000.0\n", " 3\n", - " 2.25000\n", + " 2.25\n", " 2570\n", " 7242\n", - " 2.00000\n", + " 2.0\n", " NO\n", " NONE\n", " ...\n", - " 7\n", + " 7 Average\n", " 2170\n", " 400.0\n", " 1951\n", - " 1991.00000\n", + " 1991.0\n", " 98125\n", - " 47.72100\n", - " -122.31900\n", + " 47.7210\n", + " -122.319\n", " 1690\n", " 7639\n", " \n", " \n", + " 2\n", + " 5631500400\n", + " 2/25/2015\n", + " 180000.0\n", + " 2\n", + " 1.00\n", + " 770\n", + " 10000\n", + " 1.0\n", + " NO\n", + " NONE\n", + " ...\n", + " 6 Low Average\n", + " 770\n", + " 0.0\n", + " 1933\n", + " NaN\n", + " 98028\n", + " 47.7379\n", + " -122.233\n", + " 2720\n", + " 8062\n", + " \n", + " \n", " 3\n", " 2487200875\n", " 12/9/2014\n", - " 604000.00000\n", + " 604000.0\n", " 4\n", - " 3.00000\n", + " 3.00\n", " 1960\n", " 5000\n", - " 1.00000\n", + " 1.0\n", " NO\n", " NONE\n", " ...\n", - " 7\n", + " 7 Average\n", " 1050\n", " 910.0\n", " 1965\n", - " 0.00000\n", + " 0.0\n", " 98136\n", - " 47.52080\n", - " -122.39300\n", + " 47.5208\n", + " -122.393\n", " 1360\n", " 5000\n", " \n", @@ -1328,51 +324,27 @@ " 4\n", " 1954400510\n", " 2/18/2015\n", - " 510000.00000\n", + " 510000.0\n", " 3\n", - " 2.00000\n", + " 2.00\n", " 1680\n", " 8080\n", - " 1.00000\n", + " 1.0\n", " NO\n", " NONE\n", " ...\n", - " 8\n", + " 8 Good\n", " 1680\n", " 0.0\n", " 1987\n", - " 0.00000\n", + " 0.0\n", " 98074\n", - " 47.61680\n", - " -122.04500\n", + " 47.6168\n", + " -122.045\n", " 1800\n", " 7503\n", " \n", " \n", - " 5\n", - " 7237550310\n", - " 5/12/2014\n", - " 1230000.00000\n", - " 4\n", - " 4.50000\n", - " 5420\n", - " 101930\n", - " 1.00000\n", - " NO\n", - " NONE\n", - " ...\n", - " 11\n", - " 3890\n", - " 1530.0\n", - " 2001\n", - " 0.00000\n", - " 98053\n", - " 47.65610\n", - " -122.00500\n", - " 4760\n", - " 101930\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", @@ -1400,23 +372,23 @@ " 21592\n", " 263000018\n", " 5/21/2014\n", - " 360000.00000\n", + " 360000.0\n", " 3\n", - " 2.50000\n", + " 2.50\n", " 1530\n", " 1131\n", - " 3.00000\n", + " 3.0\n", " NO\n", " NONE\n", " ...\n", - " 8\n", + " 8 Good\n", " 1530\n", " 0.0\n", " 2009\n", - " 0.00000\n", + " 0.0\n", " 98103\n", - " 47.69930\n", - " -122.34600\n", + " 47.6993\n", + " -122.346\n", " 1530\n", " 1509\n", " \n", @@ -1424,23 +396,23 @@ " 21593\n", " 6600060120\n", " 2/23/2015\n", - " 400000.00000\n", + " 400000.0\n", " 4\n", - " 2.50000\n", + " 2.50\n", " 2310\n", " 5813\n", - " 2.00000\n", + " 2.0\n", " NO\n", " NONE\n", " ...\n", - " 8\n", + " 8 Good\n", " 2310\n", " 0.0\n", " 2014\n", - " 0.00000\n", + " 0.0\n", " 98146\n", - " 47.51070\n", - " -122.36200\n", + " 47.5107\n", + " -122.362\n", " 1830\n", " 7200\n", " \n", @@ -1448,23 +420,23 @@ " 21594\n", " 1523300141\n", " 6/23/2014\n", - " 402101.00000\n", + " 402101.0\n", " 2\n", - " 0.75000\n", + " 0.75\n", " 1020\n", " 1350\n", - " 2.00000\n", + " 2.0\n", " NO\n", " NONE\n", " ...\n", - " 7\n", + " 7 Average\n", " 1020\n", " 0.0\n", " 2009\n", - " 0.00000\n", + " 0.0\n", " 98144\n", - " 47.59440\n", - " -122.29900\n", + " 47.5944\n", + " -122.299\n", " 1020\n", " 2007\n", " \n", @@ -1472,23 +444,23 @@ " 21595\n", " 291310100\n", " 1/16/2015\n", - " 400000.00000\n", + " 400000.0\n", " 3\n", - " 2.50000\n", + " 2.50\n", " 1600\n", " 2388\n", - " 2.00000\n", - " NO\n", + " 2.0\n", + " NaN\n", " NONE\n", " ...\n", - " 8\n", + " 8 Good\n", " 1600\n", " 0.0\n", " 2004\n", - " 0.00000\n", + " 0.0\n", " 98027\n", - " 47.53450\n", - " -122.06900\n", + " 47.5345\n", + " -122.069\n", " 1410\n", " 1287\n", " \n", @@ -1496,99 +468,216 @@ " 21596\n", " 1523300157\n", " 10/15/2014\n", - " 325000.00000\n", + " 325000.0\n", " 2\n", - " 0.75000\n", + " 0.75\n", " 1020\n", " 1076\n", - " 2.00000\n", + " 2.0\n", " NO\n", " NONE\n", " ...\n", - " 7\n", + " 7 Average\n", " 1020\n", " 0.0\n", " 2008\n", - " 0.00000\n", + " 0.0\n", " 98144\n", - " 47.59410\n", - " -122.29900\n", + " 47.5941\n", + " -122.299\n", " 1020\n", " 1357\n", " \n", " \n", "\n", - "

17565 rows × 21 columns

\n", + "

21597 rows × 21 columns

\n", "" ], "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.00000 3 1.00000 1180 \n", - "1 6414100192 12/9/2014 538000.00000 3 2.25000 2570 \n", - "3 2487200875 12/9/2014 604000.00000 4 3.00000 1960 \n", - "4 1954400510 2/18/2015 510000.00000 3 2.00000 1680 \n", - "5 7237550310 5/12/2014 1230000.00000 4 4.50000 5420 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.00000 3 2.50000 1530 \n", - "21593 6600060120 2/23/2015 400000.00000 4 2.50000 2310 \n", - "21594 1523300141 6/23/2014 402101.00000 2 0.75000 1020 \n", - "21595 291310100 1/16/2015 400000.00000 3 2.50000 1600 \n", - "21596 1523300157 10/15/2014 325000.00000 2 0.75000 1020 \n", + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", "\n", - " sqft_lot floors waterfront view ... grade sqft_above sqft_basement \\\n", - "0 5650 1.00000 NO NONE ... 7 1180 0.0 \n", - "1 7242 2.00000 NO NONE ... 7 2170 400.0 \n", - "3 5000 1.00000 NO NONE ... 7 1050 910.0 \n", - "4 8080 1.00000 NO NONE ... 8 1680 0.0 \n", - "5 101930 1.00000 NO NONE ... 11 3890 1530.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "21592 1131 3.00000 NO NONE ... 8 1530 0.0 \n", - "21593 5813 2.00000 NO NONE ... 8 2310 0.0 \n", - "21594 1350 2.00000 NO NONE ... 7 1020 0.0 \n", - "21595 2388 2.00000 NO NONE ... 8 1600 0.0 \n", - "21596 1076 2.00000 NO NONE ... 7 1020 0.0 \n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", "\n", - " yr_built yr_renovated zipcode lat long sqft_living15 \\\n", - "0 1955 0.00000 98178 47.51120 -122.25700 1340 \n", - "1 1951 1991.00000 98125 47.72100 -122.31900 1690 \n", - "3 1965 0.00000 98136 47.52080 -122.39300 1360 \n", - "4 1987 0.00000 98074 47.61680 -122.04500 1800 \n", - "5 2001 0.00000 98053 47.65610 -122.00500 4760 \n", - "... ... ... ... ... ... ... \n", - "21592 2009 0.00000 98103 47.69930 -122.34600 1530 \n", - "21593 2014 0.00000 98146 47.51070 -122.36200 1830 \n", - "21594 2009 0.00000 98144 47.59440 -122.29900 1020 \n", - "21595 2004 0.00000 98027 47.53450 -122.06900 1410 \n", - "21596 2008 0.00000 98144 47.59410 -122.29900 1020 \n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", "\n", - " sqft_lot15 \n", - "0 5650 \n", - "1 7639 \n", - "3 5000 \n", - "4 7503 \n", - "5 101930 \n", - "... ... \n", - "21592 1509 \n", - "21593 7200 \n", - "21594 2007 \n", - "21595 1287 \n", - "21596 1357 \n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", "\n", - "[17565 rows x 21 columns]" + "[21597 rows x 21 columns]" ] }, - "execution_count": 127, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.dropna()" + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" ] }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1596,121 +685,351 @@ "output_type": "stream", "text": [ "\n", - "Index: 21420 entries, 0 to 21596\n", + "RangeIndex: 21597 entries, 0 to 21596\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 id 21420 non-null int64 \n", - " 1 date 21420 non-null object \n", - " 2 price 21420 non-null float64\n", - " 3 bedrooms 21420 non-null int64 \n", - " 4 bathrooms 21420 non-null float64\n", - " 5 sqft_living 21420 non-null int64 \n", - " 6 sqft_lot 21420 non-null int64 \n", - " 7 floors 21420 non-null float64\n", - " 8 waterfront 21420 non-null object \n", - " 9 view 21357 non-null object \n", - " 10 condition 21420 non-null object \n", - " 11 grade 21420 non-null object \n", - " 12 sqft_above 21420 non-null int64 \n", - " 13 sqft_basement 21420 non-null object \n", - " 14 yr_built 21420 non-null int64 \n", - " 15 yr_renovated 17616 non-null float64\n", - " 16 zipcode 21420 non-null int64 \n", - " 17 lat 21420 non-null float64\n", - " 18 long 21420 non-null float64\n", - " 19 sqft_living15 21420 non-null int64 \n", - " 20 sqft_lot15 21420 non-null int64 \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.6+ MB\n" + "memory usage: 3.5+ MB\n" ] } ], "source": [ - "df.info()" + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "grade\n", - "7 8889\n", - "8 6041\n", - "9 2606\n", - "6 1995\n", - "10 1130\n", - "11 396\n", - "5 234\n", - "12 88\n", - "4 27\n", - "13 13\n", - "3 1\n", - "Name: count, dtype: int64" + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" ] }, - "execution_count": 129, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['grade'].value_counts()" + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The most common buiding grade is a 7" + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" ] }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "# Change the data type from object to int.\n", - "df['grade'] = df['grade'].astype(int)" + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" ] }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" ] }, + "execution_count": 28, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "#grade\n", - "plt.figure(figsize=(25,15))\n", - "sns.set(font_scale=2)\n", - "ax = sns.boxplot(x=\"grade\", y=\"price\", data=df)\n", - "ax.set_title('House Grade vs. Price', fontsize=50)\n", - "ax.set_ylabel('Price', fontsize=30)\n", - "ax.set_xlabel('Grade', fontsize=30)\n", - "ax.set_ylim(bottom=0, top=6000000);" + "identify_issues(house_data_clean)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 29, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
2003886489001102014555000.032.50194032112.0NONONEAverage8 Good19400.020099802747.5644-122.09318803078
\n", + "
" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "When we look at grade, we can see that as the categorical building grade designation improves, the house price does indeed rise as well. " + "house_data_clean[house_data_clean.duplicated()]" ] } ], From ce7d81d196580524a1fbf0b0df978bd49472bd9a Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 18:56:03 +0300 Subject: [PATCH 13/53] Update student.ipynb --- student.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 03ba5c12..ed8e6379 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,7 +7,7 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Derrick Kiprotich, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", From 26261b768e2cc3e4d4687414bc5c2a97decea023 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 18:59:34 +0300 Subject: [PATCH 14/53] Update student.ipynb --- student.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/student.ipynb b/student.ipynb index b3e32a00..59641be8 100644 --- a/student.ipynb +++ b/student.ipynb @@ -7,7 +7,7 @@ "## Final Project Submission\n", "\n", "Please fill out:\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng.\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Derrick Kiptoo Clyde Ochieng.\n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -105,7 +105,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.10.13" } }, "nbformat": 4, From 6c8c8b7272dd2b18c70ccc37b0c3db82aed9e419 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 19:02:17 +0300 Subject: [PATCH 15/53] Update student.ipynb --- student.ipynb | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/student.ipynb b/student.ipynb index ed8e6379..946abd6c 100644 --- a/student.ipynb +++ b/student.ipynb @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -549,7 +549,7 @@ "[21597 rows x 21 columns]" ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -571,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -677,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -721,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -739,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -770,7 +770,7 @@ " dtype: float64}" ] }, - "execution_count": 23, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -795,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -804,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -834,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -851,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -871,7 +871,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -901,7 +901,7 @@ " dtype: float64}" ] }, - "execution_count": 28, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -912,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1023,7 +1023,7 @@ "20038 2009 98027 47.5644 -122.093 1880 3078 " ] }, - "execution_count": 29, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } From faab87e08b0594aa0a086bfac402e06c194be1a0 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:00:38 +0300 Subject: [PATCH 16/53] Update student.ipynb --- student.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 09a140ae..d18aa6f3 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,6 @@ "source": [ "## Final Project Submission\n", "\n", - "Please fill out:\n", Clyde "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", From 42bea008534fe4c9531c932f1686733927c8b124 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:08:00 +0300 Subject: [PATCH 17/53] Update student.ipynb --- student.ipynb | 72 +++++++++++++++++++++++++-------------------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/student.ipynb b/student.ipynb index d18aa6f3..7d3fde3c 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1,40 +1,40 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", -Clyde - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", - - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", - main - "* Student pace: full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: Nikita \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Project Submission\n", + "\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", + "* Student pace: full time\n", + "* Scheduled project review date/time: \n", + "* Instructor name: Nikita \n", + "* Blog post URL:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + } + ] + } + "cell_type": "markdown", "metadata": {}, "source": [ From 872b835d4f541d0fc23995a1d9947487c482915e Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:11:24 +0300 Subject: [PATCH 18/53] Update student.ipynb --- student.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 7d3fde3c..45f116ae 100644 --- a/student.ipynb +++ b/student.ipynb @@ -35,7 +35,6 @@ ] } - "cell_type": "markdown", "metadata": {}, "source": [ "## Business Problem\n", From d068fa1bd538aa473be4cbcba93ff4cfa58cbdbe Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:14:58 +0300 Subject: [PATCH 19/53] Update student.ipynb --- student.ipynb | 66 +++++++++++++++++++++++++-------------------------- 1 file changed, 32 insertions(+), 34 deletions(-) diff --git a/student.ipynb b/student.ipynb index 45f116ae..4593bb43 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1,40 +1,38 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", - - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", - "* Student pace: full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: Nikita \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - } + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Project Submission\n", + "\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", + "* Student pace: full time\n", + "* Scheduled project review date/time: \n", + "* Instructor name: Nikita \n", + "* Blog post URL:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + } ] - } - + } + "metadata": {}, "source": [ "## Business Problem\n", From 18077fac0082e399f6ab72625423e677fd7b8764 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:43:40 +0300 Subject: [PATCH 20/53] Update student.ipynb --- student.ipynb | 164 +++++++++++++++++++++----------------------------- 1 file changed, 69 insertions(+), 95 deletions(-) diff --git a/student.ipynb b/student.ipynb index 4593bb43..c174e446 100644 --- a/student.ipynb +++ b/student.ipynb @@ -29,101 +29,75 @@ "\n", "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." ] - } - ] - } - - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n", - "#### Import Necessary Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Problem\n", + "\n", + "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objectives\n", + "* To determine the key factors influencing house prices.\n", + "* To develop multilinear regression models to predict house prices based on relevant features.\n", + "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hypothesis\n", + "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", + "\n", + "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Understanding:\n", + "\n", + "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### King County Housing Data Columns \n", + "\n", + "The column names contained in column_names.md are:\n", + "* `id`: A unique identifier for each house sale.\n", + "* `date`: The date when the house was sold.\n", + "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", + "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", + "* `floors`: The number of floors in the house.\n", + "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", + "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", + "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", + "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ "# Creating a function that loads data and return it in a dataframe\n", "def load_data(file_path):\n", " house_data = pd.read_csv(file_path)\n", From 457ad7cc459ba0165caa25a11e4788feddf3eda9 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:44:41 +0300 Subject: [PATCH 21/53] Update student.ipynb --- student.ipynb | 2025 +++++++++++++++++++++++++------------------------ 1 file changed, 1013 insertions(+), 1012 deletions(-) diff --git a/student.ipynb b/student.ipynb index c174e446..cea94b8e 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1,1031 +1,1032 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", - "* Student pace: full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: Nikita \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n" - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Creating a function that loads data and return it in a dataframe\n", - "def load_data(file_path):\n", - " house_data = pd.read_csv(file_path)\n", - "\n", - " #shape\n", - " shape = house_data.shape\n", - " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", - " print()\n", - " \n", - " #Data Types\n", - " data_types = house_data.dtypes\n", - " print(\"Columns and their data types:\")\n", - " for column, dtype in data_types.items():\n", - " print(f\"{column}: {dtype}\")\n", - " print()\n", - "\n", - " return house_data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Project Submission\n", + "\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student pace: full time\n", + "* Scheduled project review date/time: \n", + "* Instructor name: Nikita \n", + "* Blog post URL:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Problem\n", + "\n", + "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objectives\n", + "* To determine the key factors influencing house prices.\n", + "* To develop multilinear regression models to predict house prices based on relevant features.\n", + "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hypothesis\n", + "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", + "\n", + "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Understanding:\n", + "\n", + "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### King County Housing Data Columns \n", + "\n", + "The column names contained in column_names.md are:\n", + "* `id`: A unique identifier for each house sale.\n", + "* `date`: The date when the house was sold.\n", + "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", + "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", + "* `floors`: The number of floors in the house.\n", + "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", + "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", + "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", + "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n" + ] }, { - "data": { - "text/html": [ - "
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0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
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" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a function that loads data and return it in a dataframe\n", + "def load_data(file_path):\n", + " house_data = pd.read_csv(file_path)\n", + "\n", + " #shape\n", + " shape = house_data.shape\n", + " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", + " print()\n", + " \n", + " #Data Types\n", + " data_types = house_data.dtypes\n", + " print(\"Columns and their data types:\")\n", + " for column, dtype in data_types.items():\n", + " print(f\"{column}: {dtype}\")\n", + " print()\n", + "\n", + " return house_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontview...gradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538000.032.25257072422.0NONONE...7 Average2170400.019511991.09812547.7210-122.31916907639
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3248720087512/9/2014604000.043.00196050001.0NONONE...7 Average1050910.019650.09813647.5208-122.39313605000
419544005102/18/2015510000.032.00168080801.0NONONE...8 Good16800.019870.09807447.6168-122.04518007503
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215922630000185/21/2014360000.032.50153011313.0NONONE...8 Good15300.020090.09810347.6993-122.34615301509
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21597 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
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" + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " + "source": [ + "kings_data.info()" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } From e409f03f1cd19c8332e7b24b138dae76150ffc25 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:53:19 +0300 Subject: [PATCH 22/53] Update student.ipynb --- student.ipynb | 206 ++++++++++++++++++++++++-------------------------- 1 file changed, 99 insertions(+), 107 deletions(-) diff --git a/student.ipynb b/student.ipynb index 09a140ae..e4128c3d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1,111 +1,103 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", - "Please fill out:\n", -Clyde - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", - - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", - main - "* Student pace: full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: Nikita \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n", - "#### Import Necessary Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Project Submission\n", + "\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", + "* Student pace: full time\n", + "* Scheduled project review date/time: \n", + "* Instructor name: Nikita \n", + "* Blog post URL:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Problem\n", + "\n", + "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objectives\n", + "* To determine the key factors influencing house prices.\n", + "* To develop multilinear regression models to predict house prices based on relevant features.\n", + "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hypothesis\n", + "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", + "\n", + "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Understanding:\n", + "\n", + "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### King County Housing Data Columns \n", + "\n", + "The column names contained in column_names.md are:\n", + "* `id`: A unique identifier for each house sale.\n", + "* `date`: The date when the house was sold.\n", + "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", + "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", + "* `floors`: The number of floors in the house.\n", + "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", + "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", + "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", + "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np\n", From 9824144694280c2400705fbd29f06296d7cb192d Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:55:32 +0300 Subject: [PATCH 23/53] Update student.ipynb --- student.ipynb | 2070 +++++++++++++++++++++++++------------------------ 1 file changed, 1036 insertions(+), 1034 deletions(-) diff --git a/student.ipynb b/student.ipynb index e4128c3d..3d526e43 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1,1053 +1,1055 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", - "* Student pace: full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: Nikita \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n" - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a function that loads data and return it in a dataframe\n", - "def load_data(file_path):\n", - " house_data = pd.read_csv(file_path)\n", - "\n", - " #shape\n", - " shape = house_data.shape\n", - " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", - " print()\n", - " \n", - " #Data Types\n", - " data_types = house_data.dtypes\n", - " print(\"Columns and their data types:\")\n", - " for column, dtype in data_types.items():\n", - " print(f\"{column}: {dtype}\")\n", - " print()\n", - "\n", - " return house_data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Project Submission\n", + "\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", + "* Student pace: full time\n", + "* Scheduled project review date/time: \n", + "* Instructor name: Nikita \n", + "* Blog post URL:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Problem\n", + "\n", + "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objectives\n", + "* To determine the key factors influencing house prices.\n", + "* To develop multilinear regression models to predict house prices based on relevant features.\n", + "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hypothesis\n", + "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", + "\n", + "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Understanding:\n", + "\n", + "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### King County Housing Data Columns \n", + "\n", + "The column names contained in column_names.md are:\n", + "* `id`: A unique identifier for each house sale.\n", + "* `date`: The date when the house was sold.\n", + "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", + "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", + "* `floors`: The number of floors in the house.\n", + "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", + "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", + "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", + "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." + ] }, { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontview...gradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538000.032.25257072422.0NONONE...7 Average2170400.019511991.09812547.7210-122.31916907639
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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.stats as stats\n", + "import seaborn as sns\n", + "import statsmodels.api as sm\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a function that loads data and return it in a dataframe\n", + "def load_data(file_path):\n", + " house_data = pd.read_csv(file_path)\n", + "\n", + " #shape\n", + " shape = house_data.shape\n", + " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", + " print()\n", + " \n", + " #Data Types\n", + " data_types = house_data.dtypes\n", + " print(\"Columns and their data types:\")\n", + " for column, dtype in data_types.items():\n", + " print(f\"{column}: {dtype}\")\n", + " print()\n", + "\n", + " return house_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
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21597 rows × 21 columns

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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "
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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "nbformat": 4, + "nbformat_minor": 2 } From b05f711c493aa16e95ed47e02c83d8d582ed1acc Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Tue, 30 Apr 2024 23:56:31 +0300 Subject: [PATCH 24/53] Update student.ipynb --- student.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/student.ipynb b/student.ipynb index 3d526e43..6e366080 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,8 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo.\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng.\n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", From 990054edf2f11aae7589f9f0b24f0079e57ea7e3 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 10:18:15 +0300 Subject: [PATCH 25/53] Update student.ipynb --- student.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index cea94b8e..bde1baf8 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", From 57e844e323b484dcd7257fad75cfa16dbc1972f1 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 10:24:54 +0300 Subject: [PATCH 26/53] Update student.ipynb --- student.ipynb | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/student.ipynb b/student.ipynb index 6e366080..f8c41695 100644 --- a/student.ipynb +++ b/student.ipynb @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -546,7 +546,7 @@ "[21597 rows x 21 columns]" ] }, - "execution_count": 3, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -568,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -609,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -630,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -674,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -718,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -736,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -767,7 +767,7 @@ " dtype: float64}" ] }, - "execution_count": 9, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -792,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -801,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -831,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -848,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -868,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -898,7 +898,7 @@ " dtype: float64}" ] }, - "execution_count": 14, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -909,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1020,7 +1020,7 @@ "20038 2009 98027 47.5644 -122.093 1880 3078 " ] }, - "execution_count": 15, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } From aa1c14e5ff58f5b2840b8e60e2cf447ab8214c60 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 10:34:20 +0300 Subject: [PATCH 27/53] Update student.ipynb --- student.ipynb | 138 +------------------------------------------------- 1 file changed, 1 insertion(+), 137 deletions(-) diff --git a/student.ipynb b/student.ipynb index 6fdc9263..27522862 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,148 +6,13 @@ "source": [ "## Final Project Submission\n", "\n", -Clyde - - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo \n", - main + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", "* Blog post URL:\n" ] }, - Clyde - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading Data" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a function that loads data and return it in a dataframe\n", - "def load_data(file_path):\n", - " house_data = pd.read_csv(file_path)\n", - "\n", - " #shape\n", - " shape = house_data.shape\n", - " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", - " print()\n", - " \n", - " #Data Types\n", - " data_types = house_data.dtypes\n", - " print(\"Columns and their data types:\")\n", - " for column, dtype in data_types.items():\n", - " print(f\"{column}: {dtype}\")\n", - " print()\n", - "\n", - " return house_data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - { "cell_type": "markdown", "metadata": {}, @@ -256,7 +121,6 @@ Clyde { "cell_type": "code", "execution_count": 3, - main "metadata": {}, "outputs": [ { From cbb5de189d98ee4c56dc7720b876ef3fa82fabf4 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:06:49 +0300 Subject: [PATCH 28/53] Update student.ipynb --- student.ipynb | 1118 ++++++++++++++++--------------------------------- 1 file changed, 352 insertions(+), 766 deletions(-) diff --git a/student.ipynb b/student.ipynb index 27522862..247e570c 100644 --- a/student.ipynb +++ b/student.ipynb @@ -342,6 +342,7 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 21592\n", @@ -425,7 +426,7 @@ " 1600\n", " 2388\n", " 2.0\n", - " NaN\n", + " NO\n", " NONE\n", " ...\n", " 8 Good\n", @@ -439,33 +440,9 @@ " 1410\n", " 1287\n", " \n", - " \n", - " 21596\n", - " 1523300157\n", - " 10/15/2014\n", - " 325000.0\n", - " 2\n", - " 0.75\n", - " 1020\n", - " 1076\n", - " 2.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 7 Average\n", - " 1020\n", - " 0.0\n", - " 2008\n", - " 0.0\n", - " 98144\n", - " 47.5941\n", - " -122.299\n", - " 1020\n", - " 1357\n", - " \n", " \n", "\n", - "

21597 rows × 21 columns

\n", + "

21596 rows × 21 columns

\n", "" ], "text/plain": [ @@ -524,764 +501,373 @@ "[21597 rows x 21 columns]" ] }, -Clyde - "execution_count": 18, - - "execution_count": 3, - main + { + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is $7,700,000 and the minimum price is $78,000.\n", + "\n", + "There are 21,597 prices regarding the houses in the dataset.\n", + "\n", + "The average price of a house is $540,296.57." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 + } + "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - Clyde - "execution_count": 19, - - "execution_count": 4, - main - "metadata": {}, - "outputs": [ + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - Clyde - "execution_count": 20, - - "execution_count": 5, - main - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - Clyde - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - main - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - Clyde - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - main - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - Clyde - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, + "cell_type": "code", + "execution_count": 6, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - main - ] - }, - { - "cell_type": "code", - Clyde - "execution_count": 24, - - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - main - "metadata": {}, - "outputs": [ + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, { - "data": { - "text/plain": [ - Clyde - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 24, - - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - main + "cell_type": "code", + "execution_count": 7, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - Clyde - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 29, + "cell_type": "code", + "execution_count": 8, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - main - "metadata": {}, - "outputs": [ + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 0.00\n", + " view 0.00\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - Clyde - "execution_count": 30, - - "execution_count": 15, - main + "cell_type": "code", + "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "outputs": [], + "source": [ + "# Checking for duplicates in the dataset\n", + "house_data_clean[house_data_clean.duplicated()]" + ] } ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 5 + } \ No newline at end of file From 90359262269e6daaee8bbcd4a12e41f0a9f9a634 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:09:22 +0300 Subject: [PATCH 29/53] Update student.ipynb --- student.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 247e570c..40941025 100644 --- a/student.ipynb +++ b/student.ipynb @@ -501,7 +501,7 @@ "[21597 rows x 21 columns]" ] }, - { + "cells": [ { "cell_type": "code", From da626e5a98ef9ce6374de659e6f8b27748dc2cd0 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:13:05 +0300 Subject: [PATCH 30/53] Update student.ipynb --- student.ipynb | 451 +++++++++++++++++++++++++------------------------- 1 file changed, 229 insertions(+), 222 deletions(-) diff --git a/student.ipynb b/student.ipynb index 40941025..ab38df0d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -645,229 +645,236 @@ "nbformat_minor": 5 } - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" + { + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" ] }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 0.00\n", - " view 0.00\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" ] }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Checking for duplicates in the dataset\n", - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 - } \ No newline at end of file + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': {\n", + " 'id': 0.0,\n", + " 'date': 0.0,\n", + " 'price': 0.0,\n", + " 'bedrooms': 0.0,\n", + " 'bathrooms': 0.0,\n", + " 'sqft_living': 0.0,\n", + " 'sqft_lot': 0.0,\n", + " 'floors': 0.0,\n", + " 'waterfront': 11.0,\n", + " 'view': 0.29,\n", + " 'condition': 0.0,\n", + " 'grade': 0.0,\n", + " 'sqft_above': 0.0,\n", + " 'sqft_basement': 0.0,\n", + " 'yr_built': 0.0,\n", + " 'yr_renovated': 17.79,\n", + " 'zipcode': 0.0,\n", + " 'lat': 0.0,\n", + " 'long': 0.0,\n", + " 'sqft_living15': 0.0,\n", + " 'sqft_lot15': 0.0\n", + " }}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 0.00\n", + " view 0.00\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Checking for duplicates in the dataset\n", + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 + } + \ No newline at end of file From 48e99978650e729af9d4901d484b9c7babe585fb Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:14:21 +0300 Subject: [PATCH 31/53] Update student.ipynb --- student.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/student.ipynb b/student.ipynb index ab38df0d..b8e4f974 100644 --- a/student.ipynb +++ b/student.ipynb @@ -644,8 +644,7 @@ "nbformat": 4, "nbformat_minor": 5 } - - { + { "cells": [ { "cell_type": "code", From 571eda3d3a01ed7d6f054fcc00212bce4d186755 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:30:59 +0300 Subject: [PATCH 32/53] Update student.ipynb --- student.ipynb | 666 ++++++++++++++++++++++++-------------------------- 1 file changed, 323 insertions(+), 343 deletions(-) diff --git a/student.ipynb b/student.ipynb index b8e4f974..0893adb3 100644 --- a/student.ipynb +++ b/student.ipynb @@ -533,347 +533,327 @@ "output_type": "stream", "text": [ "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is $7,700,000 and the minimum price is $78,000.\n", - "\n", - "There are 21,597 prices regarding the houses in the dataset.\n", - "\n", - "The average price of a house is $540,296.57." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 - } - { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': {\n", - " 'id': 0.0,\n", - " 'date': 0.0,\n", - " 'price': 0.0,\n", - " 'bedrooms': 0.0,\n", - " 'bathrooms': 0.0,\n", - " 'sqft_living': 0.0,\n", - " 'sqft_lot': 0.0,\n", - " 'floors': 0.0,\n", - " 'waterfront': 11.0,\n", - " 'view': 0.29,\n", - " 'condition': 0.0,\n", - " 'grade': 0.0,\n", - " 'sqft_above': 0.0,\n", - " 'sqft_basement': 0.0,\n", - " 'yr_built': 0.0,\n", - " 'yr_renovated': 17.79,\n", - " 'zipcode': 0.0,\n", - " 'lat': 0.0,\n", - " 'long': 0.0,\n", - " 'sqft_living15': 0.0,\n", - " 'sqft_lot15': 0.0\n", - " }}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 0.00\n", - " view 0.00\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Checking for duplicates in the dataset\n", - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 - } +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is $7,700,000 and the minimum price is $78,000.\n", + "\n", + "There are 21,597 prices regarding the houses in the dataset.\n", + "\n", + "The average price of a house is $540,296.57." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5, +"cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': {\n", + " 'id': 0.0,\n", + " 'date': 0.0,\n", + " 'price': 0.0,\n", + " 'bedrooms': 0.0,\n", + " 'bathrooms': 0.0,\n", + " 'sqft_living': 0.0,\n", + " 'sqft_lot': 0.0,\n", + " 'floors': 0.0,\n", + " 'waterfront': 11.0,\n", + " 'view': 0.29,\n", + " 'condition': 0.0,\n", + " 'grade': 0.0,\n", + " 'sqft_above': 0.0,\n", + " 'sqft_basement': 0.0,\n", + " 'yr_built': 0.0,\n", + " 'yr_renovated': 17.79,\n", + " 'zipcode': 0.0,\n", + " 'lat': 0.0,\n", + " 'long': 0.0,\n", + " 'sqft_living15': 0.0,\n", + " 'sqft_lot15': 0.0\n", + " }}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 0.00\n", + " view 0.00\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Checking for duplicates in the dataset\n", + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} + + \ No newline at end of file From 0edfa118235af2f6329f710be238452ff730db5b Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:32:17 +0300 Subject: [PATCH 33/53] Update student.ipynb --- student.ipynb | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/student.ipynb b/student.ipynb index 0893adb3..c9d9d88b 100644 --- a/student.ipynb +++ b/student.ipynb @@ -853,7 +853,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} - - - \ No newline at end of file +} \ No newline at end of file From 9d605250e480b913d8a7e552b6a90206d811e08f Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:43:49 +0300 Subject: [PATCH 34/53] Update student.ipynb --- student.ipynb | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/student.ipynb b/student.ipynb index c9d9d88b..82d7ee49 100644 --- a/student.ipynb +++ b/student.ipynb @@ -827,11 +827,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Checking for duplicates in the dataset\n", - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], + "# Checking for duplicates in the dataset\n", + "house_data_clean[house_data_clean.duplicated()]" + ] +}, +{ +"cells": [], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -847,7 +848,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", + "pygments_lexer": "ipython3", "version": "3.8.11" } }, From 14adaad0364e6d4495b633c371b72cc1c352a3d6 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:45:00 +0300 Subject: [PATCH 35/53] Update student.ipynb --- student.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/student.ipynb b/student.ipynb index 82d7ee49..7aa3d364 100644 --- a/student.ipynb +++ b/student.ipynb @@ -854,4 +854,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +}, \ No newline at end of file From 4275e4e074c4b71e4d9493cb2357b7011a1332cf Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:52:56 +0300 Subject: [PATCH 36/53] Update student.ipynb --- student.ipynb | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/student.ipynb b/student.ipynb index 6fdc9263..59bab0ab 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,24 +6,20 @@ "source": [ "## Final Project Submission\n", "\n", -Clyde - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo \n", - main - "* Student pace: full time\n", + "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", "* Blog post URL:\n" ] }, - Clyde { "cell_type": "markdown", "metadata": {}, "source": [ "# Kings County Housing Analysis with Multiple Linear Regression" ] - }, + } { "cell_type": "markdown", "metadata": {}, From 11616b1c966613b440f2144e2b3a8d18708bf2a3 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:54:28 +0300 Subject: [PATCH 37/53] Update student.ipynb --- student.ipynb | 1515 ++++++++++++++++++------------------------------- 1 file changed, 564 insertions(+), 951 deletions(-) diff --git a/student.ipynb b/student.ipynb index 59bab0ab..4a29c2f7 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,144 +6,13 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo \n", - "* Student pace: full time\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", "* Blog post URL:\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - } - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading Data" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a function that loads data and return it in a dataframe\n", - "def load_data(file_path):\n", - " house_data = pd.read_csv(file_path)\n", - "\n", - " #shape\n", - " shape = house_data.shape\n", - " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", - " print()\n", - " \n", - " #Data Types\n", - " data_types = house_data.dtypes\n", - " print(\"Columns and their data types:\")\n", - " for column, dtype in data_types.items():\n", - " print(f\"{column}: {dtype}\")\n", - " print()\n", - "\n", - " return house_data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - { "cell_type": "markdown", "metadata": {}, @@ -252,7 +121,6 @@ { "cell_type": "code", "execution_count": 3, - main "metadata": {}, "outputs": [ { @@ -599,821 +467,566 @@ "\n", "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, -Clyde - "execution_count": 18, - - "execution_count": 3, - main - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - Clyde - "execution_count": 19, - - "execution_count": 4, - main - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - Clyde - "execution_count": 20, - - "execution_count": 5, - main - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - Clyde - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - main - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - Clyde - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - main - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - Clyde - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - main - ] - }, - { - "cell_type": "code", - Clyde - "execution_count": 24, - - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - main - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - Clyde - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 24, - - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - main - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - Clyde - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - main - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
2003886489001102014555000.032.50194032112.0NONONEAverage8 Good19400.020099802747.5644-122.09318803078
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - Clyde - "execution_count": 30, - - "execution_count": 15, - main - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] +}, +{ + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] +}, +{ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] +}, +{ + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] +}, +{ + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] +}, +{ + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] +}, +{ + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] +}, +{ + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] +}, +{ + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] +}, +{ + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] +}, +{ + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] +}, +{ + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] +}, +{ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] +} +], +"metadata": { +"kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" +}, +"language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" +} +}, +"nbformat": 4, +"nbformat_minor": 2 } From 7d271f1f0b6a80bf4a1fba855a89a1a60b1a94e1 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:54:48 +0300 Subject: [PATCH 38/53] Update student.ipynb --- student.ipynb | 999 +++++++++++++++++++++++++++++--------------------- 1 file changed, 587 insertions(+), 412 deletions(-) diff --git a/student.ipynb b/student.ipynb index 7aa3d364..3789754d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -342,7 +342,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " 21592\n", @@ -426,7 +425,7 @@ " 1600\n", " 2388\n", " 2.0\n", - " NO\n", + " NaN\n", " NONE\n", " ...\n", " 8 Good\n", @@ -440,418 +439,594 @@ " 1410\n", " 1287\n", " \n", + " \n", + " 21596\n", + " 1523300157\n", + " 10/15/2014\n", + " 325000.0\n", + " 2\n", + " 0.75\n", + " 1020\n", + " 1076\n", + " 2.0\n", + " NO\n", + " NONE\n", + " ...\n", + " 7 Average\n", + " 1020\n", + " 0.0\n", + " 2008\n", + " 0.0\n", + " 98144\n", + " 47.5941\n", + " -122.299\n", + " 1020\n", + " 1357\n", + " \n", " \n", "\n", - "

21596 rows × 21 columns

\n", + "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - - "cells": [ - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] +}, { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is $7,700,000 and the minimum price is $78,000.\n", - "\n", - "There are 21,597 prices regarding the houses in the dataset.\n", - "\n", - "The average price of a house is $540,296.57." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5, -"cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': {\n", - " 'id': 0.0,\n", - " 'date': 0.0,\n", - " 'price': 0.0,\n", - " 'bedrooms': 0.0,\n", - " 'bathrooms': 0.0,\n", - " 'sqft_living': 0.0,\n", - " 'sqft_lot': 0.0,\n", - " 'floors': 0.0,\n", - " 'waterfront': 11.0,\n", - " 'view': 0.29,\n", - " 'condition': 0.0,\n", - " 'grade': 0.0,\n", - " 'sqft_above': 0.0,\n", - " 'sqft_basement': 0.0,\n", - " 'yr_built': 0.0,\n", - " 'yr_renovated': 17.79,\n", - " 'zipcode': 0.0,\n", - " 'lat': 0.0,\n", - " 'long': 0.0,\n", - " 'sqft_living15': 0.0,\n", - " 'sqft_lot15': 0.0\n", - " }}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 0.00\n", - " view 0.00\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Checking for duplicates in the dataset\n", - "house_data_clean[house_data_clean.duplicated()]" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] }, { -"cells": [], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}, \ No newline at end of file + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] +}, +{ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] +}, +{ + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] +}, +{ + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] +}, +{ + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] +}, +{ + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] +}, +{ + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] +}, +{ + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] +}, +{ + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] +}, +{ + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] +}, +{ + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] +}, +{ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] +} +], +"metadata": { +"kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" +}, +"language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" +} +}, +"nbformat": 4, +"nbformat_minor": 2 +} \ No newline at end of file From 100b23763477054240c5bc616607ac3214b4aece Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 11:55:26 +0300 Subject: [PATCH 39/53] Update student.ipynb --- student.ipynb | 1126 ++++++++++++++++++++++++------------------------- 1 file changed, 563 insertions(+), 563 deletions(-) diff --git a/student.ipynb b/student.ipynb index 3789754d..93177751 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -467,566 +467,566 @@ "\n", "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] -}, -{ - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] -}, -{ - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] -}, -{ - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] -}, -{ - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] -}, -{ - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] -}, -{ - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] -}, -{ - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] -}, -{ - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] -}, -{ - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] -}, -{ - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] -}, -{ - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] -}, -{ - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] -} -], -"metadata": { -"kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" -}, -"language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } -}, -"nbformat": 4, -"nbformat_minor": 2 -} \ No newline at end of file From 2353438227239a869e32885e97d7b037bd7e9089 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 12:00:03 +0300 Subject: [PATCH 40/53] Update student.ipynb --- student.ipynb | 1150 +++++++++++++++++++++++++------------------------ 1 file changed, 586 insertions(+), 564 deletions(-) diff --git a/student.ipynb b/student.ipynb index 4a29c2f7..24625738 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -93,9 +93,31 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import Necessary Libraries" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.stats as stats\n", + "import seaborn as sns\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -467,566 +489,566 @@ "\n", "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] -}, -{ - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] -}, -{ - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] -}, -{ - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] -}, -{ - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] -}, -{ - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] -}, -{ - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] -}, -{ - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] -}, -{ - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] -}, -{ - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] -}, -{ - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] -}, -{ - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] -}, -{ - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] -} -], -"metadata": { -"kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" -}, -"language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" -} -}, -"nbformat": 4, -"nbformat_minor": 2 + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From 045ce6159d1527b5878aa4faf6feebcf0c14ebab Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 12:16:48 +0300 Subject: [PATCH 41/53] Update student.ipynb --- student.ipynb | 1152 ++++++++++++++++++++++++------------------------- 1 file changed, 564 insertions(+), 588 deletions(-) diff --git a/student.ipynb b/student.ipynb index 6216aa63..4a29c2f7 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -91,35 +91,11 @@ "source": [ "## Data Loading\n", "\n" - Clyde - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import Necessary Libraries" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm" - - ] - }, - { - "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -491,566 +467,566 @@ "\n", "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] +}, +{ + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] +}, +{ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] +}, +{ + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] +}, +{ + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] +}, +{ + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] +}, +{ + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] +}, +{ + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] +}, +{ + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] +}, +{ + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] +}, +{ + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] +}, +{ + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] +}, +{ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] +} +], +"metadata": { +"kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" +}, +"language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" +} +}, +"nbformat": 4, +"nbformat_minor": 2 } From 6565f7fb0d4dc372453b91442ef454ca4b66b073 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 12:17:45 +0300 Subject: [PATCH 42/53] Update student.ipynb --- student.ipynb | 1152 ++++++++++++++++++++++++------------------------- 1 file changed, 564 insertions(+), 588 deletions(-) diff --git a/student.ipynb b/student.ipynb index 6216aa63..3789754d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -91,35 +91,11 @@ "source": [ "## Data Loading\n", "\n" - Clyde - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import Necessary Libraries" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm" - - ] - }, - { - "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -491,566 +467,566 @@ "\n", "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] +}, +{ + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] +}, +{ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] +}, +{ + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] +}, +{ + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] +}, +{ + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] +}, +{ + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] +}, +{ + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] +}, +{ + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] +}, +{ + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] +}, +{ + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] +}, +{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] +}, +{ + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] +}, +{ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] +} +], +"metadata": { +"kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" +}, +"language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } +}, +"nbformat": 4, +"nbformat_minor": 2 +} \ No newline at end of file From 89189c4f5e4bd353c9fe4c3db76a4319e4554327 Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 12:22:29 +0300 Subject: [PATCH 43/53] Update student.ipynb --- student.ipynb | 1152 +++++++++++++++++++++++++------------------------ 1 file changed, 588 insertions(+), 564 deletions(-) diff --git a/student.ipynb b/student.ipynb index 4a29c2f7..37939df3 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -93,9 +93,33 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n", + "#### Import Necessary Libraries\n" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.stats as stats\n", + "import seaborn as sns\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -467,566 +491,566 @@ "\n", "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] -}, -{ - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] -}, -{ - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] -}, -{ - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] -}, -{ - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] -}, -{ - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] -}, -{ - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] -}, -{ - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] -}, -{ - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] -}, -{ - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] -}, -{ - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] -}, -{ - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] -}, -{ - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] -} -], -"metadata": { -"kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" -}, -"language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" -} -}, -"nbformat": 4, -"nbformat_minor": 2 + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From d3f256bf27663563c90d26f1e30397590e5f158c Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 12:32:06 +0300 Subject: [PATCH 44/53] Update student.ipynb --- student.ipynb | 1262 +++++++++++++++++-------------------------------- 1 file changed, 422 insertions(+), 840 deletions(-) diff --git a/student.ipynb b/student.ipynb index 3a8e8a47..43c7edaf 100644 --- a/student.ipynb +++ b/student.ipynb @@ -91,36 +91,11 @@ "source": [ "## Data Loading\n", "\n" - - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n", - "#### Import Necessary Libraries\n" - - - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm" ] }, { "cell_type": "code", - "execution_count": 2, - - + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +120,40 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m load_data(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata/kc_house_data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "Cell \u001b[1;32mIn[1], line 3\u001b[0m, in \u001b[0;36mload_data\u001b[1;34m(file_path)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_data\u001b[39m(file_path):\n\u001b[1;32m----> 3\u001b[0m house_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_path)\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m#shape\u001b[39;00m\n\u001b[0;32m 6\u001b[0m shape \u001b[38;5;241m=\u001b[39m house_data\u001b[38;5;241m.\u001b[39mshape\n", + "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined" + ] + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { @@ -178,7 +186,317 @@ "sqft_lot15: int64\n", "\n" ] - }, + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013936\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -210,12 +528,11 @@ " floors\n", " waterfront\n", " view\n", - " ...\n", + " condition\n", " grade\n", " sqft_above\n", " sqft_basement\n", " yr_built\n", - " yr_renovated\n", " zipcode\n", " lat\n", " long\n", @@ -225,833 +542,98 @@ " \n", " \n", " \n", - " 0\n", - " 7129300520\n", - " 10/13/2014\n", - " 221900.0\n", - " 3\n", - " 1.00\n", - " 1180\n", - " 5650\n", - " 1.0\n", - " NaN\n", - " NONE\n", - " ...\n", - " 7 Average\n", - " 1180\n", - " 0.0\n", - " 1955\n", - " 0.0\n", - " 98178\n", - " 47.5112\n", - " -122.257\n", - " 1340\n", - " 5650\n", - " \n", - " \n", - " 1\n", - " 6414100192\n", - " 12/9/2014\n", - " 538000.0\n", - " 3\n", - " 2.25\n", - " 2570\n", - " 7242\n", - " 2.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 7 Average\n", - " 2170\n", - " 400.0\n", - " 1951\n", - " 1991.0\n", - " 98125\n", - " 47.7210\n", - " -122.319\n", - " 1690\n", - " 7639\n", - " \n", - " \n", - " 2\n", - " 5631500400\n", - " 2/25/2015\n", - " 180000.0\n", - " 2\n", - " 1.00\n", - " 770\n", - " 10000\n", - " 1.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 6 Low Average\n", - " 770\n", - " 0.0\n", - " 1933\n", - " NaN\n", - " 98028\n", - " 47.7379\n", - " -122.233\n", - " 2720\n", - " 8062\n", - " \n", - " \n", - " 3\n", - " 2487200875\n", - " 12/9/2014\n", - " 604000.0\n", + " 3947\n", + " 1825069031\n", + " 2014\n", + " 550000.0\n", " 4\n", - " 3.00\n", - " 1960\n", - " 5000\n", - " 1.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 7 Average\n", - " 1050\n", - " 910.0\n", - " 1965\n", - " 0.0\n", - " 98136\n", - " 47.5208\n", - " -122.393\n", - " 1360\n", - " 5000\n", - " \n", - " \n", - " 4\n", - " 1954400510\n", - " 2/18/2015\n", - " 510000.0\n", - " 3\n", - " 2.00\n", - " 1680\n", - " 8080\n", - " 1.0\n", + " 1.75\n", + " 2410\n", + " 8447\n", + " 2.0\n", " NO\n", - " NONE\n", - " ...\n", + " GOOD\n", + " Good\n", " 8 Good\n", - " 1680\n", - " 0.0\n", - " 1987\n", - " 0.0\n", + " 2060\n", + " 350.0\n", + " 1936\n", " 98074\n", - " 47.6168\n", - " -122.045\n", - " 1800\n", - " 7503\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 47.6499\n", + " -122.088\n", + " 2520\n", + " 14789\n", " \n", " \n", - " 21592\n", - " 263000018\n", - " 5/21/2014\n", - " 360000.0\n", + " 20038\n", + " 8648900110\n", + " 2014\n", + " 555000.0\n", " 3\n", " 2.50\n", - " 1530\n", - " 1131\n", - " 3.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 8 Good\n", - " 1530\n", - " 0.0\n", - " 2009\n", - " 0.0\n", - " 98103\n", - " 47.6993\n", - " -122.346\n", - " 1530\n", - " 1509\n", - " \n", - " \n", - " 21593\n", - " 6600060120\n", - " 2/23/2015\n", - " 400000.0\n", - " 4\n", - " 2.50\n", - " 2310\n", - " 5813\n", + " 1940\n", + " 3211\n", " 2.0\n", " NO\n", " NONE\n", - " ...\n", + " Average\n", " 8 Good\n", - " 2310\n", - " 0.0\n", - " 2014\n", - " 0.0\n", - " 98146\n", - " 47.5107\n", - " -122.362\n", - " 1830\n", - " 7200\n", - " \n", - " \n", - " 21594\n", - " 1523300141\n", - " 6/23/2014\n", - " 402101.0\n", - " 2\n", - " 0.75\n", - " 1020\n", - " 1350\n", - " 2.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 7 Average\n", - " 1020\n", + " 1940\n", " 0.0\n", " 2009\n", - " 0.0\n", - " 98144\n", - " 47.5944\n", - " -122.299\n", - " 1020\n", - " 2007\n", - " \n", - " \n", - " 21595\n", - " 291310100\n", - " 1/16/2015\n", - " 400000.0\n", - " 3\n", - " 2.50\n", - " 1600\n", - " 2388\n", - " 2.0\n", - " NaN\n", - " NONE\n", - " ...\n", - " 8 Good\n", - " 1600\n", - " 0.0\n", - " 2004\n", - " 0.0\n", " 98027\n", - " 47.5345\n", - " -122.069\n", - " 1410\n", - " 1287\n", - " \n", - " \n", - " 21596\n", - " 1523300157\n", - " 10/15/2014\n", - " 325000.0\n", - " 2\n", - " 0.75\n", - " 1020\n", - " 1076\n", - " 2.0\n", - " NO\n", - " NONE\n", - " ...\n", - " 7 Average\n", - " 1020\n", - " 0.0\n", - " 2008\n", - " 0.0\n", - " 98144\n", - " 47.5941\n", - " -122.299\n", - " 1020\n", - " 1357\n", + " 47.5644\n", + " -122.093\n", + " 1880\n", + " 3078\n", " \n", " \n", "\n", - "

21597 rows × 21 columns

\n", "" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] -}, -{ - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] -}, -{ - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] -}, -{ - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] -}, -{ - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] -}, -{ - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] -}, -{ - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] -}, -{ - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] -}, -{ - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] -}, -{ - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] -}, -{ - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] -}, -{ - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] -}, -{ - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] -}, -{ - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] -} -], -"metadata": { -"kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" -}, -"language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } -}, -"nbformat": 4, -"nbformat_minor": 2 -} \ No newline at end of file From 192c53b79b199bee99e9e950b23b19b2f362a4ae Mon Sep 17 00:00:00 2001 From: clydeochieng <107258512+clydeochieng@users.noreply.github.com> Date: Wed, 1 May 2024 13:03:00 +0300 Subject: [PATCH 45/53] added libraries --- student.ipynb | 458 +++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 434 insertions(+), 24 deletions(-) diff --git a/student.ipynb b/student.ipynb index 43c7edaf..e8f51ac4 100644 --- a/student.ipynb +++ b/student.ipynb @@ -6,7 +6,7 @@ "source": [ "## Final Project Submission\n", "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng. \n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", "* Student pace: full time\n", "* Scheduled project review date/time: \n", "* Instructor name: Nikita \n", @@ -90,6 +90,8 @@ "metadata": {}, "source": [ "## Data Loading\n", + "\n", + "#### Import Necessary Libraries\n", "\n" ] }, @@ -98,6 +100,21 @@ "execution_count": 1, "metadata": {}, "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.stats as stats\n", + "import seaborn as sns\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "# Creating a function that loads data and return it in a dataframe\n", "def load_data(file_path):\n", @@ -120,20 +137,413 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'pd' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m load_data(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata/kc_house_data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "Cell \u001b[1;32mIn[1], line 3\u001b[0m, in \u001b[0;36mload_data\u001b[1;34m(file_path)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_data\u001b[39m(file_path):\n\u001b[1;32m----> 3\u001b[0m house_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_path)\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m#shape\u001b[39;00m\n\u001b[0;32m 6\u001b[0m shape \u001b[38;5;241m=\u001b[39m house_data\u001b[38;5;241m.\u001b[39mshape\n", - "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" ] + }, + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontview...gradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538000.032.25257072422.0NONONE...7 Average2170400.019511991.09812547.7210-122.31916907639
256315004002/25/2015180000.021.00770100001.0NONONE...6 Low Average7700.01933NaN9802847.7379-122.23327208062
3248720087512/9/2014604000.043.00196050001.0NONONE...7 Average1050910.019650.09813647.5208-122.39313605000
419544005102/18/2015510000.032.00168080801.0NONONE...8 Good16800.019870.09807447.6168-122.04518007503
..................................................................
215922630000185/21/2014360000.032.50153011313.0NONONE...8 Good15300.020090.09810347.6993-122.34615301509
2159366000601202/23/2015400000.042.50231058132.0NONONE...8 Good23100.020140.09814647.5107-122.36218307200
2159415233001416/23/2014402101.020.75102013502.0NONONE...7 Average10200.020090.09814447.5944-122.29910202007
215952913101001/16/2015400000.032.50160023882.0NaNNONE...8 Good16000.020040.09802747.5345-122.06914101287
21596152330015710/15/2014325000.020.75102010762.0NONONE...7 Average10200.020080.09814447.5941-122.29910201357
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21597 rows × 21 columns

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" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -153,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -194,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -215,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -259,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -303,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -321,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": null, + 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"sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", vmin=-1, vmax=1)\n", + "plt.title('Heatmap of Correlation Matrix for Numeric Columns')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect positive correlation (1): Variables increase together perfectly.\n", + "High positive correlation (0.7 to 1): Variables mostly move in the same direction strongly.\n", + "Moderate positive correlation (0.3 to 0.7): Variables tend to move together moderately.\n", + "Weak positive correlation (0 to 0.3): Variables show a weak, inconsistent relationship.\n", + "No correlation (0): Variables are independent of each other.\n", + "Weak negative correlation (-0.3 to 0): Weak, inconsistent negative relationship.\n", + "Moderate negative correlation (-0.7 to -0.3): Moderate negative relationship.\n", + "High negative correlation (-1 to -0.7): Variables move strongly in opposite directions.\n", + "Perfect negative correlation (-1): Variables decrease together perfectly." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean.drop(columns=['date', 'sqft_lot', 'condition', 'zipcode', 'long', 'sqft_lot15', 'yr_built', 'lat'], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analysis for categorical columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet visualizes the relationship between the 'waterfront' feature and the average sale price. A bar plot is used to show the average price for properties with and without waterfront. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Categorical = ['waterfront', 'condition', 'grade'] \n", + "\n", + "# How waterfront relates to saleprice\n", + "# plot the barplot\n", + "plt.figure(figsize = (7,5))\n", + "kings_data.groupby('waterfront')['price'].mean().plot.bar()\n", + "\n", + "# set the axes and title\n", + "plt.xlabel(column)\n", + "plt.ylabel('Average price')\n", + "plt.title('Waterfont vs Sales')\n", + "\n", + "# display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above clearly shows that houses with waterfronts are the most popular and sells the most" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet visualizes the relationship between the 'condition' feature and the average sale price. A bar plot is used to show the average price for properties with different conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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l2rBhg06cOKH+/furuLjYVhMWFqbU1FQlJCQoISFBqampCg8Pt20vLi5Wv379dPLkSW3YsEHLli3TJ598onHjxtlq8vPzFRoaKqvVqq1bt2rWrFmaPn264uLiKveLAgAAqGQmw4HTXE8//bQ2btyo77777oLbDcOQ1WpVdHS0nnrqKUl/zgr6+Pjo1Vdf1SOPPKK8vDzVq1dPH3zwge69915JUmZmpvz8/PTll1+qV69e2r17twICApScnKyOHTtKkpKTkxUcHKyff/5Z/v7++uqrr9S/f38dOnRIVqtVkrRs2TJFREQoJydHHh4eeueddzRx4kQdPnxYZrNZkjRt2jTNmjVLGRkZMplMl/3M+fn5slgsysvLk4eHx9/+DgEA+Cdj9fHfU5bc4dCZwpUrV6p9+/a655575O3trXbt2mnu3Lm27QcOHFB2drZ69uxpGzObzQoJCdGmTZskSdu3b1dRUZFdjdVqVWBgoK1m8+bNslgstkAoSZ06dZLFYrGrCQwMtAVCSerVq5cKCgpsp7M3b96skJAQWyA8V5OZmamDBw9e8DMWFBQoPz/f7gUAAFDdODQU/vLLL3rnnXfUokULff311/rXv/6lqKgovf/++5Kk7OxsSZKPj4/d3/n4+Ni2ZWdny9nZWZ6enpes8fb2LnV8b29vu5rzj+Pp6SlnZ+dL1px7f67mfFOnTrVdx2ixWOTn53eZbwUAAKDqOTQUlpSU6JZbbtGUKVPUrl07PfLIIxo9erTeeecdu7rzT8sahnHZU7Xn11yoviJqzp19v1g/EydOVF5enu116NChS/YNAADgCA4NhfXr11dAQIDdWMuWLZWeni5J8vX1lVR6Fi4nJ8c2Q+fr66vCwkLl5uZesubw4cOljn/kyBG7mvOPk5ubq6KiokvW5OTkSCo9m3mO2WyWh4eH3QsAAKC6cWgo7NKli/bs2WM3tnfvXjVu3FiS1LRpU/n6+iopKcm2vbCwUOvWrVPnzp0lSUFBQapVq5ZdTVZWltLS0mw1wcHBysvL0/fff2+r2bJli/Ly8uxq0tLSlJWVZatJTEyU2WxWUFCQrWb9+vV2t6lJTEyU1WpVkyZNKuIrAQAAcAiHhsInnnhCycnJmjJliv73v/9pyZIlmjNnjsaMGSPpz1Oy0dHRmjJlipYvX660tDRFRETI1dVVYWFhkiSLxaJRo0Zp3LhxWrNmjVJSUjR8+HC1bt1aPXr0kPTn7GPv3r01evRoJScnKzk5WaNHj1b//v3l7+8vSerZs6cCAgIUHh6ulJQUrVmzRuPHj9fo0aNts3thYWEym82KiIhQWlqali9frilTpigmJuaKVh4DAABUVzUdefAOHTpo+fLlmjhxoiZPnqymTZtq5syZGjZsmK1mwoQJOn36tCIjI5Wbm6uOHTsqMTFR7u7utprXX39dNWvW1NChQ3X69Gl1795d8fHxcnJystUsXrxYUVFRtlXKAwcO1OzZs23bnZyctGrVKkVGRqpLly5ycXFRWFiYpk+fbquxWCxKSkrSmDFj1L59e3l6eiomJkYxMTGV+TUBAABUOofep/BaxH0KAQC4ctyn8O+5au5TCAAAgOqBUAgAAABCIQAAAAiFAAAAEKEQAAAAIhQCAABAhEIAAACIUAgAAAARCgEAACBCIQAAAEQoBAAAgAiFAAAAEKEQAAAAIhQCAABAhEIAAACIUAgAAAARCgEAACBCIQAAAEQoBAAAgAiFAAAAEKEQAAAAIhQCAABAhEIAAACIUAgAAAARCgEAACBCIQAAAEQoBAAAgKSajm4AlavJ06sc3UKlODitn6NbAADgH4WZQgAAABAKAQAAQCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAACQVNPRDQDA1azJ06sc3UKlODitn6NbAFDFmCkEAAAAoRAAAACEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAACQg0PhpEmTZDKZ7F6+vr627YZhaNKkSbJarXJxcVHXrl21c+dOu30UFBRo7Nix8vLykpubmwYOHKiMjAy7mtzcXIWHh8tischisSg8PFzHjh2zq0lPT9eAAQPk5uYmLy8vRUVFqbCw0K5mx44dCgkJkYuLixo0aKDJkyfLMIyK/VIAAAAcwOEzha1atVJWVpbttWPHDtu22NhYxcXFafbs2dq6dat8fX0VGhqq48eP22qio6O1fPlyLVu2TBs2bNCJEyfUv39/FRcX22rCwsKUmpqqhIQEJSQkKDU1VeHh4bbtxcXF6tevn06ePKkNGzZo2bJl+uSTTzRu3DhbTX5+vkJDQ2W1WrV161bNmjVL06dPV1xcXCV/QwAAAJXP4Y+5q1mzpt3s4DmGYWjmzJl69tlnNWTIEEnSwoUL5ePjoyVLluiRRx5RXl6e5s2bpw8++EA9evSQJC1atEh+fn5avXq1evXqpd27dyshIUHJycnq2LGjJGnu3LkKDg7Wnj175O/vr8TERO3atUuHDh2S1WqVJM2YMUMRERF65ZVX5OHhocWLF+vMmTOKj4+X2WxWYGCg9u7dq7i4OMXExMhkMl3w8xUUFKigoMD2Pj8/v0K/PwAAgIrg8JnCffv2yWq1qmnTprrvvvv0yy+/SJIOHDig7Oxs9ezZ01ZrNpsVEhKiTZs2SZK2b9+uoqIiuxqr1arAwEBbzebNm2WxWGyBUJI6deoki8ViVxMYGGgLhJLUq1cvFRQUaPv27baakJAQmc1mu5rMzEwdPHjwop9v6tSpttPWFotFfn5+5f2qAAAAKo1DQ2HHjh31/vvv6+uvv9bcuXOVnZ2tzp07648//lB2drYkycfHx+5vfHx8bNuys7Pl7OwsT0/PS9Z4e3uXOra3t7ddzfnH8fT0lLOz8yVrzr0/V3MhEydOVF5enu116NChS38pAAAADuDQ08d9+vSx/Xfr1q0VHBysZs2aaeHCherUqZMklTotaxjGRU/VXqzmQvUVUXNukcml+jGbzXaziwAAANWRw08f/5Wbm5tat26tffv22a4zPH8WLicnxzZD5+vrq8LCQuXm5l6y5vDhw6WOdeTIEbua84+Tm5uroqKiS9bk5ORIKj2bCQAAcLWpVqGwoKBAu3fvVv369dW0aVP5+voqKSnJtr2wsFDr1q1T586dJUlBQUGqVauWXU1WVpbS0tJsNcHBwcrLy9P3339vq9myZYvy8vLsatLS0pSVlWWrSUxMlNlsVlBQkK1m/fr1drepSUxMlNVqVZMmTSr+ywAAAKhCDg2F48eP17p163TgwAFt2bJF//d//6f8/HyNGDFCJpNJ0dHRmjJlipYvX660tDRFRETI1dVVYWFhkiSLxaJRo0Zp3LhxWrNmjVJSUjR8+HC1bt3athq5ZcuW6t27t0aPHq3k5GQlJydr9OjR6t+/v/z9/SVJPXv2VEBAgMLDw5WSkqI1a9Zo/PjxGj16tDw8PCT9eVsbs9msiIgIpaWlafny5ZoyZcolVx4DAABcLRx6TWFGRobuv/9+/f7776pXr546deqk5ORkNW7cWJI0YcIEnT59WpGRkcrNzVXHjh2VmJgod3d32z5ef/111axZU0OHDtXp06fVvXt3xcfHy8nJyVazePFiRUVF2VYpDxw4ULNnz7Ztd3Jy0qpVqxQZGakuXbrIxcVFYWFhmj59uq3GYrEoKSlJY8aMUfv27eXp6amYmBjFxMRU9tcEAABQ6UwGj+SoUvn5+bJYLMrLy7PNQlamJk+vqvRjOMLBaf0c3QIgiX9jQGXj39jfU5bcUa2uKQQAAIBjEAoBAABAKAQAAAChEAAAACIUAgAAQIRCAAAAiFAIAAAAEQoBAAAgQiEAAABEKAQAAIAIhQAAABChEAAAACIUAgAAQIRCAAAAiFAIAAAAEQoBAAAgQiEAAABEKAQAAIAIhQAAABChEAAAACIUAgAAQIRCAAAAiFAIAAAAEQoBAAAgQiEAAABEKAQAAIAIhQAAABChEAAAACIUAgAAQIRCAAAAiFAIAAAAEQoBAAAgQiEAAABEKAQAAIAIhQAAABChEAAAACIUAgAAQIRCAAAAiFAIAAAAEQoBAAAgQiEAAABEKAQAAIAIhQAAABChEAAAACIUAgAAQIRCAAAAiFAIAAAAEQoBAACgahQKp06dKpPJpOjoaNuYYRiaNGmSrFarXFxc1LVrV+3cudPu7woKCjR27Fh5eXnJzc1NAwcOVEZGhl1Nbm6uwsPDZbFYZLFYFB4ermPHjtnVpKena8CAAXJzc5OXl5eioqJUWFhoV7Njxw6FhITIxcVFDRo00OTJk2UYRoV+DwAAAI5QLULh1q1bNWfOHLVp08ZuPDY2VnFxcZo9e7a2bt0qX19fhYaG6vjx47aa6OhoLV++XMuWLdOGDRt04sQJ9e/fX8XFxbaasLAwpaamKiEhQQkJCUpNTVV4eLhte3Fxsfr166eTJ09qw4YNWrZsmT755BONGzfOVpOfn6/Q0FBZrVZt3bpVs2bN0vTp0xUXF1eJ3wwAAEDVqOnoBk6cOKFhw4Zp7ty5evnll23jhmFo5syZevbZZzVkyBBJ0sKFC+Xj46MlS5bokUceUV5enubNm6cPPvhAPXr0kCQtWrRIfn5+Wr16tXr16qXdu3crISFBycnJ6tixoyRp7ty5Cg4O1p49e+Tv76/ExETt2rVLhw4dktVqlSTNmDFDEREReuWVV+Th4aHFixfrzJkzio+Pl9lsVmBgoPbu3au4uDjFxMTIZDJV8TcHAABQcRw+UzhmzBj169fPFurOOXDggLKzs9WzZ0/bmNlsVkhIiDZt2iRJ2r59u4qKiuxqrFarAgMDbTWbN2+WxWKxBUJJ6tSpkywWi11NYGCgLRBKUq9evVRQUKDt27fbakJCQmQ2m+1qMjMzdfDgwYt+voKCAuXn59u9AAAAqhuHhsJly5bphx9+0NSpU0tty87OliT5+PjYjfv4+Ni2ZWdny9nZWZ6enpes8fb2LrV/b29vu5rzj+Pp6SlnZ+dL1px7f67mQqZOnWq7ltFiscjPz++itQAAAI5SrlB49uxZrV69Wu+9957t+r7MzEydOHHiivdx6NAhPf7441q0aJFq16590brzT8sahnHZU7Xn11yoviJqzi0yuVQ/EydOVF5enu116NChS/YOAADgCGW+pvDXX39V7969lZ6eroKCAoWGhsrd3V2xsbE6c+aM3n333Svaz/bt25WTk6OgoCDbWHFxsdavX6/Zs2drz549kv6chatfv76tJicnxzZD5+vrq8LCQuXm5trNFubk5Khz5862msOHD5c6/pEjR+z2s2XLFrvtubm5Kioqsqs5f0YwJydHUunZzL8ym812p5wBAACqozLPFD7++ONq3769cnNz5eLiYhsfPHiw1qxZc8X76d69u3bs2KHU1FTbq3379ho2bJhSU1N1ww03yNfXV0lJSba/KSws1Lp162yBLygoSLVq1bKrycrKUlpamq0mODhYeXl5+v777201W7ZsUV5enl1NWlqasrKybDWJiYkym8220BocHKz169fb3aYmMTFRVqtVTZo0ueLPDQAAUB2VeaZww4YN2rhxo5ydne3GGzdurN9+++2K9+Pu7q7AwEC7MTc3N9WtW9c2Hh0drSlTpqhFixZq0aKFpkyZIldXV4WFhUmSLBaLRo0apXHjxqlu3bqqU6eOxo8fr9atW9sWrrRs2VK9e/fW6NGj9d5770mSHn74YfXv31/+/v6SpJ49eyogIEDh4eF67bXXdPToUY0fP16jR4+Wh4eHpD9va/PSSy8pIiJCzzzzjPbt26cpU6bohRdeYOUxAAC46pU5FJaUlNjdA/CcjIwMubu7V0hT50yYMEGnT59WZGSkcnNz1bFjRyUmJtod5/XXX1fNmjU1dOhQnT59Wt27d1d8fLycnJxsNYsXL1ZUVJRtlfLAgQM1e/Zs23YnJyetWrVKkZGR6tKli1xcXBQWFqbp06fbaiwWi5KSkjRmzBi1b99enp6eiomJUUxMTIV+ZgAAAEcwGWV8JMe9994ri8WiOXPmyN3dXT/99JPq1aunQYMGqVGjRlqwYEFl9fqPkJ+fL4vFory8PNssZGVq8vSqSj+GIxyc1s/RLQCS+DcGVDb+jf09ZckdZZ4pfP3119WtWzcFBATozJkzCgsL0759++Tl5aWlS5eWu2kAAAA4TplDodVqVWpqqpYtW6bt27erpKREo0aN0rBhw+wWngAAAODqUa7H3Lm4uOjBBx/Ugw8+WNH9AAAAwAHKfEuaqVOnav78+aXG58+fr1dffbVCmgIAAEDVKnMofO+993TTTTeVGm/VqtUV37gaAAAA1UuZQ+H5Txg5p169enY3fwYAAMDVo8yh0M/PTxs3biw1vnHjRlmt1gppCgAAAFWrzAtNHnroIUVHR6uoqEh33nmnJGnNmjWaMGGCxo0bV+ENAgAAoPKVORROmDBBR48eVWRkpO05wLVr19ZTTz2liRMnVniDAAAAqHxlDoUmk0mvvvqqnn/+ee3evVsuLi5q0aKFzGZzZfQHAACAKlCu+xRK0nXXXacOHTpUZC8AAABwkCsKhUOGDFF8fLw8PDw0ZMiQS9Z++umnFdIYAAAAqs4VhUKLxSKTyWT7bwAAAPyzXFEoXLBggSTJMAxNmjRJ9erVk6ura6U2BgAAgKpTpvsUGoahFi1a6LfffqusfgAAAOAAZQqFNWrUUIsWLfTHH39UVj8AAABwgDI/0SQ2NlZPPvmk0tLSKqMfAAAAOECZb0kzfPhwnTp1SjfffLOcnZ3l4uJit/3o0aMV1hwAAACqRplD4cyZMyuhDQAAADhSmUPhiBEjKqMPAAAAOFC5nmhSXFys5cuXa/fu3TKZTGrZsqUGDRqkmjXL/YAUAAAAOFCZU1xaWpoGDRqk7Oxs+fv7S5L27t2revXqaeXKlWrdunWFNwkAAIDKVebVxw899JBatWqljIwM/fDDD/rhhx906NAhtWnTRg8//HBl9AgAAIBKVuaZwh9//FHbtm2Tp6enbczT01OvvPKKOnToUKHNAQAAoGqUeabQ399fhw8fLjWek5Oj5s2bV0hTAAAAqFplDoVTpkxRVFSUPv74Y2VkZCgjI0Mff/yxoqOj9eqrryo/P9/2AgAAwNWhzKeP+/fvL0kaOnSoTCaTpD+fiSxJAwYMsL03mUwqLi6uqD4BAABQicocCr/99tvK6AMAAAAOVOZQGBISUhl9AAAAwIG42zQA4JrR5OlVjm6hUhyc1s/RLeAfoMwLTQAAAPDPQygEAAAAoRAAAADlDIVnz57V6tWr9d577+n48eOSpMzMTJ04caJCmwMAAEDVKPNCk19//VW9e/dWenq6CgoKFBoaKnd3d8XGxurMmTN69913K6NPAAAAVKIyzxQ+/vjjat++vXJzc+Xi4mIbHzx4sNasWVOhzQEAAKBqlHmmcMOGDdq4caOcnZ3txhs3bqzffvutwhoDrkXcLgMA4ChlniksKSm54OPrMjIy5O7uXiFNAQAAoGqVORSGhoZq5syZtvcmk0knTpzQiy++qL59+1ZkbwAAAKgiZT59/Prrr6tbt24KCAjQmTNnFBYWpn379snLy0tLly6tjB4BAABQycocCq1Wq1JTU7V06VL98MMPKikp0ahRozRs2DC7hScAAAC4epTr2ccuLi4aOXKkRo4cWdH9AAAAwAHKHApXrlx5wXGTyaTatWurefPmatq06d9uDAAAAFWnzKHwrrvukslkkmEYduPnxkwmk2677TatWLFCnp6eFdYoAAAAKk+ZVx8nJSWpQ4cOSkpKUl5envLy8pSUlKRbb71VX3zxhdavX68//vhD48ePr4x+AQAAUAnKPFP4+OOPa86cOercubNtrHv37qpdu7Yefvhh7dy5UzNnzuR6QwAAgKtImWcK9+/fLw8Pj1LjHh4e+uWXXyRJLVq00O+///73uwMAAECVKHMoDAoK0pNPPqkjR47Yxo4cOaIJEyaoQ4cOkqR9+/apYcOGFdclAAAAKlWZTx/PmzdPgwYNUsOGDeXn5yeTyaT09HTdcMMN+uyzzyRJJ06c0PPPP1/hzQIAAKBylHmm0N/fX7t379Znn32mqKgoPfbYY1q5cqV27typG2+8UdKfK5TDw8Mvu6933nlHbdq0kYeHhzw8PBQcHKyvvvrKtt0wDE2aNElWq1UuLi7q2rWrdu7cabePgoICjR07Vl5eXnJzc9PAgQOVkZFhV5Obm6vw8HBZLBZZLBaFh4fr2LFjdjXp6ekaMGCA3Nzc5OXlpaioKBUWFtrV7NixQyEhIXJxcVGDBg00efLkUquwAQAArkblunm1yWRS79691bt377918IYNG2ratGlq3ry5JGnhwoUaNGiQUlJS1KpVK8XGxiouLk7x8fG68cYb9fLLLys0NFR79uyRu7u7JCk6Olqff/65li1bprp162rcuHHq37+/tm/fLicnJ0lSWFiYMjIylJCQIEl6+OGHFR4ers8//1ySVFxcrH79+qlevXrasGGD/vjjD40YMUKGYWjWrFmSpPz8fIWGhqpbt27aunWr9u7dq4iICLm5uWncuHF/63sAAABwtHKFwpMnT2rdunVKT08vNZsWFRV1xfsZMGCA3ftXXnlF77zzjpKTkxUQEKCZM2fq2Wef1ZAhQyT9GRp9fHy0ZMkSPfLII8rLy9O8efP0wQcfqEePHpKkRYsWyc/PT6tXr1avXr20e/duJSQkKDk5WR07dpQkzZ07V8HBwdqzZ4/8/f2VmJioXbt26dChQ7JarZKkGTNmKCIiQq+88oo8PDy0ePFinTlzRvHx8TKbzQoMDNTevXsVFxenmJgYmUym8nyVAAAA1UKZQ2FKSor69u2rU6dO6eTJk6pTp45+//13ubq6ytvbu0yh8K+Ki4v13//+VydPnlRwcLAOHDig7Oxs9ezZ01ZjNpsVEhKiTZs26ZFHHtH27dtVVFRkV2O1WhUYGKhNmzapV69e2rx5sywWiy0QSlKnTp1ksVi0adMm+fv7a/PmzQoMDLQFQknq1auXCgoKtH37dnXr1k2bN29WSEiIzGazXc3EiRN18ODBiz7FpaCgQAUFBbb3+fn55fp+AAAAKlOZryl84oknNGDAAB09elQuLi5KTk7Wr7/+qqCgIE2fPr3MDezYsUPXXXedzGaz/vWvf2n58uUKCAhQdna2JMnHx8eu3sfHx7YtOztbzs7OpZ6ccn6Nt7d3qeN6e3vb1Zx/HE9PTzk7O1+y5tz7czUXMnXqVNu1jBaLRX5+fpf+QgAAABygzKEwNTVV48aNk5OTk5ycnFRQUCA/Pz/FxsbqmWeeKXMD/v7+Sk1NVXJysh599FGNGDFCu3btsm0//7TsuUfpXcr5NReqr4iac4tMLtXPxIkTbU9+ycvL06FDhy7ZOwAAgCOUORTWqlXLFoJ8fHyUnp4uSbJYLLb/LgtnZ2c1b95c7du319SpU3XzzTfrjTfekK+vr6TSs3A5OTm2GTpfX18VFhYqNzf3kjWHDx8uddwjR47Y1Zx/nNzcXBUVFV2yJicnx/Y9XIzZbLatrj73AgAAqG7KHArbtWunbdu2SZK6deumF154QYsXL1Z0dLRat279txsyDEMFBQVq2rSpfH19lZSUZNtWWFiodevW2R6xFxQUpFq1atnVZGVlKS0tzVYTHBysvLw8ff/997aaLVu2KC8vz64mLS1NWVlZtprExESZzWYFBQXZatavX2+3sCYxMVFWq1VNmjT5258bAADAkcocCqdMmaL69etLkv7973+rbt26evTRR5WTk6M5c+aUaV/PPPOMvvvuOx08eFA7duzQs88+q7Vr12rYsGEymUyKjo7WlClTtHz5cqWlpSkiIkKurq4KCwuT9Ofs5KhRozRu3DitWbNGKSkpGj58uFq3bm1bjdyyZUv17t1bo0ePVnJyspKTkzV69Gj1799f/v7+kqSePXsqICBA4eHhSklJ0Zo1azR+/HiNHj3aNrMXFhYms9msiIgIpaWlafny5ZoyZQorjwEAwD9CmVYfG4ahevXqqVWrVpKkevXq6csvvyz3wQ8fPqzw8HBlZWXJYrGoTZs2SkhIUGhoqCRpwoQJOn36tCIjI5Wbm6uOHTsqMTHRdo9CSXr99ddVs2ZNDR06VKdPn1b37t0VHx9vu0ehJC1evFhRUVG2VcoDBw7U7NmzbdudnJy0atUqRUZGqkuXLnJxcVFYWJjdwhmLxaKkpCSNGTNG7du3l6enp2JiYhQTE1Puzw8AAFBdmIwyPJKjpKREtWvX1s6dO9WiRYvK7OsfKz8/XxaLRXl5eVVyfWGTp1dV+jEc4eC0fo5uoVLwe119+M2uLvxeVx9+s7+nLLmjTKePa9SooRYtWuiPP/74Ww0CAACgeinzNYWxsbF68sknlZaWVhn9AAAAwAHK/EST4cOH69SpU7r55pvl7OwsFxcXu+1Hjx6tsOYAAABQNcocCmfOnFkJbQAAAMCRyhwKR4wYURl9AAAAwIHKfE2hJO3fv1/PPfec7r//fttTPRISErRz584KbQ4AAABVo8yhcN26dWrdurW2bNmiTz/9VCdOnJAk/fTTT3rxxRcrvEEAAABUvjKHwqefflovv/yykpKS5OzsbBvv1q2bNm/eXKHNAQAAoGqUORTu2LFDgwcPLjVer1497l8IAABwlSpzKLz++uuVlZVVajwlJUUNGjSokKYAAABQtcocCsPCwvTUU08pOztbJpNJJSUl2rhxo8aPH68HHnigMnoEAABAJStzKHzllVfUqFEjNWjQQCdOnFBAQIDuuOMOde7cWc8991xl9AgAAIBKVub7FNaqVUuLFy/W5MmTlZKSopKSErVr104tWrSojP4AAABQBcocCtetW6eQkBA1a9ZMzZo1q4yeAAAAUMXKfPo4NDRUjRo10tNPP620tLTK6AkAAABVrMyhMDMzUxMmTNB3332nNm3aqE2bNoqNjVVGRkZl9AcAAIAqUOZQ6OXlpccee0wbN27U/v37de+99+r9999XkyZNdOedd1ZGjwAAAKhk5Xr28TlNmzbV008/rWnTpql169Zat25dRfUFAACAKlTuULhx40ZFRkaqfv36CgsLU6tWrfTFF19UZG8AAACoImVeffzMM89o6dKlyszMVI8ePTRz5kzdddddcnV1rYz+AAAAUAXKHArXrl2r8ePH695775WXl5fdttTUVLVt27aiegMAAEAVKXMo3LRpk937vLw8LV68WP/5z3/0448/qri4uMKaAwAAQNUo9zWF33zzjYYPH6769etr1qxZ6tu3r7Zt21aRvQEAAKCKlGmmMCMjQ/Hx8Zo/f75OnjypoUOHqqioSJ988okCAgIqq0cAAABUsiueKezbt68CAgK0a9cuzZo1S5mZmZo1a1Zl9gYAAIAqcsUzhYmJiYqKitKjjz6qFi1aVGZPAAAAqGJXPFP43Xff6fjx42rfvr06duyo2bNn68iRI5XZGwAAAKrIFYfC4OBgzZ07V1lZWXrkkUe0bNkyNWjQQCUlJUpKStLx48crs08AAABUojKvPnZ1ddXIkSO1YcMG7dixQ+PGjdO0adPk7e2tgQMHVkaPAAAAqGR/69nH/v7+io2NVUZGhpYuXVpRPQEAAKCK/a1QeI6Tk5PuuusurVy5siJ2BwAAgCpWIaEQAAAAVzdCIQAAAAiFAAAAIBQCAABAhEIAAACIUAgAAAARCgEAACBCIQAAAEQoBAAAgAiFAAAAEKEQAAAAIhQCAABAhEIAAACIUAgAAAARCgEAACBCIQAAAEQoBAAAgAiFAAAAkIND4dSpU9WhQwe5u7vL29tbd911l/bs2WNXYxiGJk2aJKvVKhcXF3Xt2lU7d+60qykoKNDYsWPl5eUlNzc3DRw4UBkZGXY1ubm5Cg8Pl8VikcViUXh4uI4dO2ZXk56ergEDBsjNzU1eXl6KiopSYWGhXc2OHTsUEhIiFxcXNWjQQJMnT5ZhGBX3pQAAADiAQ0PhunXrNGbMGCUnJyspKUlnz55Vz549dfLkSVtNbGys4uLiNHv2bG3dulW+vr4KDQ3V8ePHbTXR0dFavny5li1bpg0bNujEiRPq37+/iouLbTVhYWFKTU1VQkKCEhISlJqaqvDwcNv24uJi9evXTydPntSGDRu0bNkyffLJJxo3bpytJj8/X6GhobJardq6datmzZql6dOnKy4urpK/KQAAgMpV05EHT0hIsHu/YMECeXt7a/v27brjjjtkGIZmzpypZ599VkOGDJEkLVy4UD4+PlqyZIkeeeQR5eXlad68efrggw/Uo0cPSdKiRYvk5+en1atXq1evXtq9e7cSEhKUnJysjh07SpLmzp2r4OBg7dmzR/7+/kpMTNSuXbt06NAhWa1WSdKMGTMUERGhV155RR4eHlq8eLHOnDmj+Ph4mc1mBQYGau/evYqLi1NMTIxMJlMVfnsAAAAVp1pdU5iXlydJqlOnjiTpwIEDys7OVs+ePW01ZrNZISEh2rRpkyRp+/btKioqsquxWq0KDAy01WzevFkWi8UWCCWpU6dOslgsdjWBgYG2QChJvXr1UkFBgbZv326rCQkJkdlstqvJzMzUwYMHL/iZCgoKlJ+fb/cCAACobqpNKDQMQzExMbrtttsUGBgoScrOzpYk+fj42NX6+PjYtmVnZ8vZ2Vmenp6XrPH29i51TG9vb7ua84/j6ekpZ2fnS9ace3+u5nxTp061XcdosVjk5+d3mW8CAACg6lWbUPjYY4/pp59+0tKlS0ttO/+0rGEYlz1Ve37NheoroubcIpOL9TNx4kTl5eXZXocOHbpk3wAAAI5QLULh2LFjtXLlSn377bdq2LChbdzX11dS6Vm4nJwc2wydr6+vCgsLlZube8maw4cPlzrukSNH7GrOP05ubq6KioouWZOTkyOp9GzmOWazWR4eHnYvAACA6sahodAwDD322GP69NNP9c0336hp06Z225s2bSpfX18lJSXZxgoLC7Vu3Tp17txZkhQUFKRatWrZ1WRlZSktLc1WExwcrLy8PH3//fe2mi1btigvL8+uJi0tTVlZWbaaxMREmc1mBQUF2WrWr19vd5uaxMREWa1WNWnSpIK+FQAAgKrn0FA4ZswYLVq0SEuWLJG7u7uys7OVnZ2t06dPS/rzlGx0dLSmTJmi5cuXKy0tTREREXJ1dVVYWJgkyWKxaNSoURo3bpzWrFmjlJQUDR8+XK1bt7atRm7ZsqV69+6t0aNHKzk5WcnJyRo9erT69+8vf39/SVLPnj0VEBCg8PBwpaSkaM2aNRo/frxGjx5tm90LCwuT2WxWRESE0tLStHz5ck2ZMoWVxwAA4Krn0FvSvPPOO5Kkrl272o0vWLBAERERkqQJEybo9OnTioyMVG5urjp27KjExES5u7vb6l9//XXVrFlTQ4cO1enTp9W9e3fFx8fLycnJVrN48WJFRUXZVikPHDhQs2fPtm13cnLSqlWrFBkZqS5dusjFxUVhYWGaPn26rcZisSgpKUljxoxR+/bt5enpqZiYGMXExFT0VwMAAFClHBoKr+RJICaTSZMmTdKkSZMuWlO7dm3NmjVLs2bNumhNnTp1tGjRokseq1GjRvriiy8uWdO6dWutX7/+kjUAAABXm2qx0AQAAACORSgEAAAAoRAAAACEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAACQg0Ph+vXrNWDAAFmtVplMJq1YscJuu2EYmjRpkqxWq1xcXNS1a1ft3LnTrqagoEBjx46Vl5eX3NzcNHDgQGVkZNjV5ObmKjw8XBaLRRaLReHh4Tp27JhdTXp6ugYMGCA3Nzd5eXkpKipKhYWFdjU7duxQSEiIXFxc1KBBA02ePFmGYVTY9wEAAOAoDg2FJ0+e1M0336zZs2dfcHtsbKzi4uI0e/Zsbd26Vb6+vgoNDdXx48dtNdHR0Vq+fLmWLVumDRs26MSJE+rfv7+Ki4ttNWFhYUpNTVVCQoISEhKUmpqq8PBw2/bi4mL169dPJ0+e1IYNG7Rs2TJ98sknGjdunK0mPz9foaGhslqt2rp1q2bNmqXp06crLi6uEr4ZAACAqlXTkQfv06eP+vTpc8FthmFo5syZevbZZzVkyBBJ0sKFC+Xj46MlS5bokUceUV5enubNm6cPPvhAPXr0kCQtWrRIfn5+Wr16tXr16qXdu3crISFBycnJ6tixoyRp7ty5Cg4O1p49e+Tv76/ExETt2rVLhw4dktVqlSTNmDFDEREReuWVV+Th4aHFixfrzJkzio+Pl9lsVmBgoPbu3au4uDjFxMTIZDJVwTcGAABQOartNYUHDhxQdna2evbsaRszm80KCQnRpk2bJEnbt29XUVGRXY3ValVgYKCtZvPmzbJYLLZAKEmdOnWSxWKxqwkMDLQFQknq1auXCgoKtH37dltNSEiIzGazXU1mZqYOHjx40c9RUFCg/Px8uxcAAEB1U21DYXZ2tiTJx8fHbtzHx8e2LTs7W87OzvL09Lxkjbe3d6n9e3t729WcfxxPT085Oztfsubc+3M1FzJ16lTbtYwWi0V+fn6X/uAAAAAOUG1D4Tnnn5Y1DOOyp2rPr7lQfUXUnFtkcql+Jk6cqLy8PNvr0KFDl+wdAADAEaptKPT19ZVUehYuJyfHNkPn6+urwsJC5ebmXrLm8OHDpfZ/5MgRu5rzj5Obm6uioqJL1uTk5EgqPZv5V2azWR4eHnYvAACA6qbahsKmTZvK19dXSUlJtrHCwkKtW7dOnTt3liQFBQWpVq1adjVZWVlKS0uz1QQHBysvL0/ff/+9rWbLli3Ky8uzq0lLS1NWVpatJjExUWazWUFBQbaa9evX292mJjExUVarVU2aNKn4LwAAAKAKOTQUnjhxQqmpqUpNTZX05+KS1NRUpaeny2QyKTo6WlOmTNHy5cuVlpamiIgIubq6KiwsTJJksVg0atQojRs3TmvWrFFKSoqGDx+u1q1b21Yjt2zZUr1799bo0aOVnJys5ORkjR49Wv3795e/v78kqWfPngoICFB4eLhSUlK0Zs0ajR8/XqNHj7bN7IWFhclsNisiIkJpaWlavny5pkyZwspjAADwj+DQW9Js27ZN3bp1s72PiYmRJI0YMULx8fGaMGGCTp8+rcjISOXm5qpjx45KTEyUu7u77W9ef/111axZU0OHDtXp06fVvXt3xcfHy8nJyVazePFiRUVF2VYpDxw40O7eiE5OTlq1apUiIyPVpUsXubi4KCwsTNOnT7fVWCwWJSUlacyYMWrfvr08PT0VExNj6xkAAOBq5tBQ2LVr10s+EcRkMmnSpEmaNGnSRWtq166tWbNmadasWRetqVOnjhYtWnTJXho1aqQvvvjikjWtW7fW+vXrL1kDAABwNaq21xQCAACg6hAKAQAAQCgEAAAAoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAIhQCAAAABEKAQAAIEIhAAAARCgsl7fffltNmzZV7dq1FRQUpO+++87RLQEAAPwthMIy+vDDDxUdHa1nn31WKSkpuv3229WnTx+lp6c7ujUAAIByIxSWUVxcnEaNGqWHHnpILVu21MyZM+Xn56d33nnH0a0BAACUW01HN3A1KSws1Pbt2/X000/bjffs2VObNm264N8UFBSooKDA9j4vL0+SlJ+fX3mN/kVJwakqOU5Vq6rvr6rxe119+M2uLvxeVx9+s4o5jmEYl60lFJbB77//ruLiYvn4+NiN+/j4KDs7+4J/M3XqVL300kulxv38/Cqlx2uFZaajO0BZ8HtdffjNri78Xlefqv7Njh8/LovFcskaQmE5mEwmu/eGYZQaO2fixImKiYmxvS8pKdHRo0dVt27di/7N1Sg/P19+fn46dOiQPDw8HN0OLoPf6+rDb3Z14fe6+vxTfzPDMHT8+HFZrdbL1hIKy8DLy0tOTk6lZgVzcnJKzR6eYzabZTab7cauv/76ymrR4Tw8PP5R/5j+6fi9rj78ZlcXfq+rzz/xN7vcDOE5LDQpA2dnZwUFBSkpKcluPCkpSZ07d3ZQVwAAAH8fM4VlFBMTo/DwcLVv317BwcGaM2eO0tPT9a9//cvRrQEAAJQbobCM7r33Xv3xxx+aPHmysrKyFBgYqC+//FKNGzd2dGsOZTab9eKLL5Y6VY7qid/r6sNvdnXh97r68JtJJuNK1igDAADgH41rCgEAAEAoBAAAAKEQAAAAIhQCAABAhEIAAACIW9Lgbzh79qzWrl2r/fv3KywsTO7u7srMzJSHh4euu+46R7cHAICdvz529nLi4uIqsZPqiVCIcvn111/Vu3dvpaenq6CgQKGhoXJ3d1dsbKzOnDmjd99919Et4jxnz55V7dq1lZqaqsDAQEe3g4tYuXLlFdcOHDiwEjtBWRUVFalnz5567733dOONNzq6HVxASkqK3fvt27eruLhY/v7+kqS9e/fKyclJQUFBjmjP4QiFKJfHH39c7du3148//qi6devaxgcPHqyHHnrIgZ3hYmrWrKnGjRuruLjY0a3gEu666y679yaTSX+9nazJZLL9N79l9VKrVi2lpaXZ/UaoXr799lvbf8fFxcnd3V0LFy6Up6enJCk3N1cPPvigbr/9dke16FBcU4hy2bBhg5577jk5OzvbjTdu3Fi//fabg7rC5Tz33HOaOHGijh496uhWcBElJSW2V2Jiotq2bauvvvpKx44dU15enr788kvdcsstSkhIcHSruIAHHnhA8+bNc3QbuAIzZszQ1KlTbYFQkjw9PfXyyy9rxowZDuzMcZgpRLmUlJRccJYiIyND7u7uDugIV+LNN9/U//73P1mtVjVu3Fhubm5223/44QcHdYYLiY6O1rvvvqvbbrvNNtarVy+5urrq4Ycf1u7dux3YHS6ksLBQ//nPf5SUlKT27duX+jd2LV6nVl3l5+fr8OHDatWqld14Tk6Ojh8/7qCuHItQiHIJDQ3VzJkzNWfOHEl/ntI6ceKEXnzxRfXt29fB3eFizj81iept//79slgspcYtFosOHjxY9Q3hstLS0nTLLbdI+vP6tL/itHL1MnjwYD344IOaMWOGOnXqJElKTk7Wk08+qSFDhji4O8fg2ccol8zMTHXr1k1OTk7at2+f2rdvr3379snLy0vr16+Xt7e3o1sErnp33HGHatWqpUWLFql+/fqSpOzsbIWHh6uwsFDr1q1zcIfA1evUqVMaP3685s+fr6KiIkl/Xns9atQovfbaa6Vmea8FhEKU2+nTp7V06VL98MMPKikp0S233KJhw4bJxcXF0a0B/wj/+9//NHjwYO3Zs0eNGjWSJKWnp+vGG2/UihUr1Lx5cwd3iEvJyMiQyWRSgwYNHN0KLuHkyZPav3+/DMNQ8+bNr8kweA6hEPiHq1Onjvbu3SsvLy95enpe8hQWC1CqH8MwlJSUpJ9//lmGYSggIEA9evTgVGQ1VVJSYluocOLECUmSu7u7xo0bp2effVY1arC+szoiwP+JawpRLhe7l5rJZFLt2rXVvHlzNW3atIq7woW8/vrrtsU/M2fOdGwzKDOTyaSePXuqZ8+ejm4FV+DZZ5/VvHnzNG3aNHXp0kWGYWjjxo2aNGmSzpw5o1deecXRLeL/R4AvjZlClEuNGjVK3T9N+n/3VDOZTLrtttu0YsUKu+X+AMpm3bp1mj59unbv3i2TyaSWLVvqySefvGbvo1bdWa1Wvfvuu6VuLP7ZZ58pMjKSW3ZVIxMnTtS8efP00ksvlQrwo0ePviYD/LUXg1EhkpKS1KFDByUlJSkvL095eXlKSkrSrbfeqi+++ELr16/XH3/8ofHjxzu6VVzE6dOnlZ+fb/dC9bJo0SL16NFDrq6uioqK0mOPPSYXFxd1795dS5YscXR7uICjR4/qpptuKjV+0003cXlGNbNw4UL95z//0aOPPqo2bdro5ptvVmRkpObOnav4+HhHt+cQzBSiXAIDAzVnzhx17tzZbnzjxo16+OGHtXPnTq1evVojR45Uenq6g7rE+U6ePKmnnnpKH330kf74449S23lCRvXSsmVLPfzww3riiSfsxuPi4jR37lzuU1gNdezYUR07dtSbb75pNz527Fht3bpVycnJDuoM56tdu7Z++umnUo8k3LNnj9q2bavTp087qDPHYaYQ5bJ//355eHiUGvfw8NAvv/wiSWrRooV+//33qm4NlzBhwgR98803evvtt2U2m/Wf//xHL730kqxWq95//31Ht4fz/PLLLxowYECp8YEDB+rAgQMO6AiXExsbq/nz5ysgIECjRo3SQw89pICAAMXHx+u1115zdHv4i5tvvlmzZ88uNT579mzdfPPNDujI8VhognIJCgrSk08+qffff1/16tWTJB05ckQTJkxQhw4dJEn79u1Tw4YNHdkmzvP555/r/fffV9euXTVy5Ejdfvvtat68uRo3bqzFixdr2LBhjm4Rf+Hn56c1a9aUuvXMmjVr5Ofn56CucCkhISHau3ev3nrrLduK8SFDhigyMlJWq9XR7eEvYmNj1a9fP61evVrBwcEymUzatGmTDh06pC+//NLR7TkEoRDlMm/ePA0aNEgNGzaUn5+fTCaT0tPTdcMNN+izzz6TJJ04cULPP/+8gzvFXx09etS2KtzDw8N2jdNtt92mRx991JGt4QLGjRunqKgopaamqnPnzjKZTNqwYYPi4+P1xhtvOLo9XITVar0mFylcbQjwpXFNIcrNMAx9/fXX2rt3rwzD0E033aTQ0NBrchn/1aJNmzaaNWuWQkJC1LNnT7Vp00bTp0/Xm2++qdjYWGVkZDi6RZxn+fLlmjFjhu36wXOrjwcNGuTgznAxx44d07x582wrxgMCAjRy5MgLPrIQqE4IhcA14JdfflGTJk30xhtvyMnJSVFRUfr222/Vr18/FRcX6+zZs4qLi9Pjjz/u6FaBq9q2bdvUq1cvubi46NZbb5VhGNq2bZtOnz6txMRE23ORUT0Q4O0RClFuJ0+e1Lp165Senq7CwkK7bVFRUQ7qChfi5OSkrKws2zOp7733Xr355psqKCjQtm3b1KxZs2v2wuqrwfbt2+3+R6tdu3aObgkXce463blz56pmzT+v0Dp79qweeugh/fLLL1q/fr2DO8Q5BPjSCIUol5SUFPXt21enTp3SyZMnVadOHf3+++9ydXWVt7e3bQUyqocaNWooOzvbFgrd3d31448/6oYbbnBwZ7iUnJwc3XfffVq7dq2uv/56GYahvLw8devWTcuWLbMt8kL14eLiopSUlFL3Kty1a5fat2+vU6dOOagznI8AXxoXf6FcnnjiCQ0YMEBHjx6Vi4uLkpOT9euvvyooKEjTp093dHvAP8LYsWOVn5+vnTt36ujRo8rNzVVaWpry8/OZja+mPDw8Lnhv1kOHDtkeN4nqYdu2bXrqqadsgVCSatasqQkTJmjbtm0O7MxxCIUol9TUVI0bN05OTk5ycnJSQUGB/Pz8FBsbq2eeecbR7eE8JpNJJpOp1Biqt4SEBL3zzjtq2bKlbSwgIEBvvfWWvvrqKwd2hou59957NWrUKH344Yc6dOiQMjIytGzZMj300EO6//77Hd0e/oIAXxq3pEG51KpVyxYqfHx8lJ6erpYtW8pisfAEk2rIMAxFRETIbDZLks6cOaN//etfcnNzs6v79NNPHdEeLqKkpES1atUqNV6rVi2VlJQ4oCNczvTp01WjRg098MADOnv2rKQ/f69HH31U06ZNc3B3+KtzAX769Ol2t3x68sknr9kATyhEubRr107btm3TjTfeqG7duumFF17Q77//rg8++ECtW7d2dHs4z4gRI+zeDx8+3EGdoCzuvPNOPf7441q6dKntvmm//fabnnjiCXXv3t3B3eGvTp06pSeffFIrVqxQUVGR7rrrLj322GOyWCxq3ry5XF1dHd0izjN9+nSZTCYC/F+w0ATlsm3bNh0/flzdunXTkSNHNGLECG3YsEHNmzfXggULWMkKVIBDhw5p0KBBSktLs90k/tdff1WbNm302Wef8cSgauTJJ5/U22+/rWHDhsnFxUVLlixR165d9d///tfRreEyTp06pf3798swjGs+wBMKUWaGYSg9PV3e3t5ycXFxdDvAP97q1au1e/duGYahgIAA9ejRw9Et4TzNmjXTK6+8ovvuu0+S9P3336tLly46c+aMnJycHNwdcGUIhSizkpIS1a5dWzt37lSLFi0c3Q7wj3P69GmtWbNG/fv3lyRNnDhRBQUFtu01a9bU5MmTVbt2bUe1iPM4OzvrwIEDatCggW3MxcVFe/fu5TnV1UxWVpZmz55texThbbfdZnerICcnJ61YscLut7xWsPoYZVajRg21aNFCf/zxh6NbAf6R3n//fb333nu297Nnz9amTZuUkpKilJQUffDBB3rnnXcc2CHOV1xcLGdnZ7uxmjVr2q5VQ/Xx9ttv69ixY7b3P/74o26//XYNGjRIgwYNkpOTk15//XXHNehAzBSiXFatWqVp06bpnXfeUWBgoKPbAf5R7rjjDj3xxBMaPHiwpNI3G1+0aJHeeustbd682ZFt4i9q1KihPn362Fb4S9Lnn3+uO++8026VPyv8Ha9t27Z67bXXFBoaKqn0v6+vv/5aMTEx2rlzpyPbdAhWH6Nchg8frlOnTunmm2+Ws7NzqWsLjx496qDOgKvf3r17deONN9re165dWzVq/L8TO7feeqvGjBnjiNZwEeev8JdY5V9dHTx4UM2aNbO9Dw0NtQvu/v7+OnDggCNaczhCIcpl5syZjm4B+MfKy8uze8rCkSNH7LaXlJTYXWMIx1uwYIGjW8AVOnv2rPLy8mzvz5+9zc3Ntft/wq4lhEKUy4X+v2IAFaNhw4ZKS0uTv7//Bbf/9NNP3I4GKCd/f39t2rRJ7dq1u+D27777zm6m/lpybUZhVIj9+/frueee0/3336+cnBxJfz6W61q8DgOoSH379tULL7ygM2fOlNp2+vRpvfTSS+rXr58DOgOufvfdd59eeOEF/fTTT6W2/fjjj3rppZeu2SeasNAE5bJu3Tr16dNHXbp00fr167V7927dcMMNio2N1ffff6+PP/7Y0S0CV63Dhw+rbdu2cnZ21mOPPaYbb7xRJpNJP//8s2bPnq2zZ88qJSVFPj4+jm4VuOoUFRWpR48e2rRpk0JDQ+Xv72/795WUlKTg4GCtWbPmgo+Y/KcjFKJcgoODdc899ygmJsZu5dbWrVt111136bfffnN0i8BV7cCBA3r00UeVlJSkc/9n2mQyKTQ0VG+//bZtpSSAsissLFRcXJyWLVumvXv3SpJatGih+++/X0888YTdKvJrCaEQ5XLddddpx44datq0qV0oPHjwoG666aYLnvYCUHZHjx7V//73P0lS8+bNVadOHQd3BOCfioUmKJfrr79eWVlZatq0qd14SkrKNXkXeKCy1KlTR7feequj2wBwDWChCcolLCxMTz31lLKzs2UymVRSUqKNGzdq/PjxeuCBBxzdHgAAKCNOH6NcioqKFBERoWXLlskwDNWsWVPFxcUKCwtTfHw8D4AHAOAqQyjE37J//36lpKSopKRE7dq1U4sWLRzdEgAAKAdCIcpl3bp1CgkJcXQbAACUy9q1a9W1a1dHt1GtEApRLs7OzvL19VVYWJiGDx+uwMBAR7cEAMAVq127tho0aKAHH3xQI0aMkJ+fn6NbcjgWmqBcMjMzNWHCBH333Xdq06aN2rRpo9jYWGVkZDi6NQAALiszM1OPP/64Pv30UzVt2lS9evXSRx99pMLCQke35jDMFOJvO3DggJYsWaKlS5fq559/1h133KFvvvnG0W0BAHBFUlNTNX/+fC1dulQlJSUaNmyYRo0apZtvvtnRrVUpQiEqRHFxsb766is9//zz+umnn1RcXOzolgAAuGKZmZmaM2eOpk2bppo1a+rMmTMKDg7Wu+++q1atWjm6vSrB6WP8LRs3blRkZKTq16+vsLAwtWrVSl988YWj2wIA4LKKior08ccfq2/fvmrcuLG+/vprzZ49W4cPH9aBAwfk5+ene+65x9FtVhlmClEuzzzzjJYuXarffvtNoaGhGjZsmO666y65uroqNTVVbdu2dXSLAABc1NixY7V06VJJ0vDhw/XQQw+VWjSZnp6uJk2aqKSkxBEtVjkec4dyWbt2rcaPH697771XXl5eysvLU3x8vObNm6fU1FROHwMAqrVdu3Zp1qxZuvvuu+Xs7HzBGqvVqm+//baKO3McQiHKZdOmTZKkb775RvPnz9enn36qxo0b6+6779Z//vMfB3cHAMDFFRUVqVGjRurYseNFA6Ek1axZ85q6Jy+nj1FmGRkZio+P14IFC3TixAkNHTpU7777rn788UcFBAQ4uj0AAC7r+uuv1w8//KAbbrjB0a1UGyw0QZn07dtXAQEB2rlzp958801lZmZq1qxZjm4LAIAyGTx4sFasWOHoNqoVTh+jTBITExUVFaVHH32U5xwDAK5azZs317///W9t2rRJQUFBcnNzs9seFRXloM4ch9PHKJPNmzdr/vz5+uijj3TTTTcpPDxc9957r6xWK6ePAQBXjaZNm150m8lk0i+//FKF3VQPhEKUy6lTp7Rs2TLNnz9f33//vYqLixUXF6eRI0fK3d3d0e0BAIAyIhTib9uzZ4/mzZunDz74QMeOHVNoaKhWrlzp6LYAALiswsJCHThwQM2aNVPNmtf2VXUsNMHf5u/vr9jYWGVkZNhuBAoAQHV26tQpjRo1Sq6urmrVqpXS09Ml/Xkt4bRp0xzcnWMQClFhnJycdNdddzFLCACo9iZOnKgff/xRa9euVe3atW3jPXr00IcffujAzhzn2p4nBQAA16QVK1boww8/VKdOnWQymWzjAQEB2r9/vwM7cxxmCgEAwDXnyJEj8vb2LjV+8uRJu5B4LSEUAgCAa06HDh20atUq2/tzQXDu3LkKDg52VFsOxeljAABwzZk6dap69+6tXbt26ezZs3rjjTe0c+dObd68WevWrXN0ew7BTCEAALhmpKamSpI6d+6sjRs36tSpU2rWrJkSExPl4+OjzZs3KygoyLFNOgj3KQQAANeMGjVqqF27dnrooYcUFhYmi8Xi6JaqDWYKAQDANWPjxo265ZZb9PTTT6t+/foKDw/Xt99+6+i2qgVmCgEAwDXn9OnT+uijj7RgwQJ99913atKkiUaOHKkRI0aoYcOGjm7PIQiFAADgmrZ//34tWLBA77//vrKyshQaGqovv/zS0W1VOUIhAAC45p04cUKLFy/WM888o2PHjqm4uNjRLVU5bkkDAACuWevWrdP8+fP1ySefyMnJSUOHDtWoUaMc3ZZDMFMIAACuKYcOHVJ8fLzi4+N14MABde7cWaNGjdLQoUPl5ubm6PYchplCAABwzQgNDdW3336revXq6YEHHtDIkSPl7+/v6LaqBUIhAAC4Zri4uOiTTz5R//795eTk5Oh2qhVOHwMAAICbVwMAAIBQCAAAABEKAQAAIEIhAAAARCgEgEpnGIYefvhh1alTRyaTSampqWXex6RJk9S2bdsK7w0AziEUAkAlS0hIUHx8vL744gtlZWUpMDBQJpNJK1asqLRjHjx48IIBdOfOnbr77rvVpEkTmUwmzZw5s9TfTpo0SSaTye7l6+tbab0CqB64TyEAVLL9+/erfv366ty5s6Nb0alTp3TDDTfonnvu0RNPPHHRulatWmn16tW299zPDfjnY6YQAK7Axx9/rNatW8vFxUV169ZVjx49dPLkSRUXFysmJkbXX3+96tatqwkTJmjEiBG66667JEkREREaO3as0tPTZTKZ1KRJEzVp0kSSNHjwYNtYWZWUlGjy5Mlq2LChzGaz2rZtq4SEBNv2pk2bSpLatWsnk8mkrl27SpI6dOig1157Tffdd5/MZvNF91+zZk35+vraXvXq1StzjwCuLoRCALiMrKws3X///Ro5cqR2796ttWvXasiQITIMQzNmzND8+fM1b948bdiwQUePHtXy5cttf/vGG2/YwltWVpa2bt2qrVu3SpIWLFhgGyurN954QzNmzND06dP1008/qVevXho4cKD27dsnSfr+++8lSatXr1ZWVpY+/fTTMu1/3759slqtatq0qe677z798ssvZe4RwNWF08cAcBlZWVk6e/ashgwZosaNG0uSWrduLUmaOXOmJk6cqLvvvluS9O677+rrr7+2/a3FYpG7u7ucnJxKXZd3/fXXl/tavenTp+upp57SfffdJ0l69dVX9e2332rmzJl66623bDN7devWLfMxOnbsqPfff1833nijDh8+rJdfflmdO3fWzp07Vbdu3XL1C6D6IxQCwGXcfPPN6t69u1q3bq1evXqpZ8+e+r//+z/VqFFDWVlZCg4OttXWrFlT7du3V2U+QTQ/P1+ZmZnq0qWL3XiXLl30448//u399+nTx/bfrVu3VnBwsJo1a6aFCxcqJibmb+8fQPXE6WMAuAwnJyclJSXpq6++UkBAgGbNmiV/f38dPHjQoX2ZTCa794ZhlBqrCG5ubmrdurXt1DSAfyZCIQBcAZPJpC5duuill15SSkqKnJ2dtWbNGtWvX1/Jycm2urNnz2r79u2X3V+tWrVUXFxcrl48PDxktVq1YcMGu/FNmzapZcuWkiRnZ2dJKvcx/qqgoEC7d+9W/fr1//a+AFRfnD4GgMvYsmWL1qxZo549e8rb21tbtmzRkSNH1LJlSz3++OOaNm2aWrRooZYtWyouLk7Hjh277D6bNGmiNWvWqEuXLjKbzfL09CxTT08++aRefPFFNWvWTG3bttWCBQuUmpqqxYsXS5K8vb3l4uKihIQENWzYULVr15bFYlFhYaF27dolSSosLNRvv/2m1NRUXXfddWrevLkkafz48RowYIAaNWqknJwcvfzyy8rPz9eIESPK9sUBuKoQCgHgMjw8PLR+/XrNnDlT+fn5aty4sWbMmKE+ffooNDRUWVlZioiIUI0aNTRy5EgNHjxYeXl5l9znjBkzFBMTo7lz56pBgwZlPhUdFRWl/Px8jRs3Tjk5OQoICNDKlSvVokULSX9e2/jmm29q8uTJeuGFF3T77bdr7dq1yszMVLt27Wz7mT59uqZPn66QkBCtXbtWkpSRkaH7779fv//+u+rVq6dOnTopOTnZtsgGwD+TyajMq6EB4BoUERGhY8eOVeoTSwCgonFNIQAAAAiFAFAdtGrVStddd90FX+euEwSAysTpYwCoBn799VcVFRVdcJuPj4/c3d2ruCMA1xpCIQAAADh9DAAAAEIhAAAARCgEAACACIUAAAAQoRAAAAAiFAIAAECEQgAAAEj6/wAFYUbmgoNv0AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# How condition relates to saleprice\n", + "# plot the barplot\n", + "plt.figure(figsize = (7,5))\n", + "kings_data.groupby('condition')['price'].mean().plot.bar()\n", + "\n", + "# set the axes and title\n", + "plt.xlabel(column)\n", + "plt.ylabel('Average price')\n", + "plt.title('Condition vs Sales')\n", + "\n", + "# display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The houses that are in good cnditions are the most popular" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet visualizes the relationship between the 'grade' feature and the average sale price. A bar plot is used to show the average price for properties with different grades. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# How grade relates to saleprice\n", + "# plot the barplot\n", + "plt.figure(figsize = (7,5))\n", + "kings_data.groupby('grade')['price'].mean().plot.bar()\n", + "\n", + "# set the axes and title\n", + "plt.xlabel(column)\n", + "plt.ylabel('Average price')\n", + "plt.title('Grade vs Sales')\n", + "\n", + "# display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "compairing mansions with the others u can tell its the highest selling " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This code snippet iterates through the categorical columns specified in the list `Categorical` and creates bar charts to visualize the count of each category\n", + "for column in Categorical:\n", + " plt.figure()\n", + " kings_data[column].value_counts().plot.bar()\n", + " plt.xlabel(column)\n", + " plt.ylabel('Count')\n", + " plt.title(column)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "#This function creates subplots of boxplots for each numeric column in the DataFrame, allowing for visualization of the data distribution and identification of outliers. Adjust the parameters and example usage according to your DataFrame and preferences.\n", + "\n", + "def check_for_outliers_and_plot_boxplot(df, numeric_columns_list, figsize=(12, 12)):\n", + " \n", + " # Calculate number of subplots\n", + " num_plots = len(numeric_columns)\n", + " num_cols = 2 # Number of columns in each row of subplots\n", + " num_rows = (num_plots + 1) // num_cols\n", + " \n", + " # Create subplots\n", + " fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n", + " axes = axes.flatten()\n", + "\n", + "\n", + " # Plot boxplots for each column\n", + " for i, column in enumerate(numeric_columns):\n", + " sns.boxplot(x=df[column], ax=axes[i])\n", + " axes[i].set_title(f'Boxplot of {column}')\n", + " axes[i].set_xlabel(column)\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "check_for_outliers_and_plot_boxplot(house_data_clean, numeric_columns, figsize=(12, 12))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the illustration you can see the data set has outliers " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function removes outliers from the specified column(s) in the DataFrame based on Z-scores, with an option to set a custom threshold. ." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_outliers_by_zscore(df, col, threshold=3):\n", + " \n", + " # Iterate over each specified column\n", + " for column in col:\n", + " # Calculate Z-scores for the column\n", + " z_scores = (df[column] - df[column].mean()) / df[column].std()\n", + " # Filter rows where Z-score exceeds the threshold\n", + " df_cleaned = df[(z_scores.abs() < threshold)]\n", + " \n", + " return df_cleaned" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
071293005202014221900.031.00118056501.0NONONEAverage7 Average11800.019559817847.5112-122.25713405650
164141001922014538000.032.25257072422.0NONONEAverage7 Average2170400.019519812547.7210-122.31916907639
256315004002015180000.021.00770100001.0NONONEAverage6 Low Average7700.019339802847.7379-122.23327208062
324872008752014604000.043.00196050001.0NONONEVery Good7 Average1050910.019659813647.5208-122.39313605000
419544005102015510000.032.00168080801.0NONONEAverage8 Good16800.019879807447.6168-122.04518007503
...............................................................
215922630000182014360000.032.50153011313.0NONONEAverage8 Good15300.020099810347.6993-122.34615301509
2159366000601202015400000.042.50231058132.0NONONEAverage8 Good23100.020149814647.5107-122.36218307200
2159415233001412014402101.020.75102013502.0NONONEAverage7 Average10200.020099814447.5944-122.29910202007
215952913101002015400000.032.50160023882.0NONONEAverage8 Good16000.020049802747.5345-122.06914101287
2159615233001572014325000.020.75102010762.0NONONEAverage7 Average10200.020089814447.5941-122.29910201357
\n", + "

21173 rows × 20 columns

\n", + "
" + ], + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "0 7129300520 2014 221900.0 3 1.00 1180 5650 \n", + "1 6414100192 2014 538000.0 3 2.25 2570 7242 \n", + "2 5631500400 2015 180000.0 2 1.00 770 10000 \n", + "3 2487200875 2014 604000.0 4 3.00 1960 5000 \n", + "4 1954400510 2015 510000.0 3 2.00 1680 8080 \n", + "... ... ... ... ... ... ... ... \n", + "21592 263000018 2014 360000.0 3 2.50 1530 1131 \n", + "21593 6600060120 2015 400000.0 4 2.50 2310 5813 \n", + "21594 1523300141 2014 402101.0 2 0.75 1020 1350 \n", + "21595 291310100 2015 400000.0 3 2.50 1600 2388 \n", + "21596 1523300157 2014 325000.0 2 0.75 1020 1076 \n", + "\n", + " floors waterfront view condition grade sqft_above \\\n", + "0 1.0 NO NONE Average 7 Average 1180 \n", + "1 2.0 NO NONE Average 7 Average 2170 \n", + "2 1.0 NO NONE Average 6 Low Average 770 \n", + "3 1.0 NO NONE Very Good 7 Average 1050 \n", + "4 1.0 NO NONE Average 8 Good 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 3.0 NO NONE Average 8 Good 1530 \n", + "21593 2.0 NO NONE Average 8 Good 2310 \n", + "21594 2.0 NO NONE Average 7 Average 1020 \n", + "21595 2.0 NO NONE Average 8 Good 1600 \n", + "21596 2.0 NO NONE Average 7 Average 1020 \n", + "\n", + " sqft_basement yr_built zipcode lat long sqft_living15 \\\n", + "0 0.0 1955 98178 47.5112 -122.257 1340 \n", + "1 400.0 1951 98125 47.7210 -122.319 1690 \n", + "2 0.0 1933 98028 47.7379 -122.233 2720 \n", + "3 910.0 1965 98136 47.5208 -122.393 1360 \n", + "4 0.0 1987 98074 47.6168 -122.045 1800 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 98103 47.6993 -122.346 1530 \n", + "21593 0.0 2014 98146 47.5107 -122.362 1830 \n", + "21594 0.0 2009 98144 47.5944 -122.299 1020 \n", + "21595 0.0 2004 98027 47.5345 -122.069 1410 \n", + "21596 0.0 2008 98144 47.5941 -122.299 1020 \n", + "\n", + " sqft_lot15 \n", + "0 5650 \n", + "1 7639 \n", + "2 8062 \n", + "3 5000 \n", + "4 7503 \n", + "... ... \n", + "21592 1509 \n", + "21593 7200 \n", + "21594 2007 \n", + "21595 1287 \n", + "21596 1357 \n", + "\n", + "[21173 rows x 20 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cleaned_data = remove_outliers_by_zscore(house_data_clean, numeric_columns, threshold=3)\n", + "cleaned_data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will return a DataFrame with outliers removed from the specified numeric columns based on their Z-scores, using a threshold of 3." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\USER PC\\AppData\\Local\\Temp\\ipykernel_17800\\1295920640.py:3: UserWarning: \n", + "\n", + "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", + "\n", + "Please adapt your code to use either `displot` (a figure-level function with\n", + "similar flexibility) or `histplot` (an axes-level function for histograms).\n", + "\n", + "For a guide to updating your code to use the new functions, please see\n", + "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", + "\n", + " price_dist = sns.distplot(kings_data[\"price\"])\n", + "c:\\Users\\USER PC\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting a histogram with kernel density estimate (KDE) of the \"price\" variable\n", + "plt.figure(figsize=(10,4))\n", + "price_dist = sns.distplot(kings_data[\"price\"])\n", + "price_dist.set(xlabel=\"Price in Millions\", title=\"Price Density of Houses in King's County\")\n", + "plt.savefig('Visualization2') # Save the plot as an image file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observation reveals that the distribution of house is right-skewed.we look at house prices, we notice that most houses are not very expensive, but there are a few that are very pricey." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#This code snippet creates a scatter plot matrix for the house data, with each numeric variable plotted against the 'price'.\n", + "# creating a list of all column names\n", + "plot_list = list(house_data_clean.columns)\n", + "len(plot_list)\n", + "fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(19,20))\n", + "axes = axes.flatten() # flatten the array to make it easier to iterate over\n", + "\n", + "for i, xcol in enumerate(plot_list[0:19]):\n", + " fig.patch.set_facecolor('whitesmoke')\n", + " house_data_clean.plot(kind='scatter', x=xcol, y='price', ax=axes[i], alpha=0.4, color='Purple', marker='^')\n", + " \n", + "plt.show() # add this to display the plot" + ] } ], "metadata": { @@ -1041,7 +1940,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.7" } }, "nbformat": 4, From 4a8e31190855756eaacea403395eea6ddbe87ea0 Mon Sep 17 00:00:00 2001 From: HSimiyu Date: Wed, 1 May 2024 16:11:25 +0000 Subject: [PATCH 47/53] create functions for regression --- student.ipynb | 3671 +++++++++++++++++++++++-------------------------- 1 file changed, 1730 insertions(+), 1941 deletions(-) diff --git a/student.ipynb b/student.ipynb index da0bd619..7005a4f3 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1,1948 +1,1737 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Project Submission\n", - "\n", - "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", - "* Student pace: full time\n", - "* Scheduled project review date/time: \n", - "* Instructor name: Nikita \n", - "* Blog post URL:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Kings County Housing Analysis with Multiple Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Business Problem\n", - "\n", - "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Objectives\n", - "* To determine the key factors influencing house prices.\n", - "* To develop multilinear regression models to predict house prices based on relevant features.\n", - "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hypothesis\n", - "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", - "\n", - "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Understanding:\n", - "\n", - "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### King County Housing Data Columns \n", - "\n", - "The column names contained in column_names.md are:\n", - "* `id`: A unique identifier for each house sale.\n", - "* `date`: The date when the house was sold.\n", - "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", - "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", - "* `floors`: The number of floors in the house.\n", - "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", - "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", - "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", - "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading\n", - "\n", - "#### Import Necessary Libraries\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy.stats as stats\n", - "import seaborn as sns\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a function that loads data and return it in a dataframe\n", - "def load_data(file_path):\n", - " house_data = pd.read_csv(file_path)\n", - "\n", - " #shape\n", - " shape = house_data.shape\n", - " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", - " print()\n", - " \n", - " #Data Types\n", - " data_types = house_data.dtypes\n", - " print(\"Columns and their data types:\")\n", - " for column, dtype in data_types.items():\n", - " print(f\"{column}: {dtype}\")\n", - " print()\n", - "\n", - " return house_data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontview...gradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221900.031.00118056501.0NaNNONE...7 Average11800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538000.032.25257072422.0NONONE...7 Average2170400.019511991.09812547.7210-122.31916907639
256315004002/25/2015180000.021.00770100001.0NONONE...6 Low Average7700.01933NaN9802847.7379-122.23327208062
3248720087512/9/2014604000.043.00196050001.0NONONE...7 Average1050910.019650.09813647.5208-122.39313605000
419544005102/18/2015510000.032.00168080801.0NONONE...8 Good16800.019870.09807447.6168-122.04518007503
..................................................................
215922630000185/21/2014360000.032.50153011313.0NONONE...8 Good15300.020090.09810347.6993-122.34615301509
2159366000601202/23/2015400000.042.50231058132.0NONONE...8 Good23100.020140.09814647.5107-122.36218307200
2159415233001416/23/2014402101.020.75102013502.0NONONE...7 Average10200.020090.09814447.5944-122.29910202007
215952913101001/16/2015400000.032.50160023882.0NaNNONE...8 Good16000.020040.09802747.5345-122.06914101287
21596152330015710/15/2014325000.020.75102010762.0NONONE...7 Average10200.020080.09814447.5941-122.29910201357
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21597 rows × 21 columns

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" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living \\\n", - "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", - "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", - "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", - "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", - "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", - "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", - "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", - "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", - "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", - "\n", - " sqft_lot floors waterfront view ... grade sqft_above \\\n", - "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", - "1 7242 2.0 NO NONE ... 7 Average 2170 \n", - "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", - "3 5000 1.0 NO NONE ... 7 Average 1050 \n", - "4 8080 1.0 NO NONE ... 8 Good 1680 \n", - "... ... ... ... ... ... ... ... \n", - "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", - "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", - "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", - "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", - "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", - "\n", - " sqft_basement yr_built yr_renovated zipcode lat long \\\n", - "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", - "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", - "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", - "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", - "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", - "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", - "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", - "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", - "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", - "\n", - " sqft_living15 sqft_lot15 \n", - "0 1340 5650 \n", - "1 1690 7639 \n", - "2 2720 8062 \n", - "3 1360 5000 \n", - "4 1800 7503 \n", - "... ... ... \n", - "21592 1530 1509 \n", - "21593 1830 7200 \n", - "21594 1020 2007 \n", - "21595 1410 1287 \n", - "21596 1020 1357 \n", - "\n", - "[21597 rows x 21 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", - "\n", - "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataset contains 21597 houses with 21 features\n", - "\n", - "Columns and their data types:\n", - "id: int64\n", - "date: object\n", - "price: float64\n", - "bedrooms: int64\n", - "bathrooms: float64\n", - "sqft_living: int64\n", - "sqft_lot: int64\n", - "floors: float64\n", - "waterfront: object\n", - "view: object\n", - "condition: object\n", - "grade: object\n", - "sqft_above: int64\n", - "sqft_basement: object\n", - "yr_built: int64\n", - "yr_renovated: float64\n", - "zipcode: int64\n", - "lat: float64\n", - "long: float64\n", - "sqft_living15: int64\n", - "sqft_lot15: int64\n", - "\n" - ] - } - ], - "source": [ - "kings_data = load_data('data/kc_house_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#create a function that takes in a column and returns the column statistics as a dictionary\n", - "def descriptive_analytics(column):\n", - " stats_dict = column.describe().to_dict()\n", - " \n", - " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", - " print(\"The count of the column is:\", stats_dict['count'])\n", - " print(\"The mean of the column is:\", stats_dict['mean'])\n", - " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", - " print(\"The minimum value of the column is:\", stats_dict['min'])\n", - " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", - " print(\"The median of the column is:\", stats_dict['50%'])\n", - " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", - " print(\"The maximum value of the column is:\", stats_dict['max'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Descriptive Statistics for Column 'price':\n", - "The count of the column is: 21597.0\n", - "The mean of the column is: 540296.5735055795\n", - "The standard deviation of the column is: 367368.1401013936\n", - "The minimum value of the column is: 78000.0\n", - "The 25th percentile of the column is: 322000.0\n", - "The median of the column is: 450000.0\n", - "The 75th percentile of the column is: 645000.0\n", - "The maximum value of the column is: 7700000.0\n" - ] - } - ], - "source": [ - "descriptive_analytics(kings_data['price'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", - "\n", - "There are 21597 prices regarding to the houses in the dataset\n", - "\n", - "Average price of a house is 540296.57 dollars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preperation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21597 entries, 0 to 21596\n", - "Data columns (total 21 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 21597 non-null int64 \n", - " 1 date 21597 non-null object \n", - " 2 price 21597 non-null float64\n", - " 3 bedrooms 21597 non-null int64 \n", - " 4 bathrooms 21597 non-null float64\n", - " 5 sqft_living 21597 non-null int64 \n", - " 6 sqft_lot 21597 non-null int64 \n", - " 7 floors 21597 non-null float64\n", - " 8 waterfront 19221 non-null object \n", - " 9 view 21534 non-null object \n", - " 10 condition 21597 non-null object \n", - " 11 grade 21597 non-null object \n", - " 12 sqft_above 21597 non-null int64 \n", - " 13 sqft_basement 21597 non-null object \n", - " 14 yr_built 21597 non-null int64 \n", - " 15 yr_renovated 17755 non-null float64\n", - " 16 zipcode 21597 non-null int64 \n", - " 17 lat 21597 non-null float64\n", - " 18 long 21597 non-null float64\n", - " 19 sqft_living15 21597 non-null int64 \n", - " 20 sqft_lot15 21597 non-null int64 \n", - "dtypes: float64(6), int64(9), object(6)\n", - "memory usage: 3.5+ MB\n" - ] - } - ], - "source": [ - "kings_data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def identify_issues(dataset):\n", - " # Identify missing values as a percentage of the whole dataset\n", - " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", - "\n", - " # Identify duplicates\n", - " duplicates = dataset.duplicated().sum()\n", - " \n", - " #return a dictionary \n", - " return {'duplicates': duplicates,\n", - " 'missing values': missing_values.round(2)} \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 0,\n", - " 'missing values': id 0.00\n", - " date 0.00\n", - " price 0.00\n", - " bedrooms 0.00\n", - " bathrooms 0.00\n", - " sqft_living 0.00\n", - " sqft_lot 0.00\n", - " floors 0.00\n", - " waterfront 11.00\n", - " view 0.29\n", - " condition 0.00\n", - " grade 0.00\n", - " sqft_above 0.00\n", - " sqft_basement 0.00\n", - " yr_built 0.00\n", - " yr_renovated 17.79\n", - " zipcode 0.00\n", - " lat 0.00\n", - " long 0.00\n", - " sqft_living15 0.00\n", - " sqft_lot15 0.00\n", - " dtype: float64}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(kings_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Before making changes make a copy instead of overwriting data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean = kings_data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Changing the date to date time\n", - "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", - "\n", - "# Extracting only the year from the column Date\n", - "house_data_clean.date = house_data_clean['date'].dt.year\n", - "\n", - "# Changing the dates for the year built \n", - "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dealing with the missing values" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def missing_values(dataset):\n", - " # drop the rows from views\n", - " dataset.dropna(subset=['view'],inplace=True)\n", - "\n", - " # Filling the NaN values for waterfront with NO\n", - " dataset.waterfront.fillna('NO',inplace=True)\n", - " \n", - " # Dropping the yr_renovated column \n", - " dataset.drop('yr_renovated',axis=1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "missing_values(house_data_clean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", - "\n", - "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", - "\n", - "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'duplicates': 2,\n", - " 'missing values': id 0.0\n", - " date 0.0\n", - " price 0.0\n", - " bedrooms 0.0\n", - " bathrooms 0.0\n", - " sqft_living 0.0\n", - " sqft_lot 0.0\n", - " floors 0.0\n", - " waterfront 0.0\n", - " view 0.0\n", - " condition 0.0\n", - " grade 0.0\n", - " sqft_above 0.0\n", - " sqft_basement 0.0\n", - " yr_built 0.0\n", - " zipcode 0.0\n", - " lat 0.0\n", - " long 0.0\n", - " sqft_living15 0.0\n", - " sqft_lot15 0.0\n", - " dtype: float64}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identify_issues(house_data_clean)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
394718250690312014550000.041.75241084472.0NOGOODGood8 Good2060350.019369807447.6499-122.088252014789
2003886489001102014555000.032.50194032112.0NONONEAverage8 Good19400.020099802747.5644-122.09318803078
\n", - "
" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", - "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", - "\n", - " floors waterfront view condition grade sqft_above sqft_basement \\\n", - "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", - "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", - "\n", - " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", - "3947 1936 98074 47.6499 -122.088 2520 14789 \n", - "20038 2009 98027 47.5644 -122.093 1880 3078 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_data_clean[house_data_clean.duplicated()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exploratory Data Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### King County Housing Analysis and Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the exploratory phase of data analysis, histograms and box plots play pivotal roles in understanding the distribution patterns of variables. Histograms provide a visual representation of the frequency and spread of values within each variable, offering valuable insights into the data's central tendencies and variability. Meanwhile, box plots offer a concise summary of the data's distribution, including measures of central tendency, variability, and the presence of potential outliers. Together, these visualizations provide a comprehensive overview of the dataset's characteristics, laying the groundwork for deeper analysis and informing subsequent modeling and decision-making processes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_statistics(df, column_name):\n", - " #calculate mean\n", - " mean_value = df[column_name].mean()\n", - " #calculate mode, and pick the first if multiple mode exist\n", - " mode_value = df[column_name].mode()[0]\n", - " #calculating median\n", - " median_value = house_data_clean[column_name].median()\n", - " #calculating std\n", - " std_value = df[column_name].std()\n", - "\n", - " # Create a dictionary to store the statistics\n", - " statistics = {\n", - " \"Mean\": mean_value,\n", - " \"Mode\": mode_value,\n", - " \"Median\": median_value,\n", - " \"Standard Deviation\": std_value\n", - " }\n", - " return statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Mean': 540057.663833937,\n", - " 'Mode': 350000.0,\n", - " 'Median': 450000.0,\n", - " 'Standard Deviation': 366059.5812312955}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "calculate_statistics(house_data_clean, \"price\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code snippet categorizes columns with numeric values from the DataFrame house_data_clean and prints them out" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "These are columns with numerical values:\n", - " ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'sqft_above', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15']\n" - ] - } - ], - "source": [ - "# Initialize lists for numeric columns\n", - "numeric_columns = []\n", - "\n", - "\n", - "# Iterate through columns and categorize them based on data type\n", - "for column in house_data_clean.columns:\n", - " if house_data_clean[column].dtype in ['int64', 'float64']: # Check if the column data type is numeric\n", - " numeric_columns.append(column)\n", - " \n", - " \n", - "print (\"These are columns with numerical values:\\n\",numeric_columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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QoUMF+h68LyoqCm/evMkxPMrOzg5fffUVDh06pFAuCALatm2b4zw/HH6qTN++fXH+/HlcuXIFq1atQqVKldCgQYNc62ZkZOCXX36Bq6sr9PT0oKOjAz09Pdy4caPA553XtfqQTCbDli1b8PTpU3h6ekIURWzatAna2toFOl62w4cPA0COdu7SpQuMjIxytHNu3x1VODk5oV+/fli0aBHi4+M/KtYP2drawsvLS/7Z0tISZcqUgYeHB8qWLSsv//C+8fbtWxw6dAgdOnSAoaGhwve9devWePv2LU6fPq1wLFWuz/Xr1xEXF4d+/fpBX1+/ME4xVx9zL/gUbdq0Ufic3Z5ff/11jvKkpKQcQw8bN24MExMT+Wdra2uUKVNG4d9mnTp1EBYWhmnTpuH06dNIT08vtPiJ6NOw80X0kVxcXFCrVq0ci5mZmUK9hw8fQhRFWFtbQ1dXV2E5ffq0/HmurKwsNG/eHNu3b8eYMWNw6NAhnD17Vv6j5c2bNyrHZmlpqfBZT08v3/K3b9/Ky3r06IFFixbh22+/xf79+3H27FmcO3cOpUuXzjUGGxubXMuePn2aZ3zPnj2DKIq5zsSX/SMvv+3zUqFCBTx+/BivXr1SWjd7/3nFkJWVlWMmtLxmDsyt/OHDh9i9e3eOa+7m5gYACs/xfWjOnDn47rvvULduXYSHh+P06dM4d+4cWrZsWaDvwfuUne+H7W1oaJjjB69MJlP4rqiiQYMGqFKlCkJDQ7F+/Xr07dsXgiDkWjcgIAATJkxA+/btsXv3bpw5cwbnzp1DjRo1CnTehoaGMDU1Vbl+5cqV8eWXX+Lt27fo2bPnJ80Q+fTpU+jo6OSY7EQQhFz/XXzKsYKCgqCtrY0JEyZ89D7e9+H9AXh3j1B233j69CkyMjKwcOHCHN/31q1bA8j5fVflvB8/fgwAeT6zlZ+ivhd8ik+5PwOAlZVVjn3KZDKFfyObN29G7969sXLlSnh7e8PS0hJ+fn5ITEwslHMgoo/H2Q6JilipUqUgCAJOnDgBmUyWY3122d9//43Lly8jLCwMvXv3lq+/efOm2mJNTk7Gnj17MGnSJIwbN05enpqaiqSkpFy3ye0/5omJiahcuXKex7GwsICWlhYSEhJyrHvw4AGAd+1WUC1atMCBAwewe/fuXCd0eF/2D5i8YtDS0oKFhYVCeV6dhtzKS5UqherVq+Pnn3/OdZv3Mwkf2rBhAxo1aoSlS5cqlL948SLPbZRRdr4f096q6tOnD8aPHw9BEBS+2x/asGED/Pz88MsvvyiUP3nypECvVMjrOuVl5cqV+P3331GnTh0sWrQIvr6+qFu3boH2kc3KygoZGRl4/PixQgdMFEUkJibKJwv52FjfZ2tri5EjR2L69OkYNWpUjvXZnecPJ0nJr+P/MSwsLORZ6yFDhuRax9HRUeGzKued3X737t0rcExFfS94n76+fq4T0Tx58qRI/13lp1SpUpg3bx7mzZuH+Ph47Nq1C+PGjcOjR49ynSyFiNSHmS+iIpb9npn79+/nmilzd3cH8N+PkQ87aKGhoTn2mV3nY7MgeREEAaIo5ohh5cqVub7UFQA2btyo8PnUqVO4c+dOjvdOvc/IyAh169bF9u3bFc4hKysLGzZsQPny5T9qKFa/fv1gY2ODMWPG4P79+7nW2b59O4B3w7bKlSuHX3/9VeFFwK9evUJ4eLh81rOP1aZNG/z999+oVKlSrtc9v86XIAg5rsFff/2VY+a1gnwPvL29YWBggA0bNiiU37t3L9d3cRWm3r17o23btvjhhx8UhsN+KLfz/v3333Ncy8L8/l+5cgXDhw+Hn58fTpw4gerVq8PX1/ejMx3Z7fhhO4eHh+PVq1eF3s5jx46FpaWlwh9LsmXP7PjXX38plO/atatQYzA0NETjxo1x6dIlVK9ePdfve27ZGmWqVq2KSpUqYfXq1QWaZRNQ773AwcEhRxtfv369yIdOq6pChQoYOnQomjVrhosXL0odDpHGY+aLqIj5+PhgwIAB6NOnD86fP48GDRrAyMgICQkJOHnyJNzd3fHdd9/B2dkZlSpVwrhx4yCKIiwtLbF79+5cp7vO7rDNnz8fvXv3hq6uLpycnBSeA/gYpqamaNCgAWbNmoVSpUrBwcEBx44dw6pVq/LMPJw/fx7ffvstunTpgrt37+Knn35CuXLlMHjw4HyPFRwcjGbNmqFx48YYPXo09PT0sGTJEvz999/YtGnTR2UEzMzMsHPnTrRp0wY1a9ZUeLHqjRs3sGHDBly+fBkdO3aElpYWZs6ciZ49e6JNmzYYOHAgUlNTMWvWLDx//hzTp08v8PHfN2XKFERGRqJ+/foYPnw4nJyc8PbtW9y+fRt79+7FsmXL8hxO1aZNG0ydOhWTJk1Cw4YNERsbiylTpsDR0VFhmnoTExPY29tj586daNKkCSwtLeXX7UPm5uaYMGECfvzxR/j5+aF79+54+vQpJk+eDH19fUyaNOmTzjc/ZcuWRUREhNJ6bdq0QVhYGJydnVG9enVcuHABs2bNytFOlSpVgoGBATZu3AgXFxcYGxujbNmy+XZoc/Pq1St07doVjo6OWLJkCfT09LBlyxZ4enqiT58+KsX8oWbNmqFFixYYO3YsUlJS4OPjg7/++guTJk1CzZo10atXrwLvMz+mpqb46aef8P333+dYV7t2bTg5OWH06NHIyMiAhYUFduzYgZMnTxZqDMC7e9EXX3yBL7/8Et999x0cHBzw4sUL3Lx5E7t375Y/C1dQixcvRtu2bVGvXj18//33qFChAuLj47F///4cf/h5nzrvBb169cL//vc/DB48GJ06dcKdO3cwc+ZMyd6zl5ycjMaNG6NHjx5wdnaGiYkJzp07h3379qFjx46SxERE/2Hmi0gNQkNDsWjRIhw/fhzdunXD119/jYkTJ+LVq1eoU6cOAEBXVxe7d+9G1apVMXDgQHTv3h2PHj3K9b0sjRo1QmBgIHbv3o0vvvgCtWvXxoULFwol1l9//RWNGzfGmDFj0LFjR5w/fx6RkZE5nmXLtmrVKqSlpaFbt24YPnw4atWqhaNHj+b6/Mj7GjZsiMOHD8PIyAj+/v7o1q0bkpOTsWvXLvj6+n50/HXq1MGVK1fQt29fbNmyBe3bt0eLFi0wY8YMODs7K7wrq0ePHoiIiMDTp0/h6+uLPn36wNTUFEeOHMkxcUpB2dra4vz582jevDlmzZqFli1bolevXli9ejU8PDzyHcb0008/YdSoUVi1ahW+/vprrFy5EsuWLcs1plWrVsHQ0BDffPMNateuLX/fVW4CAwOxcuVKXL58Ge3bt8fQoUPh5uaGU6dOoUqVKp90voVh/vz5+N///ofg4GC0bdsWu3btwvbt23O8HNnQ0BCrV6/G06dP0bx5c9SuXRvLly8v8PEGDRqE+Ph4bN26FUZGRgCAihUrYuXKldi5cyfmzZtX4H0KgoCIiAgEBARgzZo1aN26NWbPno1evXrh8OHDuQ49/lSDBw/OMawPeDeBze7du+Hs7IxBgwbBz88PMpksx3vNCoOrqysuXryIatWqYfz48WjevDn69euHbdu2fVK2r0WLFjh+/DhsbW0xfPhwtGzZElOmTIG1tbXSbdV1L+jRowdmzpyJ/fv3o02bNli6dCmWLl36Udn7wqCvr4+6deti/fr16NmzJ1q1aoWVK1di7NixanuxORHlTRDfz7ETEakoLCwMffr0wblz51CrVi2pwyEiIiIq9pj5IiIiIiIiUgN2voiIiIiIiNSAww6JiIiIiIjUgJkvIiIiIiIq1o4fP462bduibNmy8smNlDl27Bi8vLygr6+PihUrYtmyZTnqhIeHw9XVFTKZDK6urtixY0cRRP8fdr6IiIiIiKhYe/XqFWrUqKHyjK23bt1C69at8eWXX+LSpUv48ccfMXz4cISHh8vrREVFwdfXF7169cLly5fRq1cvdO3aFWfOnCmq0+CwQyIiIiIiKjkEQcCOHTvQvn37POuMHTsWu3btQkxMjLxs0KBBuHz5MqKiogAAvr6+SElJwR9//CGv07JlS1hYWGDTpk1FEjszX0REREREpFapqalISUlRWFJTUwtt/1FRUWjevLlCWYsWLXD+/Hmkp6fnW+fUqVOFFseHdIpsz6QWv+s6SR2CUlWvHZA6BKW0kSF1CJ+NrBLwN53nGeZSh6CSUtqPpQ5BqQxBV+oQlNIR06UOQSUloS1LAkEoGQN6tLOK/393MrWK/8/EpHRLqUNQSW0nc6lDyJWUvyPP/dQdkydPViibNGkSgoKCCmX/iYmJOV7Ibm1tjYyMDDx58gS2trZ51klMTCyUGHJT/P9VERERERHRZyUwMBABAQEKZTKZrFCPIQiCwufsp63eL8+tzodlhYmdLyIiIiIiUiuZTFbona332djY5MhgPXr0CDo6OrCyssq3zofZsMJU/McHERERERFRoRN0BcmWoubt7Y3IyEiFsgMHDqBWrVrQ1dXNt079+vWLLC5mvoiIiIiIqFh7+fIlbt68Kf9869YtREdHw9LSEhUqVEBgYCDu37+PdevWAXg3s+GiRYsQEBCA/v37IyoqCqtWrVKYxXDEiBFo0KABZsyYgXbt2mHnzp04ePAgTp48WWTnwc4XEREREZEG0tIp+gxUYTl//jwaN24s/5z9vFjv3r0RFhaGhIQExMfHy9c7Ojpi7969+P7777F48WKULVsWCxYsQKdOneR16tevj99++w3jx4/HhAkTUKlSJWzevBl169YtsvPge75KOM52WDg422Hh4WyHhYezHRYOznaoWTjbYeHhbIeFp7jOdrjP1EWyY7dMiVFe6TNU/P9VERERERFRoRN0i/8fTD83bHEiIiIiIiI1YOeLiIiIiIhIDTjskIiIiIhIA5WkCTc+F8x8ERERERERqQEzX0REREREGkgdLzsmRcx8ERERERERqQE7X2rSqFEjjBw5Ms/1Dg4OmDdvntriISIiIiIi9eKwQzXZvn07dHXV9wJNyy9qoeKofjDzrAb9smVwvtNgPNx1KP9tvqwN19njYOxaBakPHiEuZCXil/+mUMemQ3NUDRoBw0oV8DouHrET5+LhzoOfFKsoivh143rs37cXL1++RFUnZ3w3eCjs7R3y3e7PkyewYf1aJCQkwNbWFr16+6N+/S/k67ds3oSoU3/i3r270NPTg4uLK/z7fovy5e0KHOOePXuwLTwcSUlJsLe3x8ABA1CtWrU86/915QpWrFiBO3fuwMrKCp07dcLXX3+tUOfkyZNYt369PP7evXvDp379AsdWlHHeuXMH69evx42bN/Ho0SMMGDAAHdq3/6QY313vDdi37w+8fPkSTk5O+G7wEBWu90msX79O3l5+vXujfn0f+fq/r1xBePg23Lx5A0lJSRg/fiK8P6E9RVHE9k0rcfjATrx6+QKVq7rCf9APKF+hYp7b3Iv/F9s2LsetuGt48igR/+s3Eq3adVOoc3BvOA7+sR2PHyUAAMpXqIgO3frCw6vgse7e8zu2bt+OpKRnsK9QAYMG9Id7Nbc86/915QpCV6zCnfh4WFlaokvnTmjTupV8/d59+3Hw8GHcuX0HAFC5cmX06e0HZ6eqBY4tW0m43iWhHYGS0ZYlIcbsODdu3Ih9f/wX5+AhQ2Bvb5/vdidPnsT6desU7tv1fXwU6uzZswfh27bJ78MDBg7M9z6cl5LwvSwJ7Zgd5/ZNK3HkQARevXyBSlXdVLqfh28Mxa24WDx5lID/9RuJlu2651l/19YwbFm/FC3a+qJX/4CPilMKnHBD/Zj5UhNLS0uYmJio7XjaRoZI+SsW/4yYolJ9A4fyqL17OZJOXsDJ2u1xc8YyuM39CTYdmsvrmNfzQM1f5+L+xp044dUO9zfuhOemeTCvU/2TYg3ftgURO7Zj0HdDMWfeQlhYWGDCT+Pw+vXrPLeJibmKGdN/RuOvmmDh4qVo/FUTzAj+GbHX/ntb+t9/X8HXbb7B7DnzMfXn6cjMzMKEnwLx9u2bAsV37NgxhC5fjm6+vli0cCHc3NwwYeJEPHr0KNf6iYmJmDhxItzc3LBo4UL4du2KZaGhOHny5HvxxyB4+nQ0+eorLFm8GE2++grBwcG4du1agWIr6jjfpqbCxtYWffr0gYWFxUfH9r5t27Zix44dGPTdYMydtwAWFpYY/9OPSq/39Om/4KuvvsKixUvw1VdfYXrwLwrt9fbtWzg6OmLQd4MLJc4929dj785N8B8wClNDVsPMwgrBE4fjzetXeW6TmvoWZWzKoZvfEJhbWOVax7JUGXTrPQTT5oRh2pwwuFX3wpyfx+Be/L8Fiu/o8RNYtmIluvt2xZIF81GtmhvGTwrK93qPnzQZ1aq5YcmC+ejm2wVLQ5fjxJ9/yuv8deUKGjdogJnBv2BuyCyUKVMaP06YiCdPnhYotvcV9+tdUtoRKP5tWVJiBIBtW7dix/bt+G7wYMybPx8WFhb46UdlccZgenAwvmrSBIuXLMFXTZrkuG8fO3YMy0ND4dutGxYuWgQ3NzdMnDAhz+9TXkrK97K4t2O2PdvX44+dv6L3gNGYErIG5haWmD5xmNL7eWmbcvD1GwyzPO7n2eJuXMWR/RGo4FD5o+IjzcLOl5q8P+zw0aNHaNu2LQwMDODo6IiNGzcW+vEe7z+O65PmITEiUqX69gO64W18Aq6O+gUvr/2Lu6u34W7YdlQM6Cuv4zisN54cPIW4mcvxKvZfxM1cjieHT8NhWO+PjlMUReyM2AHfbt1R3+cLODg4ImDUD0hNTcWxo4fz3G5XxA7UrOmJrr7dYWdXAV19u6OGR03s3LlDXmfK1F/QtFlz2Ns7oGLFShgZMAqPHz/CzRs3ChTjjh070Lx5c7Rs2RIVKlTAoIEDUbp0afz++++51v99716UKVMGgwYORIUKFdCyZUs0b9YM4du3y+tERETAs2ZN+Pr6ws7ODr6+vvDw8EDEzp0Fiq2o43SqWhXf9uuHRg0bFkrm9r/r3Q0+Pl/AwcEBAaNG/f/1PpLndjsjIv7/eneDnZ0duvp2Qw0PD4XrXat2bfj19oePzxd57qcgce7btRntu/qjdv3GsLOvhEEjJyIt9S1OHT+Q53aVqriiR59h8G7QDDp5tJdnnS/hUas+bMtVgG25Cuja6zvo6xvi5rW/CxTj9h0RaNG8GVq1aIEKFezw3YD+KF2qFPbs/SPX+nv27kOZ0qXx3YD+qFDBDq1atEDzZk0Rvv2/Nhz3w2i0bfM1KlWqiAp2dhg5bCjErCxcuny5QLFlKwnXuyS0I1Ay2rIkxJgdZ0REBLp16wYfHx84ODhg1P/HefTo0Ty3i4iIQE1Pzxz37Z0REfI6H96HBw4alO99OC8l4XtZEtoxO859u35Du6595PfzgSMn/f/9fH+e2727nw+Hd4Pm0NXVy7Pe2zevsTRkIvoN/RGGxqYFjk9qgq4g2aKp2PmSgL+/P27fvo3Dhw9j27ZtWLJkyUf/NaewmNfzwOODfyqUPT5wAmZe1SDovBudalHPA08OnlSo8yTyBCy8a370cR8mJuLZsyTU9PSSl+nq6qGae3XExFzNc7tr164qbAMAnp5eiLma9zavXr37C5dxATKQ6enpuHHzJjw9PRWPVbMmrsbE5LrNtZgYeNZUbBNPLy/cuHEDGRkZAICYa9dy7NPL0zPf+KWIs7AlJibi2bNnCnG+u97uiMkjTgC4di0GNT88N08vxFzNe5tP8fjhAzx/9hTuHnUV4nR2q4kbMVcK7ThZmZmIOh6J1LdvUNnZXeXtsq+31wfXz8sz7+sdc+0avDwV69fy9MT1GzfzvN6pqanIyMyEiYmxyrG9r7hf75LSjkDxb8uSEmOecerpwd3dPd978LWYmJz3WC8v+XclPT0dN2/cyFGnZgHv7SXle1nc2zHb44cPkFyE9/OwZbPgUcsH1TzqfPK+SDPwmS81u379Ov744w+cPn0adeu+uxGsWrUKLi4uksYlsy6F1IdPFMrSHj2Flq4u9EpZIDXxMWQ2pZD6UHF4QurDp5DZlP7o4z57lgQAMDdXHNJmbm6eb4f02bNnuWxjgWfPnuVaXxRFrFwRCle3anBwcFQ5vpSUFGRlZcHC3FzxWBZ5H+vZs2cw/2CInoW5OTIzM5GSkgJLS8v/j/+DfZqbIymPfUoVZ2HLjiW3a/f40cN8t7Mw/zDWvM/tUz1/9u57bmau2AZm5pZ48jjxk/cff/smgsb0R3paGvQNDPD9jzNQvkLBv5e5fYeePXue6zZ5fecyMzORnJICq1yu9+qwtbCysoKnh4fKsX14zHfHKZ7Xu6S0Y/Zx3x2reLZlSYkx+3gActz/VPvvjnmObZ4lvfvvmPz7lMt9tSDnUlK+l8W9HbMV5f086vgB3P43FlNC1nzSfkizsPOlZjExMdDR0UGtWrXkZc7OzjluRLlJTU1FamqqQlm6mAVdoZASmKKo+FkQcpbnVufDsnwcOXIIixfOl3+eNHmawqHeP4zwYeEHPlwvQsyxn2zLlizC7Vu3MHP2HJVjzfdYophvfB+uEXNpo5zxKz9nZYoizk9x5MhhLFq4QP45aPK7ZxBzhPTugue/sw+/I/lc74L68+g+rFoyQ/75h4kh/3/MXL5jOVqt4MqWs8cv89bh9auXOHvqCJbNm4LxvywtUAfsXXg5r3d+4eX8Lry73rmd05Zt4Thy7DhmTf8Fenp5D7l5X0m53jkOVczaESgZbVkSYgSAI4cPY+HChfLPkydPfnfIj7gH51ifyz22oPdhVY8l9feypLTjn0f3YfWS6fLPoyfOyd5hjjjzbVAlnj5+iPUr5mDslAXQ05N99H6kxgk31I+dLzXL/nH7MTfi4OBg+c0uW3fBEj21S31yXKkPn+TIYOmVtkRWejrSnj5/VyfxCWQ2iseSlbHMkTHLT9263nBycpZ/Tk9PB/DuL2GWlv890Jqc/DzfDqmFhYU8aybf5vnzHH9xBYBlSxfjzJkoTJ8ZglKlCpalMzU1hZaWVo6M1Ltj5R6fRS7ZpufJydDW1oapqWmedZKfP8+RuZI6zk9Vt269D653GoCc1/t58vMcf/lWGmse1/tjeNb5EpWq/jeLWEbGu+9l8rOnsLD87zuf8vxZjr+efgwdXV3YlH0362bFKi749+ZV7N+9Gf2GjFNp++zrneM7lJyc53co9zbMvt6KQ3G3hm/Hb1u2YvrPU1HRUfUOYUm53tmKazsCJaMtS0KMAFC3Xj04Oefy352kJIUMf373yzzjTE6WZ2jk36ekpJx1CnBvL67fy5LSjqrfz5M+6X5+K+4aUpKfYcL3/vKyrKxMxP5zCZG/b0NY+AloaWt/9P7p88VnvtTMxcUFGRkZOH/+vLwsNjYWz58/V7ptYGAgkpOTFZauWoUzNOz56WiUaqI4bW/pZl8g+cLfEP9/vPiz09Eo1URxKthSTb/As6hLKh/H0NAQZcuWky8VKtjDwsISly5elNdJT0/H31f+gouLa577cXZ2xaVLFxXKLl28ABfX/7YRRRFLlyzCqVMn8XPwLNjY2KocZzZdXV1UqVwZly4pnuPFS5fgmsdQUWcXF1z8sP7Fi6hSpQp0/v/5ORdn51zrvB9/cYjzU7273mXly7vrbYFLF/877rvrfSXfobfOzi6I/iDWSxcvwsW1cIbrGhgawaasnXwpZ+cIcwsrXIk+K6+TkZ6Oa/9cQhUX1Z/NUpn43w9XVWRf7xzX71J0ntf73XcuWqHswqVLqFqlssL13hq+Hb/+thk/TwlC1SpVVI4JKDnXO1txbUegZLRlSYgx9zgrwMLCQuG6p6en48qVK/neg51dXBT+WwW8u2dmf1d0dXVRuUqVHPfhSwW8txfX72VJacfc7udmFlb4u5Dv527VayF44a/4ef56+eJY2QX1G7bAz/PXl5iOl6AtSLZoKna+1MzJyQktW7ZE//79cebMGVy4cAHffvstDAwMlG4rk8lgamqqsOQ15FDbyBCmNZxhWuPdX6kMHcvDtIYz9O3edUCcpgWgxpr/hlndWf4bDOzLwmXWOBg7V0R5/06w69MJ/85ZLa9ze9E6lGrmg4qj+8PIqSIqju6PUk28cXvh2o9uD0EQ0K59B2zdsgmnTp3E7du3MG/ObMhkMjRs9JW8XsjsmQhbs0r++Zt27XHp4gVs27oZd+/GY9vWzYiOvoR27TrI6yxdshBHjxzCD2MCYWhggGdJSXiWlJRj6KYyHTp0wP79+7H/wAHEx8cjdPlyPH78GK1btwYArFmzBrNnz5bX/7p1azx69AjLly9HfHw89h84gAMHDqBTx47yOu3atcPFixexZetW3L17F1u2bsWl6Gi0b9euwG1YlHGmp6cjLi4OcXFxyMjIwNOnTxEXF4cHDx58VIzZ13vLlt9w6tSfuH37NubOCfn/691YXi9k9iyErfnvu/dNu/a4ePECtm7dgrt372Lr1i05rvebN2/ksQJA4sNExMXFfdRkNoIgoOU3vti1bS3ORR3F3TtxWDZ/KvRk+qjf4L/XLyydOxm/rV0i/5yRno7b/17H7X+vIyMjA8+SHuP2v9eR+OCuvM7mdUtx7Z9oPH74APG3b2LL+qW4+vdF+DRsUaAYO3Zoj30HIrH/QCTi4+9i2fIVePT4Mb7+//f6rA5bi5kh/w2zbdO6JR4+eoTQFSsRH38X+/9/204d/2vDLdvCsXbdegSMHA7rMtZISnqGpKRnePOmYK9nyFYSrndJaEegZLRlSYgxO8727dtjy+bNOPXnuzjnzJkDmUyGRo0ayevNnj0ba9b89yxP9n1765b/j3PLFkRfuoR27737MPs+fGD/fsTHx2N5aKjCfVhVJeF7WRLaMTvOlt90w65tYfL7eej8Kf9/P//vvrtsbhA2r10s/5yRno47/17HnX+vIyMjHUlJj3Hnvfu5gaER7OwrKSwyfQMYm5jBzr5SgeMkzcFhhxJYs2YNvv32WzRs2BDW1taYNm0aJkyYUKjHMPOqBu9D6+WfXWf/CAC4u247/uoXCJltaRjY/ZcJenP7Hs61HQDXkEDYf9cTqQ8e4Z/vf0bijv+m1X4WdQmXegbAafJIOE0ejtdxd3Gpx/d4fvavT4q1U+euSE1NxdLFi/Dy5Qs4OTljyrRgGBoayus8fvwIWlr//ZXExdUNY8b9iA3rwrBh/VrY2Npi7Lif4OT8318F9/6+BwAQOHa0wvFGfj8aTZs1h6oaNmyIFy9e4Ndff0VSUhIcHBwwZfJkWFtbAwCSnj3Do8eP5fVtbGwwZcoULF++HLv37IGVlRUGDRyIL774b4pkV1dXjBs3DuvWrcP69etha2uLwHHj4PzekI6CKoo4k5KSMHTYMPnn8PBwhIeHw93dHTNn/Nd5L4jOnbsgLTUVSxYv+v+Xcjpj6rRfclxv4b3r7erqirHjArF+3VpsWL/u/693oEJ73bhxHYHjxso/r1yxHADQpGlTBAQofgdU0aZjL6SlpiJs2Sz5SznHTZ4PA0MjeZ2njxMVhhA/S3qMn0b6yT//vmMjft+xES7VamL8L0sBAMnPk7B0bhCeJz2FoZEx7BwqYeykuXCv+d9MXKpo1OBLvEhJwcZNv8lfQjpt8iRYlykD4N21e/zB9Z42eRJCV6zE7j2/w9LKEt8NHIAv33ux6Z7f9yI9IwPTfpmucKz/9eiOXj17FCi+bMX9epeUdgSKf1uWlBgBoHOXLkhNS8PixYvlLwee9vPPinE+egQtQTHOD+/b4wIV48ztPjx5yhT5fVhVJeV7WdzbMdt/9/OZeP3/9/Oxkxco3M+fPH4I4b0/aL+7n/eSf967YyP27tgI52qe8vv550BLgzNQUhHEwn7CntTqd10nqUNQquq1vN+LVFxoo2imVtdEWSUgof48w1zqEFRSSvux8koSyxA+/f1vRU1HTJc6BJWUhLYsCQShZPys0c4q/v/dydQq/n+jT0ov/Jl5i0JtJ3OpQ8jVyRqeyisVkS8uX1Re6TNU/H8lERERERERfQaK/580iIiIiIio0L0/BJjUg5kvIiIiIiIiNWDmi4iIiIhIAwnazMOoG1uciIiIiIhIDdj5IiIiIiIiUgMOOyQiIiIi0kB8z5f6MfNFRERERESkBsx8ERERERFpIE41r37MfBEREREREakBM19ERERERBqIz3ypHzNfREREREREasDOFxERERERkRpw2CERERERkQYSOOxQ7Zj5IiIiIiIiUgNmvkq4qtcOSB2CUtedm0sdglJN9v0kdQgqybj2t9QhKPX82m2pQ1DKbvBYqUNQicmT21KHoNTbI8X/HvS66zCpQ1CJSdoTqUNQ6pGBvdQhKOV465DUIagk0b6e1CEoZbV1jtQhKGXUrp/UIaioptQB5ErQYh5G3djiREREREREasDOFxERERERkRpw2CERERERkQYStDjhhrox80VERERERKQGzHwREREREWkgLU41r3bMfBEREREREakBM19ERERERBqIz3ypHzNfREREREREasDOFxERERERkRpw2CERERERkQYStJiHUTe2OBERERERkRow80VEREREpIE44Yb6MfNFRERERESkBux8FZLbt29DEARER0dLHQoRERERERVDHHZYSOzs7JCQkIBSpUpJHQoRERERkVJa2hx2qG7sfBWCtLQ06OnpwcbGRupQiIiIiIiomOKww1w0atQIQ4cOxdChQ2Fubg4rKyuMHz8eoigCABwcHDBt2jT4+/vDzMwM/fv3z3XY4T///IOvv/4apqamMDExwZdffom4uDj5+jVr1sDFxQX6+vpwdnbGkiVL1H2qRERERKShBC1BskVTMfOVh7Vr16Jfv344c+YMzp8/jwEDBsDe3h79+/cHAMyaNQsTJkzA+PHjc93+/v37aNCgARo1aoTDhw/D1NQUf/75JzIyMgAAK1aswKRJk7Bo0SLUrFkTly5dQv/+/WFkZITevXur7TyJiIiIiEg92PnKg52dHebOnQtBEODk5IQrV65g7ty58s7XV199hdGjR8vr3759W2H7xYsXw8zMDL/99ht0dXUBAFWrVpWvnzp1KkJCQtCxY0cAgKOjI65evYrQ0FB2voiIiIioyPEly+rHzlce6tWrB0H4LyXq7e2NkJAQZGZmAgBq1aqV7/bR0dH48ssv5R2v9z1+/Bh3795Fv3795J05AMjIyICZmVme+0xNTUVqaqpCWVpqKvRkMpXOiYiIiIiIpMPO10cyMjLKd72BgUGe67KysgC8G3pYt25dhXXa2tp5bhccHIzJkycrlA0dNgLDR3yvLFwiIiIiIpIYO195OH36dI7PVapUybdz9L7q1atj7dq1SE9Pz5H9sra2Rrly5fDvv/+iZ8+eKscUGBiIgIAAhbK79xJV3p6IiIiIKJsmT3whFQ70zMPdu3cREBCA2NhYbNq0CQsXLsSIESNU3n7o0KFISUlBt27dcP78edy4cQPr169HbGwsACAoKAjBwcGYP38+rl+/jitXrmDNmjWYM2dOnvuUyWQwNTVVWDjkkIiIiIioZGDmKw9+fn548+YN6tSpA21tbQwbNgwDBgxQeXsrKyscPnwYP/zwAxo2bAhtbW14eHjAx8cHAPDtt9/C0NAQs2bNwpgxY2BkZAR3d3eMHDmyiM6IiIiIiOg/zHypHztfedDV1cW8efOwdOnSHOs+nNkQePfur+z3gGWrXr069u/fn+cxevTogR49enxyrEREREREVPxx2CEREREREZEaMPNFRERERKSBOOxQ/dj5ysXRo0elDoGIiIiIiD4z7HwREREREWkgQYtPIKkbW5yIiIiIiEgNmPkiIiIiItJAWtp85kvdmPkiIiIiIqJib8mSJXB0dIS+vj68vLxw4sSJPOv6+/tDEIQci5ubm7xOWFhYrnXevn1bZOfAzhcRERERERVrmzdvxsiRI/HTTz/h0qVL+PLLL9GqVSvEx8fnWn/+/PlISEiQL3fv3oWlpSW6dOmiUM/U1FShXkJCAvT19YvsPDjskIiIiIhIA5WkqebnzJmDfv364dtvvwUAzJs3D/v378fSpUsRHByco76ZmRnMzMzknyMiIvDs2TP06dNHoZ4gCLCxsSna4N/DzBcREREREalVamoqUlJSFJbU1NRc66alpeHChQto3ry5Qnnz5s1x6tQplY63atUqNG3aFPb29grlL1++hL29PcqXL482bdrg0qVLH3dCKmLni4iIiIhIAwlaWpItwcHB8uxU9pJbBgsAnjx5gszMTFhbWyuUW1tbIzExUel5JiQk4I8//pBnzbI5OzsjLCwMu3btwqZNm6Cvrw8fHx/cuHHj4xtVCQ47JCIiIiIitQoMDERAQIBCmUwmy3cbQVAcJimKYo6y3ISFhcHc3Bzt27dXKK9Xrx7q1asn/+zj4wNPT08sXLgQCxYsULrfj8HOFxERERERqZVMJlPa2cpWqlQpaGtr58hyPXr0KEc27EOiKGL16tXo1asX9PT08q2rpaWF2rVrF2nmi8MOiYiIiIg0kKAlSLYUhJ6eHry8vBAZGalQHhkZifr16+e77bFjx3Dz5k3069dP6XFEUUR0dDRsbW0LFF9BMPNFRERERETFWkBAAHr16oVatWrB29sby5cvR3x8PAYNGgTg3TDG+/fvY926dQrbrVq1CnXr1kW1atVy7HPy5MmoV68eqlSpgpSUFCxYsADR0dFYvHhxkZ0HO18lnDYypA5BqSb7fpI6BKUOtfxZ6hBU0nBhR6lDUMq4fBmpQ1AqRcdQ6hBUom9gprySxPTLqm963o8lvHkqdQgqSTYqur+0FpbkdGOpQ1DqnsOXUoegEos3CVKHoJR+lSpSh6BUcgm5nxdXJWmqeV9fXzx9+hRTpkxBQkICqlWrhr1798pnL0xISMjxzq/k5GSEh4dj/vz5ue7z+fPnGDBgABITE2FmZoaaNWvi+PHjqFOnTpGdBztfRERERERU7A0ePBiDBw/OdV1YWFiOMjMzM7x+/TrP/c2dOxdz584trPBUws4XEREREZEGErQ4/YO6scWJiIiIiIjUgJ0vIiIiIiIiNeCwQyIiIiIiDVSSJtz4XDDzRUREREREpAbMfBERERERaSBOuKF+bHEiIiIiIiI1YOeLiIiIiIhIDTjskIiIiIhIEwmccEPdmPkiIiIiIiJSA2a+iIiIiIg0EKeaVz9mvoiIiIiIiNSg2HS+GjVqhJEjRxbqPsPCwmBubl6o+yQiIiIiIvoYHHZIRERERKSB+J4v9dPoFk9PT5c6BCIiIiIi0hDFqvOVkZGBoUOHwtzcHFZWVhg/fjxEUQQApKWlYcyYMShXrhyMjIxQt25dHD16VGH7sLAwVKhQAYaGhujQoQOePn2qsD4oKAgeHh5YvXo1KlasCJlMBlEUER8fj3bt2sHY2Bimpqbo2rUrHj58qLDt0qVLUalSJejp6cHJyQnr169XWC8IAkJDQ9GmTRsYGhrCxcUFUVFRuHnzJho1agQjIyN4e3sjLi5Ovs3ly5fRuHFjmJiYwNTUFF5eXjh//nwhtigRERERUe4ELUGyRVMVq87X2rVroaOjgzNnzmDBggWYO3cuVq5cCQDo06cP/vzzT/z222/466+/0KVLF7Rs2RI3btwAAJw5cwZ9+/bF4MGDER0djcaNG2PatGk5jnHz5k1s2bIF4eHhiI6OBgC0b98eSUlJOHbsGCIjIxEXFwdfX1/5Njt27MCIESMwatQo/P333xg4cCD69OmDI0eOKOx76tSp8PPzQ3R0NJydndGjRw8MHDgQgYGB8k7V0KFD5fV79uyJ8uXL49y5c7hw4QLGjRsHXV3dQm1TIiIiIiIqHorVM192dnaYO3cuBEGAk5MTrly5grlz5+Krr77Cpk2bcO/ePZQtWxYAMHr0aOzbtw9r1qzBL7/8gvnz56NFixYYN24cAKBq1ao4deoU9u3bp3CMtLQ0rF+/HqVLlwYAREZG4q+//sKtW7dgZ2cHAFi/fj3c3Nxw7tw51K5dG7Nnz4a/vz8GDx4MAAgICMDp06cxe/ZsNG7cWL7vPn36oGvXrgCAsWPHwtvbGxMmTECLFi0AACNGjECfPn3k9ePj4/HDDz/A2dkZAFClSpVCb1MiIiIiotzwmS/1K1YtXq9ePQjvvWnb29sbN27cwPnz5yGKIqpWrQpjY2P5cuzYMfkwvpiYGHh7eyvs78PPAGBvby/veGVvZ2dnJ+94AYCrqyvMzc0RExMjr+Pj46OwHx8fH/n6bNWrV5f/f2trawCAu7u7Qtnbt2+RkpIC4F0n7ttvv0XTpk0xffp0hSGJuUlNTUVKSorCkpqamu82RERERERUPBSrzld+tLW1ceHCBURHR8uXmJgYzJ8/HwDkz4YpY2RkpPBZFEWFDl9e5R/WyW2794cMZq/LrSwrKwvAu2fQ/vnnH3z99dc4fPgwXF1dsWPHjjxjDw4OhpmZmcKybNmyfM+XiIiIiIiKh2LV+Tp9+nSOz1WqVEHNmjWRmZmJR48eoXLlygqLjY0NgHfZqty2V8bV1RXx8fG4e/euvOzq1atITk6Gi4sLAMDFxQUnT55U2O7UqVPy9Z+iatWq+P7773HgwAF07NgRa9asybNuYGAgkpOTFZZBgwZ9cgxEREREpHk44Yb6Fatnvu7evYuAgAAMHDgQFy9exMKFCxESEoKqVauiZ8+e8PPzQ0hICGrWrIknT57g8OHDcHd3R+vWrTF8+HDUr18fM2fORPv27XHgwIEcz3vlpmnTpqhevTp69uyJefPmISMjA4MHD0bDhg1Rq1YtAMAPP/yArl27wtPTE02aNMHu3buxfft2HDx48KPP9c2bN/jhhx/QuXNnODo64t69ezh37hw6deqU5zYymQwymUyh7MkHn4mIiIiIqHgqVpkvPz8/vHnzBnXq1MGQIUMwbNgwDBgwAACwZs0a+Pn5YdSoUXBycsI333yDM2fOyJ/VqlevHlauXImFCxfCw8MDBw4cwPjx45UeUxAEREREwMLCAg0aNEDTpk1RsWJFbN68WV6nffv2mD9/PmbNmgU3NzeEhoZizZo1aNSo0Uefq7a2Np4+fQo/Pz9UrVoVXbt2RatWrTB58uSP3icRERERkaqY+VI/QVT1YSkqlv5VMklHcVD29p9Sh6DUoZY/Sx2CShou7Ch1CEplJL+QOgSlUjoOkToElZi9uC91CErpRp+QOgSlUr2+kjoElSQb2UodglKJGdZSh6BUKd0kqUNQicWbBKlDUMro+jmpQ1DqYfWWUoegEvvKTlKHkKtHgX6SHbtM8DrJji2lYpX5IiIiIiIi+lwVq2e+iIiIiIhITfieL7VjixMREREREakBM19ERERERBoot3fdUtFi5ouIiIiIiEgNmPkiIiIiItJAAp/5Uju2OBERERERkRqw80VERERERKQGHHZIRERERKSBBC1OuKFuzHwRERERERGpATNfRERERESaiBNuqB1bnIiIiIiISA3Y+SIiIiIiIlIDDjskIiIiItJAnHBD/Zj5IiIiIiIiUgNmvqjIZVz7W+oQlGq4sKPUIajk2LDtUoeglEvPKlKHoJRRxmupQ1CJzrkjUoeg1PNr/0odglJm6RlSh6ASncbdpQ5BqbcZelKHoJSuTprUIahE9uqp1CEolfpv8f/3re/yUuoQSjRBYB5G3djiREREREREasDMFxERERGRJuIzX2rHzBcREREREZEasPNFRERERESkBhx2SERERESkgQQt5mHUjS1ORERERESkBsx8ERERERFpIL5kWf2Y+SIiIiIiIlIDdr6IiIiIiIjUgMMOiYiIiIg0kcA8jLqxxYmIiIiIiNSAmS8iIiIiIg3ECTfUj5kvIiIiIiIiNVB756tRo0YYOXJkidkvEREREdFnSUtLukVDlbgzP3r0KARBwPPnz6UOhYiIiIiISGUlrvNVmNLT06UOgYiIiIiINIQkna+MjAwMHToU5ubmsLKywvjx4yGKIgBgw4YNqFWrFkxMTGBjY4MePXrg0aNHAIDbt2+jcePGAAALCwsIggB/f3/5frOysjBmzBhYWlrCxsYGQUFBCscVBAHLli1Du3btYGRkhGnTpgEAli5dikqVKkFPTw9OTk5Yv369wnbx8fFo164djI2NYWpqiq5du+Lhw4fy9UFBQfDw8MDq1atRoUIFGBsb47vvvkNmZiZmzpwJGxsblClTBj///LPCfoOCglChQgXIZDKULVsWw4cPL5T2JSIiIiJSRhAEyRZNJUnna+3atdDR0cGZM2ewYMECzJ07FytXrgQApKWlYerUqbh8+TIiIiJw69YteQfLzs4O4eHhAIDY2FgkJCRg/vz5Cvs1MjLCmTNnMHPmTEyZMgWRkZEKx540aRLatWuHK1euoG/fvtixYwdGjBiBUaNG4e+//8bAgQPRp08fHDlyBAAgiiLat2+PpKQkHDt2DJGRkYiLi4Ovr6/CfuPi4vDHH39g37592LRpE1avXo2vv/4a9+7dw7FjxzBjxgyMHz8ep0+fBgBs27YNc+fORWhoKG7cuIGIiAi4u7sXSXsTEREREZH0JJlq3s7ODnPnzoUgCHBycsKVK1cwd+5c9O/fH3379pXXq1ixIhYsWIA6derg5cuXMDY2hqWlJQCgTJkyMDc3V9hv9erVMWnSJABAlSpVsGjRIhw6dAjNmjWT1+nRo4fCMXr06AF/f38MHjwYABAQEIDTp09j9uzZaNy4MQ4ePIi//voLt27dgp2dHQBg/fr1cHNzw7lz51C7dm0A77Juq1evhomJCVxdXdG4cWPExsZi79690NLSgpOTE2bMmIGjR4+iXr16iI+Ph42NDZo2bQpdXV1UqFABderUKfzGJiIiIiLKjQZPfCEVSVq8Xr16CulGb29v3LhxA5mZmbh06RLatWsHe3t7mJiYoFGjRgDeDf1Tpnr16gqfbW1t5UMWs9WqVUvhc0xMDHx8fBTKfHx8EBMTI19vZ2cn73gBgKurK8zNzeV1AMDBwQEmJibyz9bW1nB1dYXWe19qa2treTxdunTBmzdvULFiRfTv3x87duxARkZGvueXmpqKlJQUhSU1NTXfbYiIiIiIqHgoVt3dt2/fonnz5jA2NsaGDRtw7tw57NixA8C74YjK6OrqKnwWBAFZWVkKZUZGRjm2+3DcqSiK8rL3/39edfI6dn7x2NnZITY2FosXL4aBgQEGDx6MBg0a5DsJSHBwMMzMzBSWZcuW5VmfiIiIiIiKD0k6X9nPPb3/uUqVKrh27RqePHmC6dOn48svv4Szs3OOzJWenh4AIDMzs1BicXFxwcmTJxXKTp06BRcXFwDvslzx8fG4e/eufP3Vq1eRnJwsr/OxDAwM8M0332DBggU4evQooqKicOXKlTzrBwYGIjk5WWEZNGjQJ8VARERERJpJ0BIkWzSVJM983b17FwEBARg4cCAuXryIhQsXIiQkBBUqVICenh4WLlyIQYMG4e+//8bUqVMVtrW3t4cgCNizZw9at24NAwMDGBsbf3QsP/zwA7p27QpPT080adIEu3fvxvbt23Hw4EEAQNOmTVG9enX07NkT8+bNQ0ZGBgYPHoyGDRvmGMJYEGFhYcjMzETdunVhaGiI9evXw8DAAPb29nluI5PJIJPJFMqefPCZiIiIiIiKJ0kyX35+fnjz5g3q1KmDIUOGYNiwYRgwYABKly6NsLAwbN26Fa6urpg+fTpmz56tsG25cuUwefJkjBs3DtbW1hg6dOgnxdK+fXvMnz8fs2bNgpubG0JDQ7FmzRr5s2aCICAiIgIWFhZo0KABmjZtiooVK2Lz5s2fdFxzc3OsWLECPj4+qF69Og4dOoTdu3fDysrqk/ZLRERERKQSQUu6RUMJYvYLtqhE+jcuTuoQlCqzL1TqEJTT1pY6ApUcG7Zd6hCUculZReoQlDIaO1nqEFRifLz4X+/ka/9KHYJSZs4VpQ5BJc8ad5c6BKVuplWSOgSlHPWVT9BVHFg9vS51CEplnjgodQhKvf3aT+oQVGLt4iV1CLl6uWScZMc2HjxdsmNLSZJhh0REREREJDENfvZKKpqb8yMiIiIiohJjyZIlcHR0hL6+Pry8vHDixIk86x49ehSCIORYrl27plAvPDwcrq6ukMlkcHV1lc+0XlTY+SIiIiIiomJt8+bNGDlyJH766SdcunQJX375JVq1aqX0XcCxsbFISEiQL1Wq/Pd4RFRUFHx9fdGrVy9cvnwZvXr1QteuXXHmzJkiOw92voiIiIiINJAgaEm2FNScOXPQr18/fPvtt3BxccG8efNgZ2eHpUuX5rtdmTJlYGNjI1+033vOf968eWjWrBkCAwPh7OyMwMBANGnSBPPmzStwfKpi54uIiIiIiNQqNTUVKSkpCktqamquddPS0nDhwgU0b95cobx58+Y4depUvsepWbMmbG1t0aRJExw5ckRhXVRUVI59tmjRQuk+PwU7X0REREREmkhLkGwJDg6GmZmZwhIcHJxrmE+ePEFmZiasra0Vyq2trZGYmJjrNra2tli+fDnCw8Oxfft2ODk5oUmTJjh+/Li8TmJiYoH2WRg42yEREREREalVYGAgAgICFMpkMlm+2wiC4uyMoijmKMvm5OQEJycn+Wdvb2/cvXsXs2fPRoMGDT5qn4WBnS8iIiIiIlIrmUymtLOVrVSpUtDW1s6RkXr06FGOzFV+6tWrhw0bNsg/29jYfPI+C4rDDomIiIiINJCgpSXZUhB6enrw8vJCZGSkQnlkZCTq16+v8n4uXboEW1tb+Wdvb+8c+zxw4ECB9llQzHwREREREVGxFhAQgF69eqFWrVrw9vbG8uXLER8fj0GDBgF4N4zx/v37WLduHYB3Mxk6ODjAzc0NaWlp2LBhA8LDwxEeHi7f54gRI9CgQQPMmDED7dq1w86dO3Hw4EGcPHmyyM6DnS8iIiIiIk1UhM82FTZfX188ffoUU6ZMQUJCAqpVq4a9e/fC3t4eAJCQkKDwzq+0tDSMHj0a9+/fh4GBAdzc3PD777+jdevW8jr169fHb7/9hvHjx2PChAmoVKkSNm/ejLp16xbZebDzRURERERExd7gwYMxePDgXNeFhYUpfB4zZgzGjBmjdJ+dO3dG586dCyM8lbDzRURERESkiQr47BV9OrY4ERERERGRGrDzRUREREREpAYcdkhEREREpIlK0IQbnwt2vkq4rBKQvHx+7bbUIShlXL6M1CGoxKVnFalDUCpm4w2pQ1Dqi+HPpA5BJdrl7aQOQan76w9JHYJSlq1bSB2CSgSIUoeglCAU/xi1s9KlDkElQlam1CEo9fTvOKlDUKrUV0lSh0BUIOx8ERERERFpoIK+7Jg+HVuciIiIiIhIDdj5IiIiIiIiUgMOOyQiIiIi0kQC8zDqxhYnIiIiIiJSA2a+iIiIiIg0kRanmlc3Zr6IiIiIiIjUgJ0vIiIiIiIiNeCwQyIiIiIiDSRwwg21Y4sTERERERGpATNfRERERESaiBNuqB0zX0RERERERGpQ4jtfoihiwIABsLS0hCAIiI6OLvA+goKC4OHhIf/s7++P9u3bf/T2RERERETFnqAl3aKhSvyZ79u3D2FhYdizZw8SEhJQrVo1CIKAiIiIj97n/PnzERYWpnL90aNH49ChQx99PCIiIiIi+vyV+Ge+4uLiYGtri/r16xfaPs3MzApU39jYGMbGxoV2fCIiIiIi+vwUi8zXtm3b4O7uDgMDA1hZWaFp06Z49eoVMjMzERAQAHNzc1hZWWHMmDHo3bu3fEigv78/hg0bhvj4eAiCAAcHBzg4OAAAOnToIC8rqPeHHYaGhqJcuXLIyspSqPPNN9+gd+/eAPIetjh79mzY2trCysoKQ4YMQXp6urxOQkICvv76axgYGMDR0RG//vorHBwcMG/evALHS0RERERUYIIg3aKhJO98JSQkoHv37ujbty9iYmJw9OhRdOzYEaIoIiQkBKtXr8aqVatw8uRJJCUlYceOHfJt58+fjylTpqB8+fJISEjAuXPncO7cOQDAmjVr5GWfokuXLnjy5AmOHDkiL3v27Bn279+Pnj175rndkSNHEBcXhyNHjmDt2rUICwtTGMro5+eHBw8e4OjRowgPD8fy5cvx6NGjT4qViIiIiIiKL8mHHSYkJCAjIwMdO3aEvb09AMDd3R0AMG/ePAQGBqJTp04AgGXLlmH//v3ybc3MzGBiYgJtbW3Y2Ngo7Nfc3DxH2cewtLREy5Yt8euvv6JJkyYAgK1bt8LS0lL+OTcWFhZYtGgRtLW14ezsjK+//hqHDh1C//79ce3aNRw8eBDnzp1DrVq1AAArV65ElSpVPjleIiIiIiKVaEmeh9E4krd4jRo10KRJE7i7u6NLly5YsWIFnj17huTkZCQkJMDb21teV0dHR95ZUaeePXsiPDwcqampAICNGzeiW7du0NbWznMbNzc3hfW2trbyzFZsbCx0dHTg6ekpX1+5cmVYWFjkG0dqaipSUlIUluyYiIiIiIioeJO886WtrY3IyEj88ccfcHV1xcKFC+Hk5ITbt29LHZpc27ZtkZWVhd9//x13797FiRMn8L///S/fbXR1dRU+C4Igf25MFMVct8mrPFtwcDDMzMwUltBlSwtwJkREREREJBXJO1/Au46Jj48PJk+ejEuXLkFPTw+HDh2Cra0tTp8+La+XkZGBCxcuKN2frq4uMjMzCy0+AwMDdOzYERs3bsSmTZtQtWpVeHl5ffT+nJ2dkZGRgUuXLsnLbt68iefPn+e7XWBgIJKTkxWWgYO+++g4iIiIiEiD8T1faif5M19nzpzBoUOH0Lx5c5QpUwZnzpzB48eP4eLighEjRmD69OmoUqUKXFxcMGfOHKUdFABwcHDAoUOH4OPjA5lMpnQ4nyp69uyJtm3b4p9//lGa9VLG2dkZTZs2xYABA7B06VLo6upi1KhRMDAwgJDP7C8ymQwymeyDsqefFAsREREREamH5N1OU1NTHD9+HK1bt0bVqlUxfvx4hISEoFWrVhg1ahT8/Pzg7+8Pb29vmJiYoEOHDkr3GRISgsjISNjZ2aFmzZqFEudXX30FS0tLxMbGokePHp+8v3Xr1sHa2hoNGjRAhw4d0L9/f5iYmEBfX78QoiUiIiIiUkJLkG7RUIKo7EGjYsbf3x/Pnz9HRESE1KEUqnv37sHOzg4HDx7MdxbFD92Mu1WEURUO/XljpQ5BKePyZaQOQSVJMbelDkGpmI03pA5BqS/OLJY6BJXIEm5KHYJSV4LDpA5BKfdJg6QOQSVP7T9+OLu63EyvJHUISlXS/VfqEFRi8bT4//t+GLpS6hCUKjV8hNQhqMTUs5nUIeTqbcQCyY6t3364ZMeWkuTDDjXV4cOH8fLlS7i7uyMhIQFjxoyBg4MDGjRoIHVoRERERKQJNPjZK6loRIu7ubnB2Ng412Xjxo2SxJSeno4ff/wRbm5u6NChA0qXLo2jR4/mmCWRiIiIiIg+DyUu8xUWFlbgbfbu3Yv09PRc11lbW39iRB+nRYsWaNGihSTHJiIiIiIi9Stxna+PYW9vL3UIRERERETFSz6zbFPR0Ihhh0RERERERFLTiMwXERERERF9QIt5GHVjixMREREREakBO19ERERERERqwGGHRERERESaiBNuqB0zX0RERERERGrAzBcRERERkSYSmIdRN7Y4ERERERGRGjDzRURERESkiTjVvNqxxYmIiIiIiNSAnS8iIiIiIiI14LBDIiIiIiJNxKnm1U4QRVGUOgj6eOdjn0kdglJ2Wf9KHYJSqTqGUoegElnGa6lDUEr/TfH/Tp6sO0TqEFTiHPuH1CEoVfp5nNQhKJVibCt1CCpJ0zaQOgSlkjIspQ5BKZl2mtQhqMTxyRmpQ1AqzdBC6hCUerVsntQhqMRuSbjUIeTq7f5Vkh1bv0U/yY4tJWa+iIiIiIg0EaeaVzu2OBERERERkRqw80VERERERKQGHHZIRERERKSJOOGG2jHzRUREREREpAbMfBERERERaSIt5mHUjS1ORERERESkBsx8ERERERFpIJHPfKkdM19ERERERERqwM4XERERERGRGnDYIRERERGRJhKYh1E3tjgREREREZEasPP1AVEUMWDAAFhaWkIQBERHRxd4H0FBQfDw8Cj02IiIiIiICo2gJd2ioTT3zPOwb98+hIWFYc+ePUhISEC1atUgCAIiIiKK7Ji3b9/+6I4eERERERGVDHzm6wNxcXGwtbVF/fr1pQ6FiIiIiIg+I59l5mvbtm1wd3eHgYEBrKys0LRpU7x69QqZmZkICAiAubk5rKysMGbMGPTu3Rvt27cHAPj7+2PYsGGIj4+HIAhwcHCAg4MDAKBDhw7ysoLKysrClClTUL58echkMnh4eGDfvn3y9Y6OjgCAmjVrQhAENGrU6BNbgIiIiIgof6IgSLZoqs+u85WQkIDu3bujb9++iImJwdGjR9GxY0eIooiQkBCsXr0aq1atwsmTJ5GUlIQdO3bIt50/f768k5SQkIBz587h3LlzAIA1a9bIywpq/vz5CAkJwezZs/HXX3+hRYsW+Oabb3Djxg0AwNmzZwEABw8eREJCArZv314ILUFERERERMXJZzfsMCEhARkZGejYsSPs7e0BAO7u7gCAefPmITAwEJ06dQIALFu2DPv375dva2ZmBhMTE2hra8PGxkZhv+bm5jnKVDV79myMHTsW3bp1AwDMmDEDR44cwbx587B48WKULl0aAGBlZfXRxyAiIiIiKhANnvhCKp9d56tGjRpo0qQJ3N3d0aJFCzRv3hydO3eGlpYWEhIS4O3tLa+ro6ODWrVqQRTFIosnJSUFDx48gI+Pj0K5j48PLl++XKB9paamIjU1VaEsLS0VenqyT46TiIiIiIiK1mfX3dXW1kZkZCT++OMPuLq6YuHChXBycsLt27cljUv4YGyrKIo5ypQJDg6GmZmZwhIWOrcwwyQiIiIiTSEI0i0a6rPrfAHvOjo+Pj6YPHkyLl26BD09PRw6dAi2trY4ffq0vF5GRgYuXLigdH+6urrIzMz8qFhMTU1RtmxZnDx5UqH81KlTcHFxAQDo6ekBgNJjBAYGIjk5WWHxH/j9R8VFRERERFSSLFmyBI6OjtDX14eXlxdOnDiRZ93t27ejWbNmKF26NExNTeHt7a3wuBEAhIWFQRCEHMvbt2+L7Bw+u2GHZ86cwaFDh9C8eXOUKVMGZ86cwePHj+Hi4oIRI0Zg+vTpqFKlClxcXDBnzhw8f/5c6T4dHBxw6NAh+Pj4QCaTwcLCokAx/fDDD5g0aRIqVaoEDw8PrFmzBtHR0di4cSMAoEyZMjAwMMC+fftQvnx56Ovrw8zMLMd+ZDIZZDLFIYZ6eh/XKSQiIiIiKik2b96MkSNHYsmSJfDx8UFoaChatWqFq1evokKFCjnqHz9+HM2aNcMvv/wCc3NzrFmzBm3btsWZM2dQs2ZNeT1TU1PExsYqbKuvr19k5/HZdb5MTU1x/PhxzJs3DykpKbC3t0dISAhatWqFZs2aISEhAf7+/tDS0kLfvn3RoUMHJCcn57vPkJAQBAQEYMWKFShXrlyBhzAOHz4cKSkpGDVqFB49egRXV1fs2rULVapUAfDu2bMFCxZgypQpmDhxIr788kscPXr0I1uAiIiIiEgFWiVnENycOXPQr18/fPvttwDeTaS3f/9+LF26FMHBwTnqz5s3T+HzL7/8gp07d2L37t0KnS9BENQ64Z0gFuVsEyWAv78/nj9/joiICKlD+SjnY59JHYJSdln/Sh2CUqk6hlKHoBJZxmupQ1BK/03x/06erDtE6hBU4hz7h9QhKFX6eZzUISiVYmwrdQgqSdM2kDoEpZIyLKUOQSmZdprUIajE8ckZqUNQKs2wYCN9pPBq2TypQ1CJ3ZJwqUPI1es/pYtLu1abHBPJ5TbKCwDS0tJgaGiIrVu3okOHDvLyESNGIDo6GseOHVN6vKysLDg4OGDMmDEYOnQogHfDDr/99luUK1cOmZmZ8PDwwNSpUxU6Z4Wt5HR3iYiIiIio0Ej5kuXcJpLLLYMFAE+ePEFmZiasra0Vyq2trZGYmKjSuYaEhODVq1fo2rWrvMzZ2RlhYWHYtWsXNm3aBH19ffj4+MjfxVsUPrthh+rg5uaGO3fu5LouNDQUPXv2VHNEREREREQlR2BgIAICAhTKcst6ve9jZw/ftGkTgoKCsHPnTpQpU0ZeXq9ePdSrV0/+2cfHB56enli4cCEWLFigymkUmMZ3vsLCwgq8zd69e5Genp7rug975EREREREpCivIYa5KVWqFLS1tXNkuR49eqT0t/fmzZvRr18/bN26FU2bNs23rpaWFmrXrs3MV3Fjb28vdQhERERERJ9GKBlPIOnp6cHLywuRkZEKz3xFRkaiXbt2eW63adMm9O3bF5s2bcLXX3+t9DiiKCI6Ohru7u6FEndu2PkiIiIiIqJiLSAgAL169UKtWrXg7e2N5cuXIz4+HoMGDQLwbhjj/fv3sW7dOgDvOl5+fn6YP38+6tWrJ8+aGRgYyF/pNHnyZNSrVw9VqlRBSkoKFixYgOjoaCxevLjIzoOdLyIiIiIiDSSWkMwXAPj6+uLp06eYMmUKEhISUK1aNezdu1c+Ii0hIQHx8fHy+qGhocjIyMCQIUMwZMh/sxz37t1b/tjR8+fPMWDAACQmJsLMzAw1a9bE8ePHUadOnSI7D42far6k41TzhYNTzRceTjVfeDjVfOHgVPOFh1PNFx5ONV84ONX8p3l5epdkxzau941kx5YSM19ERERERJpIhZkCqXCVnFwjERERERFRCcbOFxERERERkRpw2CERERERkQYqSRNufC7Y4kRERERERGrAzBcRERERkSbihBtqx8wXERERERGRGrDzRUREREREpAYcdkhEREREpIk44YbasfNVwpXSfix1CEqZPLktdQhK6RuYSR2CSnTOHZE6BKW0y9tJHYJSzrF/SB2CSq45tZI6BKWs57WTOgSlLPX1pQ5BJYmNeksdglKG2m+kDkEp06wkqUNQSapxKalDUMrgabzUISiVNGq21CEQFQg7X0REREREGkjkhBtqx1wjERERERGRGrDzRUREREREpAYcdkhEREREpIk44YbascWJiIiIiIjUgJkvIiIiIiINJIITbqgbM19ERERERERqwMwXEREREZEGEvnMl9qxxYmIiIiIiNSAnS8iIiIiIiI14LBDIiIiIiJNxGGHascWJyIiIiIiUgN2vgCIoogBAwbA0tISgiDA3NwcI0eOlDosIiIiIqIiIwqCZIumYucLwL59+xAWFoY9e/YgISEB1apVkzokIiIiIiL6zPCZLwBxcXGwtbVF/fr1AQA6OkXfLGlpadDT0yvy4xARERERUfGg8Zkvf39/DBs2DPHx8RAEAQ4ODjnqPHv2DH5+frCwsIChoSFatWqFGzduKNQJDw+Hm5sbZDIZHBwcEBISorDewcEB06ZNg7+/P8zMzNC/f3+kpaVh6NChsLW1hb6+PhwcHBAcHFyUp0tEREREBODde76kWjSV5p75/5s/fz6mTJmC8uXLIyEhAefOnctRx9/fH+fPn8euXbsQFRUFURTRunVrpKenAwAuXLiArl27olu3brhy5QqCgoIwYcIEhIWFKexn1qxZqFatGi5cuIAJEyZgwYIF2LVrF7Zs2YLY2Fhs2LAh184fERERERGVfBo/7NDMzAwmJibQ1taGjY1NjvU3btzArl278Oeff8qHJW7cuBF2dnaIiIhAly5dMGfOHDRp0gQTJkwAAFStWhVXr17FrFmz4O/vL9/XV199hdGjR8s/x8fHo0qVKvjiiy8gCALs7e2L9mSJiIiIiLJp8MQXUtH4zJcyMTEx0NHRQd26deVlVlZWcHJyQkxMjLyOj4+PwnY+Pj64ceMGMjMz5WW1atVSqOPv74/o6Gg4OTlh+PDhOHDgQL6xpKamIiUlRWFJTU371FMkIiIiIiI1YOdLCVEU8ywX/v+vBe////y2MzIyUvjs6emJW7duYerUqXjz5g26du2Kzp075xlLcHAwzMzMFJaloaEFPSUiIiIiIj7zJQHNPXMVubq6IiMjA2fOnJGXPX36FNevX4eLi4u8zsmTJxW2O3XqFKpWrQptbe18929qagpfX1+sWLECmzdvRnh4OJKSknKtGxgYiOTkZIXlu4EDP/EMiYiIiIhIHTT+mS9lqlSpgnbt2qF///4IDQ2FiYkJxo0bh3LlyqFdu3YAgFGjRqF27dqYOnUqfH19ERUVhUWLFmHJkiX57nvu3LmwtbWFh4cHtLS0sHXrVtjY2MDc3DzX+jKZDDKZTKEsScbp6omIiIiISgJmvlSwZs0aeHl5oU2bNvD29oYoiti7dy90dXUBvBs+uGXLFvz222+oVq0aJk6ciClTpihMtpEbY2NjzJgxA7Vq1ULt2rVx+/Zt7N27F1pavCxEREREVLRECJItmkoQ83qoiUqE2zevSx2CUmUeXpE6BKXSDcykDkElOueOSB2CUtrl7aQOQan7Ls2lDkEl15xaSR2CUg3ntZM6BKW09PWlDkEliY16Sx2CUm9FA6lDUMo0K/eh+8WNQVqK1CEoZfA0XuoQlHpQvq7ySsVA5UqOUoeQqyd/R0l27FLVvCU7tpQ47JCIiIiISANp8sQXUmGLExERERERqQE7X0RERERERGrAYYdERERERJpI0NyJL6TCzBcREREREZEaMPNFRERERKSBROZh1I4tTkREREREpAbMfBERERERaSCRz3ypHTNfREREREREasDOFxERERERkRpw2CERERERkQYSBeZh1I0tTkREREREpAbMfBERERERaSARnHBD3Zj5IiIiIiIiUgN2voiIiIiIiNSAww6JiIiIiDQQJ9xQP3a+SrgMQVfqEJR6e+SA1CEopV/WRuoQVPL82r9Sh6DU/fWHpA5BKacFlaQOQSXW89pJHYJSx0bulDoEpZps6Cd1CCrREjOlDkGpOy9KSx2CUlXN3kodgkpK3TwldQhKPYzYK3UISpUZaCF1CCpylDoAKibY+SIiIiIi0kCiwAk31I25RiIiIiIiIjVg5ouIiIiISANxqnn1Y+aLiIiIiIhIDdj5IiIiIiIiUgMOOyQiIiIi0kCcal792OJERERERERqwMwXEREREZEG4oQb6sfMFxERERERkRqw80VERERERKQGHHZIRERERKSBOOGG+rHFiYiIiIiI1KBEdb5EUcSAAQNgaWkJQRAQHR1dJMdxcHDAvHnzimTfRERERETFgQhBskVTlajO1759+xAWFoY9e/YgISEB1apVgyAIiIiIkDo0IiIiIiKifJWoZ77i4uJga2uL+vXrSx0KEREREVGJxme+1E/tLb5t2za4u7vDwMAAVlZWaNq0KV69eoXMzEwEBATA3NwcVlZWGDNmDHr37o327dsDAPz9/TFs2DDEx8dDEAQ4ODjAwcEBANChQwd5mTJxcXFo164drK2tYWxsjNq1a+PgwYM56r148QI9evSAsbExypYti4ULFyqsj4+PR7t27WBsbAxTU1N07doVDx8+BADExsZCEARcu3ZNYZs5c+bAwcEBoigCAK5evYrWrVvD2NgY1tbW6NWrF548eVLAFiUiIiIi+vwtWbIEjo6O0NfXh5eXF06cOJFv/WPHjsHLywv6+vqoWLEili1blqNOeHg4XF1dIZPJ4Orqih07dhRV+ADU3PlKSEhA9+7d0bdvX8TExODo0aPo2LEjRFFESEgIVq9ejVWrVuHkyZNISkpSOPn58+djypQpKF++PBISEnDu3DmcO3cOALBmzRp5mTIvX75E69atcfDgQVy6dAktWrRA27ZtER8fr1Bv1qxZqF69Oi5evIjAwEB8//33iIyMBPDu2bP27dsjKSkJx44dQ2RkJOLi4uDr6wsAcHJygpeXFzZu3Kiwz19//RU9evSAIAhISEhAw4YN4eHhgfPnz2Pfvn14+PAhunbt+kltTERERET0udm8eTNGjhyJn376CZcuXcKXX36JVq1a5fgNn+3WrVto3bo1vvzyS1y6dAk//vgjhg8fjvDwcHmdqKgo+Pr6olevXrh8+TJ69eqFrl274syZM0V2HoKYnYZRg4sXL8LLywu3b9+Gvb29wrqyZctixIgRGDt2LAAgIyMDjo6O8PLykj/TNW/ePMybNw+3b9/+7wQEATt27JBnyD6Gm5sbvvvuOwwdOhTAuwk3XFxc8Mcff8jrdOvWDSkpKdi7dy8iIyPRqlUr3Lp1C3Z2dgDeZbHc3Nxw9uxZ1K5dG3PnzsWiRYsQFxcHALh+/TqcnJzwzz//wNXVFRMnTsSZM2ewf/9++THu3bsHOzs7xMbGomrVqirFfjPu1keft7pYbpoudQhK6Ze1kToElTy/cl3qEJS6f674fyedFkyVOgSVaEVFSh2CUsdG7pQ6BKWabOgndQgqSfRqL3UISsW8cJQ6BKWqmt2VOgSVlLuWc9RNcfMwYq/UIShlMfA7qUNQialXC6lDyNW///87VQoVK1UqUP26devC09MTS5culZe5uLigffv2CA4OzlF/7Nix2LVrF2JiYuRlgwYNwuXLlxEVFQUA8PX1RUpKisJv/pYtW8LCwgKbNm0q6CmpRK2Zrxo1aqBJkyZwd3dHly5dsGLFCjx79gzJyclISEiAt7e3vK6Ojg5q1apV6DG8evUKY8aMgaurK8zNzWFsbIxr167l6DW/H0v25+yLFxMTAzs7O3nHC4B8f9l1unXrhjt37uD06dMAgI0bN8LDwwOurq4AgAsXLuDIkSMwNjaWL87OzgAg77B9KDU1FSkpKQpLampqIbQKEREREZH6FOR3bVpaGi5cuIDmzZsrlDdv3hynTp3KdZuoqKgc9Vu0aIHz588jPT093zp57bMwqLXzpa2tjcjISPzxxx9wdXXFwoUL4eTkpJDJKmo//PADwsPD8fPPP+PEiROIjo6Gu7s70tLSlG4rCO+mxRRFUf7/3/d+ua2tLRo3boxff/0VALBp0yb873//k9fNyspC27ZtER0drbDcuHEDDRo0yPX4wcHBMDMzU1hCly3NtS4RERERUX5EQZBsye13bW4ZLAB48uQJMjMzYW1trVBubW2NxMTEXLdJTEzMtX5GRoZ8joW86uS1z8Kg9tkOBUGAj48PfHx8MHHiRNjb2+PQoUOwtbXF6dOn5R2PjIwMXLhwAZ6envnuT1dXF5mZmSof/8SJE/D390eHDh0AvHsGLLfOX3bG6v3P2ZkpV1dXxMfH4+7duwrDDpOTk+Hi4iLfpmfPnhg7diy6d++OuLg4dOvWTb7O09MT4eHhcHBwgI6OapchMDAQAQEBCmV37z1QaVsiIiIiouIit9+1Mpks320+TH7klRDJr/6H5QXd56dSa+brzJkz+OWXX3D+/HnEx8dj+/btePz4MVxcXDBixAhMnz4dO3bswLVr1zB48GA8f/5c6T4dHBxw6NAhJCYm4tmzZ0rrV65cGdu3b0d0dDQuX76MHj16ICsrK0e9P//8EzNnzsT169exePFibN26FSNGjAAANG3aFNWrV0fPnj1x8eJFnD17Fn5+fmjYsKHCUMmOHTsiJSUF3333HRo3boxy5crJ1w0ZMgRJSUno3r07zp49i3///RcHDhxA37598+xMymQymJqaKizKvqRERERERMVNQX7XlipVCtra2jkyUo8ePcqRucpmY2OTa30dHR1YWVnlWyevfRYGtXa+TE1Ncfz4cbRu3RpVq1bF+PHjERISglatWmHUqFHw8/ODv78/vL29YWJiIs9O5SckJASRkZGws7NDzZo1ldafO3cuLCwsUL9+fbRt2xYtWrTINbs2atQoXLhwATVr1sTUqVMREhKCFi3ePSyZ/WJnCwsLNGjQAE2bNkXFihWxefPmHOfbtm1bXL58GT179lRYV7ZsWfz555/IzMxEixYtUK1aNYwYMQJmZmbQ0uI7F4iIiIioaImiINlSEHp6evDy8pLPPJ4tMjIyz/f/ent756h/4MAB1KpVC7q6uvnWKcp3Cqt1tsOC8vf3x/Pnz+WzHVJOnO2wcHC2w8LD2Q4LD2c7LByc7bDwcLbDwsPZDgsHZzv8NFL+jqxcqWD3k82bN6NXr15YtmwZvL29sXz5cqxYsQL//PMP7O3tERgYiPv372PdunUA3k01X61aNQwcOBD9+/dHVFQUBg0ahE2bNqFTp04AgFOnTqFBgwb4+eef0a5dO+zcuRPjx4/HyZMnUbdu3UI/Z0CCZ76IiIiIiEh6onoHwX0SX19fPH36FFOmTEFCQgKqVauGvXv3yl9flZCQoDB7uaOjI/bu3Yvvv/8eixcvRtmyZbFgwQJ5xwsA6tevj99++w3jx4/HhAkTUKlSJWzevLnIOl7AZ9j5cnNzw507d3JdFxoammP4HxERERERFX+DBw/G4MGDc10XFhaWo6xhw4a4ePFivvvs3LkzOnfuXBjhqaRYd75ya0Rl9u7dK5+7/0NF+fAcEREREVFJIqLoZvWj3BXrztfHyE49EhERERERFSclZ6AnERERERFRCfbZZb6IiIiIiEg5DjtUP2a+iIiIiIiI1ICZLyIiIiIiDcTMl/ox80VERERERKQG7HwRERERERGpAYcdEhERERFpIA47VD9mvoiIiIiIiNSAmS8iIiIiIg0kisx8qRszX0RERERERGrAzhcREREREZEacNghEREREZEG4oQb6sfOVwmnI6ZLHYJSr7sOkzoEpYQ3T6UOQSVm6RlSh6CUZesWUoeg1BNjW6lDUImlvr7UISjVZEM/qUNQ6tD/Vkkdgkr0Lw6XOgSlkt8U/58NTw2spA5BJeVevZA6BKWsO7eTOgSlnhlZSx2CSkylDoCKjeJ/FyUiIiIiokLHzJf68ZkvIiIiIiIiNWDmi4iIiIhIAzHzpX7MfBEREREREakBO19ERERERERqwGGHREREREQaSBQ57FDdmPkiIiIiIiJSA2a+iIiIiIg0UBYn3FA7Zr6IiIiIiIjUgJ0vIiIiIiIiNeCwQyIiIiIiDcT3fKkfM19ERERERERqwM6XEo0aNcLIkSM/aR+3b9+GIAiIjo4GABw9ehSCIOD58+efHB8RERER0ccQRUGyRVOx86UGdnZ2SEhIQLVq1XJdHxYWBnNzc/UGRUREREREaqXRz3ylp6dDV1e3yI+jra0NGxubIj8OEREREZGq+MyX+n1Wma9169bBysoKqampCuWdOnWCn58fgoKC4OHhgdWrV6NixYqQyWQQRVHpfjMyMjB06FCYm5vDysoK48ePV9hOEAREREQobGNubo6wsDAAOYcdvu/o0aPo06cPkpOTIQgCBEFAUFBQQU+diIiIiIiKuc+q89WlSxdkZmZi165d8rInT55gz5496NOnDwDg5s2b2LJlC8LDw3PtDOVm7dq10NHRwZkzZ7BgwQLMnTsXK1euLJSY69evj3nz5sHU1BQJCQlISEjA6NGjC2XfRERERERUfHxWww4NDAzQo0cPrFmzBl26dAEAbNy4EeXLl0ejRo1w7NgxpKWlYf369ShdurTK+7Wzs8PcuXMhCAKcnJxw5coVzJ07F/379//kmPX09GBmZgZBEDg0kYiIiIjURpMnvpDKZ5X5AoD+/fvjwIEDuH//PgBgzZo18Pf3hyC8+3LZ29sXqOMFAPXq1ZNvDwDe3t64ceMGMjMzCy9wFaSmpiIlJUVhSU1NU2sMRERERET0cT67zlfNmjVRo0YNrFu3DhcvXsSVK1fg7+8vX29kZFToxxQEIcezY+np6YV+nODgYJiZmSksS0NDC/04RERERPT5EyFItmiqz2rYYbZvv/0Wc+fOxf3799G0aVPY2dl90v5Onz6d43OVKlWgra0NAChdujQSEhLk62/cuIHXr1+rvH89PT2VsmiBgYEICAhQKEu4G6/ycYiIiIiISDqfXeYLAHr27In79+9jxYoV6Nu37yfv7+7duwgICEBsbCw2bdqEhQsXYsSIEfL1X331FRYtWoSLFy/i/PnzGDRoUIGmsHdwcMDLly9x6NAhPHnyJM+Om0wmg6mpqcIik+l98vkREREREVHR+yw7X6ampujUqROMjY3Rvn37T96fn58f3rx5gzp16mDIkCEYNmwYBgwYIF8fEhICOzs7NGjQAD169MDo0aNhaGio8v7r16+PQYMGwdfXF6VLl8bMmTM/OWYiIiIiovyIoiDZoqkEUZUXXZVAzZo1g4uLCxYsWCB1KEXq9s3rUoeglE5W8Z8UxOjNU6lDUIne6QNSh6CUdsUqUoeg1BP7WlKHoBLLE1ukDkEpLWNjqUNQ6tD/Vkkdgkr0L16WOgSlHr+QSR2CUhUtU6QOQSXVrxTOK2uKlJml1BEo9axCTalDUImts4fUIeTq7LVkyY5dx9lMsmNL6bN75ispKQkHDhzA4cOHsWjRIqnDISIiIiIqlrKkDkADfXadL09PTzx79gwzZsyAk5NTvnXj4+Ph6uqa5/qrV6+iQoUKhR0iERERERFpoM+u83X79m2V65YtWxbR0dH5riciIiIi+hxp8rNXUvnsOl8FoaOjg8qVK0sdBhERERERaYDPcrZDIiIiIiKi4kajM19ERERERJpKBIcdqhszX0RERERERGrAzBcRERERkQbihBvqx8wXERERERGRGrDzRUREREREpAYcdkhEREREpIE44Yb6MfNFRERERESkBsx8ERERERFpoCxR6gg0DzNfREREREREasDMFxERERGRBuIzX+rHzBcREREREZEaCKIocrRnCXYz7pbUIShl8SZB6hCUeqFfSuoQVKKTlSZ1CEoJKP63lFRtQ6lD+GxoiZlSh6BU3JsKUoegkreeNaQOQamaf2+VOgSlMrR0pQ5BJSmChdQhKGWg9VrqEJQSSsjP2IqVKkkdQq6O/SPdNW7oppn/LeawQyIiIiIiDSSKHHaobhx2SEREREREpAbMfBERERERaaASMmrzs8LMFxERERERkRqw80VERERERKQGHHZIRERERKSBsvieL7Vj5ouIiIiIiEgNmPkiIiIiItJAnGpe/Zj5IiIiIiIiUgNmvoiIiIiINBCnmlc/Zr6IiIiIiOiz8OzZM/Tq1QtmZmYwMzNDr1698Pz58zzrp6enY+zYsXB3d4eRkRHKli0LPz8/PHjwQKFeo0aNIAiCwtKtW7cCx8fOFxERERERfRZ69OiB6Oho7Nu3D/v27UN0dDR69eqVZ/3Xr1/j4sWLmDBhAi5evIjt27fj+vXr+Oabb3LU7d+/PxISEuRLaGhogePjsEMiIiIiIg0kfmZTzcfExGDfvn04ffo06tatCwBYsWIFvL29ERsbCycnpxzbmJmZITIyUqFs4cKFqFOnDuLj41GhQgV5uaGhIWxsbD4pRma+igl/f3+0b99e6jCIiIiIiIpcamoqUlJSFJbU1NRP2mdUVBTMzMzkHS8AqFevHszMzHDq1CmV95OcnAxBEGBubq5QvnHjRpQqVQpubm4YPXo0Xrx4UeAY2fn6BOwwEREREVFJlSVKtwQHB8ufy8pegoODP+l8EhMTUaZMmRzlZcqUQWJiokr7ePv2LcaNG4cePXrA1NRUXt6zZ09s2rQJR48exYQJExAeHo6OHTsWOMYiG3aYnp4OXV3dAm2TlpYGPT29IoqIiIiIiIiKg8DAQAQEBCiUyWSyXOsGBQVh8uTJ+e7v3LlzAABByDmUUhTFXMs/lJ6ejm7duiErKwtLlixRWNe/f3/5/69WrRqqVKmCWrVq4eLFi/D09FS672wqZ77WrVsHKyurHOnATp06wc/PD0FBQfDw8MDq1atRsWJFyGQyiErmr2zUqBGGDh2KgIAAlCpVCs2aNQMAXL16Fa1bt4axsTGsra3Rq1cvPHnyRGG74cOHY8yYMbC0tISNjQ2CgoIU9h0fH4927drB2NgYpqam6Nq1Kx4+fAgAiI2NhSAIuHbtmsI2c+bMgYODA0RRRGZmJvr16wdHR0cYGBjAyckJ8+fPl9cNCgrC2rVrsXPnTvmMJ0ePHgUA3L9/H76+vrCwsICVlRXatWuH27dvy7fNzMxEQEAAzM3NYWVlhTFjxihtKyIiIiKiz4VMJoOpqanCklfna+jQoYiJicl3qVatGmxsbOS/99/3+PFjWFtb5xtPeno6unbtilu3biEyMlIh65UbT09P6Orq4saNG6qfNArQ+erSpQsyMzOxa9cuedmTJ0+wZ88e9OnTBwBw8+ZNbNmyBeHh4YiOjlZpv2vXroWOjg7+/PNPhIaGIiEhAQ0bNoSHhwfOnz+Pffv24eHDh+jatWuO7YyMjHDmzBnMnDkTU6ZMkT8sJ4oi2rdvj6SkJBw7dgyRkZGIi4uDr68vAMDJyQleXl7YuHGjwj5//fVX9OjRA4IgICsrC+XLl8eWLVtw9epVTJw4ET/++CO2bNkCABg9ejS6du2Kli1bymc8qV+/Pl6/fo3GjRvD2NgYx48fx8mTJ2FsbIyWLVsiLS0NABASEoLVq1dj1apVOHnyJJKSkrBjxw5VLwURERER0ScTRUGypSBKlSoFZ2fnfBd9fX14e3sjOTkZZ8+elW975swZJCcno379+nnuP7vjdePGDRw8eBBWVlZKY/rnn3+Qnp4OW1vbAp2LIBYg5TJ48GDcvn0be/fuBQDMnz8fCxYswM2bNzF58mT88ssvuH//PkqXLq3S/ho1aoTk5GRcunRJXjZx4kScOXMG+/fvl5fdu3cPdnZ2iI2NRdWqVdGoUSNkZmbixIkT8jp16tTBV199henTpyMyMhKtWrXCrVu3YGdnB+BdNs3NzQ1nz55F7dq1MXfuXCxatAhxcXEAgOvXr8PJyQn//PMPXF1dc413yJAhePjwIbZt2wbg3TNfz58/R0REhLzO6tWrMXPmTMTExMjTm2lpaTA3N0dERASaN2+OsmXLYsSIERg7diwAICMjA46OjvDy8lLY14dSU1NzZB7v3nuQ518JiguLNwlSh6DUC/1SUoegEp2sNKlDUEpA8c/ipmobSh3CZ0NLzJQ6BKXi3lRQXqkYeOtZQ+oQlKr591apQ1AqQ6tgjzxIJUWwkDoEpQy0XksdglJCCRk5VLFSJalDyNUfl9IlO3armkXzb7VVq1Z48OCBfBr4AQMGwN7eHrt375bXcXZ2RnBwMDp06ICMjAx06tQJFy9exJ49exQyZJaWltDT00NcXBw2btyI1q1bo1SpUrh69SpGjRoFAwMDnDt3Dtra2irHV6AJN/r3748DBw7g/v37AIA1a9bA399f3smwt7dXueOVrVatWgqfL1y4gCNHjsDY2Fi+ODs7A4C8owQA1atXV9jO1tYWjx49AvBumkk7Ozt5xwsAXF1dYW5ujpiYGABAt27dcOfOHZw+fRrAu9lLPDw8FDpey5YtQ61atVC6dGkYGxtjxYoViI+Pz/d8Lly4gJs3b8LExEQev6WlJd6+fYu4uDgkJycjISEB3t7e8m10dHRytENucnswMXTZUqXbERERERF9SBSlW4rKxo0b4e7ujubNm6N58+aoXr061q9fr1AnNjYWycnJAN4leXbt2oV79+7Bw8MDtra28iV7hkQ9PT0cOnQILVq0gJOTE4YPH47mzZvj4MGDBep4AQWccKNmzZqoUaMG1q1bhxYtWuDKlSsKvUgjI6MCHTy3bbKystC2bVvMmDEjR93303ofTuaRPVQQyPuhuvfLbW1t0bhxY/z666+oV68eNm3ahIEDB8rrbtmyBd9//z1CQkLg7e0NExMTzJo1C2fOnMn3fLKysnId0gigwB3TD+X2YOLdew/yqE1EREREpFksLS2xYcOGfOu8P/Ave76H/NjZ2eHYsWOFEl+BZzv89ttvMXfuXNy/fx9NmzZVyC4VBk9PT4SHh8PBwQE6Oh83GaOrqyvi4+Nx9+5dhWGHycnJcHFxkdfr2bMnxo4di+7duyMuLg7dunWTrztx4gTq16+PwYMHy8vez7wB73rBmZmKQ248PT2xefNmlClTJs8H9WxtbXH69Gk0aNAAwLthhxcuXFA6U4pMJssxxFAme5rvNkREREREucn6zF6yXBIU+D1fPXv2xP3797FixQr07du30AMaMmQIkpKS0L17d5w9exb//vsvDhw4gL59++bo6OSladOmqF69Onr27ImLFy/i7Nmz8PPzQ8OGDRWG93Xs2BEpKSn47rvv0LhxY5QrV06+rnLlyjh//jz279+P69evY8KECfIpLLM5ODjgr7/+QmxsLJ48eYL09HT07NkTpUqVQrt27XDixAncunULx44dw4gRI3Dv3j0AwIgRIzB9+nTs2LED165dw+DBg/H8+fNPbzwiIiIiIiq2Ctz5MjU1RadOnWBsbFwkLxguW7Ys/vzzT2RmZqJFixaoVq0aRowYATMzM2hpqRauIAiIiIiAhYUFGjRogKZNm6JixYrYvHlzjnNp27YtLl++jJ49eyqsGzRoEDp27AhfX1/UrVsXT58+VciCAe+egXNycpI/F/bnn3/C0NAQx48fR4UKFdCxY0e4uLigb9++ePPmjTwTNmrUKPj5+cHf318+pLFDhw6f0GpERERERFTcFWi2w2zNmjWDi4sLFixYUBQxUQHcjLsldQhKcbbDwsPZDgsHZzssPJztsPBwtsPCwdkOCw9nOyw8xXW2w90XMiQ7dluvj3u8qKQr0FknJSXhwIEDOHz4MBYtWlRUMREREREREX12CtT58vT0xLNnzzBjxgw4OTnlWzc+Pj7P92UB7ybAqFChZPw1koiIiIjoc1PQlx3TpytQ5+v27dsq1y1btiyio6PzXU9ERERERKQpimywpY6ODipXrlxUuyciIiIiIipRNPNJNyIiIiIiDZdVMuYr+awUeKp5IiIiIiIiKjhmvoiIiIiINFAJman/s8LMFxERERERkRqw80VERERERKQGHHZIRERERKSBRPA9X+rGzBcREREREZEaMPNFRERERKSBONW8+jHzRUREREREpAbMfBERERERaSBONa9+zHwRERERERGpATNfVOQeGdhLHYJSyenGUoegkrcZelKHoJQgFP8/oxmJqVKHoBJD7TdSh6DUnRelpQ5BqeQ3JeM/dQ3+3ip1CEpdqtZF6hCUanxoqtQhqCTsbkepQ1DK00XqCJQrY/hK6hBUUlHqAKjYKBn/RSIiIiIiokLFYYfqx2GHREREREREasDMFxERERGRBsoS+ZJldWPmi4iIiIiISA3Y+SIiIiIiIlIDDjskIiIiItJAnHBD/Zj5IiIiIiIiUgNmvoiIiIiINBAzX+rHzBcREREREZEaMPNFRERERKSBspj5UjtmvoiIiIiIiNSAnS8iIiIiIiI14LBDIiIiIiINJIqC1CFonM8+8xUWFgZzc3Opw0BQUBA8PDykDoOIiIiIiCTy2Xe+fH19cf36danDICIiIiIqVkRRukVTffbDDg0MDGBgYCB1GEREREREpOE+i8zX7du3IQhCjqVRo0Y5hh1mD/8LDQ2FnZ0dDA0N0aVLFzx//lxhn6tXr4abmxtkMhlsbW0xdOhQ+br4+Hi0a9cOxsbGMDU1RdeuXfHw4UOF7adPnw5ra2uYmJigX79+ePv2bY6416xZAxcXF+jr68PZ2RlLliwp1HYhIiIiIqLi47PofNnZ2SEhIUG+XLp0CVZWVmjQoEGu9W/evIktW7Zg9+7d2LdvH6KjozFkyBD5+qVLl2LIkCEYMGAArly5gl27dqFy5coAAFEU0b59eyQlJeHYsWOIjIxEXFwcfH195dtv2bIFkyZNws8//4zz58/D1tY2R8dqxYoV+Omnn/Dzzz8jJiYGv/zyCyZMmIC1a9cWQQsRERERESnKEqVbNNVnMexQW1sbNjY2AIC3b9+iffv28Pb2RlBQENatW5ej/tu3b7F27VqUL18eALBw4UJ8/fXXCAkJgY2NDaZNm4ZRo0ZhxIgR8m1q164NADh48CD++usv3Lp1C3Z2dgCA9evXw83NDefOnUPt2rUxb9489O3bF99++y0AYNq0aTh48KBC9mvq1KkICQlBx44dAQCOjo64evUqQkND0bt371zPMzU1FampqTnKZDLZR7UbERERERGpz2eR+Xpfv3798OLFC/z666/Q0sr99CpUqCDveAGAt7c3srKyEBsbi0ePHuHBgwdo0qRJrtvGxMTAzs5O3vECAFdXV5ibmyMmJkZex9vbW2G79z8/fvwYd+/eRb9+/WBsbCxfpk2bhri4uDzPLTg4GGZmZgpL6LKlyhuFiIiIiOgDnHBD/T6LzFe2adOmYd++fTh79ixMTExU3k4QBPn/KpucQxRFeX1VynOTlZUF4N3Qw7p16yqs09bWznO7wMBABAQEKJTdvfdApWMSEREREZG0PpvMV3h4OKZMmYItW7agUqVK+daNj4/Hgwf/dVqioqKgpaWFqlWrwsTEBA4ODjh06FCu27q6uiI+Ph53796Vl129ehXJyclwcXEBALi4uOD06dMK273/2draGuXKlcO///6LypUrKyyOjo55xi2TyWBqaqqwcMghEREREX0MZr7U77PIfP3999/w8/PD2LFj4ebmhsTERACAnp5ervX19fXRu3dvzJ49GykpKRg+fDi6dv2/9u48LMqqfwP4PSCLyCrIpggYKiCYKLnxpuICmuWC5YaKmGiWiuDem2uW5gYub5q5oIlaapot4kJSqSEu4BYqqIgLiAuLiiEwz+8Pfk6OgIDhnBnm/lzXXBc888zMrWzzfc4539NfsW5s9uzZ+OCDD2BtbY0ePXrgwYMHOHLkCMaNG4euXbuiefPmCAwMRGRkJIqKivDhhx+iY8eO8Pb2BgCEhoYiKCgI3t7e+M9//oPo6GicP38ejRo1UmSYPXs2xo8fD1NTU/To0QMFBQU4ceIEsrOzS41uERERERGR5qsRI18nTpxAfn4+5s2bBzs7O8XtaTOL57m4uCAgIABvvfUW/Pz84OHhodSNMCgoCJGRkfjyyy/RrFkzvP3220hJSQFQMjVx9+7dsLCwQIcOHdC1a1c0atQI3377reLxAwYMwMyZMzF16lS0atUK165dw5gxY5QyjBw5EmvXrkVUVBQ8PT3RsWNHREVFvXDki4iIiIiINJdMkrRr4G/27NnYvXs3kpKSREepFqmXr4qOUKFClD0CqU5yC41FR6iUv4vU//9SJlP/Xyl1ahVUfJIaMNJ9LDpCha49qCc6QoVyH2vGJI8OlmdFR6hQosd7oiNUyDf2U9ERKmXx9bIvEKuTlm6iE1TM2uiR6AiV0trVTHSEMq0te5WNSowsu7ddjVcjRr6IiIiIiIjUnWZcDiQiIiIiomqlXfPf1IPWjXzNnj27xkw5JCIiIiIizaF1xRcREREREZEInHZIRERERKSF5HLRCbQPR76IiIiIiIhUgCNfRERERERaiA03VI8jX0RERERERCrAkS8iIiIiIi3EkS/V48gXERERERGRCrD4IiIiIiIiUgFOOyQiIiIi0kJyTjtUOY58ERERERERqQBHvoiIiIiItJAktOOGTOBri8ORLyIiIiIiIhXgyJeGk8nUf7Ku89VY0REqdMPpTdERKkWv1hPRESqkKy8UHaFC2Tr1REeoFFP5fdERKtTE7G/RESp0r7al6AiVUqSjJzpChXxjPxUdoUKHuswQHaFSepzzEx2hQnayG6IjVOi+zFp0BKIqYfFFRERERKSFuM+X6nHaIRERERERkQpw5IuIiIiISAvJ5aITaB+OfBEREREREakAR76IiIiIiLQQ13ypHke+iIiIiIioRsjOzsbQoUNhZmYGMzMzDB06FDk5OS98zPDhwyGTyZRubdu2VTqnoKAA48aNg5WVFerUqYNevXrhxo2qdwRl8UVERERERDXC4MGDkZSUhJiYGMTExCApKQlDhw6t8HHdu3dHRkaG4vbLL78o3T9hwgTs2rUL27Ztw+HDh/Hw4UO8/fbbKC4urlI+TjskIiIiItJC8ho27TA5ORkxMTGIj49HmzZtAABff/012rVrh4sXL6Jp06blPtbAwAC2trZl3pebm4t169bhm2++QdeuXQEAmzdvhoODAw4ePAh/f/9KZ+TIFxERERERqVRBQQHy8vKUbgUFBf/qOf/880+YmZkpCi8AaNu2LczMzHD06NEXPjYuLg7W1tZo0qQJQkJCkJWVpbjv5MmTKCwshJ/fP5uj29vbw8PDo8LnfR6LLyIiIiIiLSRJ4m7z589XrMt6eps/f/6/+vdkZmbC2tq61HFra2tkZmaW+7gePXogOjoav/76K5YsWYLjx4+jc+fOimIwMzMT+vr6sLCwUHqcjY3NC5+3LJx2SEREREREKjV9+nSEh4crHTMwMCjz3NmzZ2POnDkvfL7jx48DAGQyWan7JEkq8/hTAwYMUHzs4eEBb29vODo64ueff0ZAQEC5j6voecvC4ouIiIiIiFTKwMCg3GLreWPHjsXAgQNfeI6TkxPOnDmD27dvl7rvzp07sLGxqXQ2Ozs7ODo6IiUlBQBga2uLJ0+eIDs7W2n0KysrC+3bt6/08wIsvoiIiIiItJIktONG5UeMrKysYGVlVeF57dq1Q25uLhISEtC6dWsAwLFjx5Cbm1ulIunevXu4fv067OzsAACtWrWCnp4eDhw4gP79+wMAMjIycO7cOSxcuLDSzwtwzdcr0alTJ0yYMEF0DCIiIiIireHm5obu3bsjJCQE8fHxiI+PR0hICN5++22lToeurq7YtWsXAODhw4eYNGkS/vzzT6SlpSEuLg7vvPMOrKys0LdvXwCAmZkZ3n//fUycOBGxsbFITEzEkCFD4Onpqeh+WFksvgSLi4uDTCarcPM3IiIiIqLqJJfE3V6V6OhoeHp6ws/PD35+fmjevDm++eYbpXMuXryI3NxcAICuri7Onj2L3r17o0mTJggKCkKTJk3w559/wsTERPGYiIgI9OnTB/3794ePjw+MjIzw448/QldXt0r5OO2QiIiIiIhqhLp162Lz5s0vPEeS/qn+ateujX379lX4vIaGhlixYgVWrFjxr/Jx5OsV27x5M7y9vWFiYgJbW1sMHjxYsW9AWloafH19AQAWFhaQyWQYPny4wLREREREpC1EtprXViy+XrEnT57g008/xenTp7F7925cvXpVUWA5ODhg586dAEqGPzMyMrBs2TKBaYmIiIiI6FXhtMNXbMSIEYqPGzVqhOXLl6N169Z4+PAhjI2NUbduXQAlm7+Zm5sLSklERERERK8ai69XLDExEbNnz0ZSUhLu378PuVwOAEhPT4e7u3uVnqugoECx0/azxyq7RwIRERER0VNyoa3mtROnHb5Cjx49gp+fH4yNjbF582YcP35c0dbyyZMnVX6++fPnw8zMTOm2evXq6o5NRERERESvAEe+XqELFy7g7t27WLBgARwcHAAAJ06cUDpHX18fAFBcXFzh802fPh3h4eFKx27cvFlNaYmIiIhIm2hz4wtROPL1CjVs2BD6+vpYsWIFrly5gj179uDTTz9VOsfR0REymQw//fQT7ty5g4cPH5b7fAYGBjA1NVW6ccohEREREZFmYPH1CtWrVw9RUVHYvn073N3dsWDBAixevFjpnPr162POnDmYNm0abGxsMHbsWEFpiYiIiIjoVeK0w1cgLi5O8fGgQYMwaNAgpful58Z4Z8yYgRkzZqgiGhERERERAE47FIEjX0RERERERCrAkS8iIiIiIi0k59CXynHki4iIiIiISAVYfBEREREREakApx0SEREREWkhSS46gfbhyBcREREREZEKcOSLiIiIiEgLPb/9Eb16HPkiIiIiIiJSAY58ERERERFpITnXfKkcR76IiIiIiIhUgMUXERERERGRCnDaIRERERGRFmLDDdXjyBcREREREZEKcOSLiIiIiEgLyTnwpXIc+SIiIiIiIlIBjnxpOF15kegIFcp0bCs6QoUsHmeIjlApBo/uiY5QIZm8WHSECpnLroiOUCkFxlaiI1TIKvWo6AgVqv/ogegIlZLqFSg6QoWirgeIjlChHuf8REeolCyPNqIjVMjzpymiI1SorrG56AiV07if6ASkJlh8ERERERFpIYnzDlWO0w6JiIiIiIhUgCNfRERERERaiJ3mVY8jX0RERERERCrAkS8iIiIiIi0k55ovlePIFxERERERkQqw+CIiIiIiIlIBTjskIiIiItJCEjtuqBxHvoiIiIiIiFSAI19ERERERFpIkotOoH048kVERERERKQCLL6IiIiIiIhUgNMOiYiIiIi0kJwNN1SOI1+V0KlTJ0yYMEF0DCIiIiIi0mAc+SIiIiIi0kJsNa96HPkiIiIiIiJSARZfVZSdnY1hw4bBwsICRkZG6NGjB1JSUhT3R0VFwdzcHPv27YObmxuMjY3RvXt3ZGRkKM4pKirC+PHjYW5uDktLS0ydOhVBQUHo06ePgH8REREREWkjuVwSdtNWLL6qaPjw4Thx4gT27NmDP//8E5Ik4a233kJhYaHinPz8fCxevBjffPMNfv/9d6Snp2PSpEmK+7/44gtER0djw4YNOHLkCPLy8rB7924B/xoiIiIiIlIVFl9VkJKSgj179mDt2rV488038frrryM6Oho3b95UKp4KCwuxevVqeHt7o2XLlhg7dixiY2MV969YsQLTp09H37594erqipUrV8Lc3Fz1/yAiIiIiIlIZNtyoguTkZNSqVQtt2rRRHLO0tETTpk2RnJysOGZkZITXXntN8bmdnR2ysrIAALm5ubh9+zZat26tuF9XVxetWrWCXP7ibcYLCgpQUFDw3LEnMDDQ/1f/LiIiIiLSPuy3oXoc+aqC8jrCSJIEmUym+FxPT0/pfplMVuqxz57/oud+1vz582FmZqZ0W/XVV5WNT0REREREArH4qgJ3d3cUFRXh2LFjimP37t3DpUuX4ObmVqnnMDMzg42NDRISEhTHiouLkZiYWOFjp0+fjtzcXKXbmNGjq/4PISIiIiKtJ8klYTdtxWmHVdC4cWP07t0bISEh+Oqrr2BiYoJp06ahfv366N27d6WfZ9y4cZg/fz5cXFzg6uqKFStWIDs7u9Ro2PMMDAxgYGCgdOw+pxwSEREREWkEjnxV0YYNG9CqVSu8/fbbaNeuHSRJwi+//FJqquGLTJ06FYMGDcKwYcPQrl07GBsbw9/fH4aGhq8wORERERERicSRr0qIi4tTfGxhYYFNmzaVe+7w4cMxfPhwpWN9+vRRWtNVq1YtrFixAitWrAAAyOVyuLm5oX///tWam4iIiIioPHJ23FA5Fl8CXLt2Dfv370fHjh1RUFCAlStX4urVqxg8eLDoaERERERE9Iqw+BJAR0cHUVFRmDRpEiRJgoeHBw4ePFjpph1ERERERP+WNje+EIXFlwAODg44cuSI6BhERERERKRCLL6IiIiIiLQQR75Uj90OiYiIiIiIVIDFFxERERERkQpw2iERERERkRbirEPV48gXERERERGRCnDki4iIiIhIC7Hhhupx5IuIiIiIiEgFWHwRERERERGpAKcdEhERERFpIUnitENV48gXERERERGRCnDki4iIiIhIC8nZcEPlOPJFRERERESkAhz50nDFOur/JbTcvlR0hAoZNm4sOkKlFFy5IjpChe6duyw6QoXMw6eKjlApte+li45Qodu7fxEdoUI27/YWHaFSauvki45QoZZuohNUzE52Q3SESvH8aYroCBWKfXuh6AgVevPYStERNBrXfKkeR76IiIiIiKhGyM7OxtChQ2FmZgYzMzMMHToUOTk5L3yMTCYr87Zo0SLFOZ06dSp1/8CBA6ucT/2HTYiIiIiIiCph8ODBuHHjBmJiYgAAo0aNwtChQ/Hjjz+W+5iMjAylz/fu3Yv3338f/fr1UzoeEhKCuXPnKj6vXbt2lfOx+CIiIiIi0kJSDWu4kZycjJiYGMTHx6NNmzYAgK+//hrt2rXDxYsX0bRp0zIfZ2trq/T5Dz/8AF9fXzRq1EjpuJGRUalzq4rTDomIiIiISKUKCgqQl5endCsoKPhXz/nnn3/CzMxMUXgBQNu2bWFmZoajR49W6jlu376Nn3/+Ge+//36p+6Kjo2FlZYVmzZph0qRJePDgQZUzsvgiIiIiItJCklwSdps/f75iXdbT2/z58//VvyczMxPW1taljltbWyMzM7NSz7Fx40aYmJggICBA6XhgYCC2bt2KuLg4zJgxAzt37ix1TmVw2iEREREREanU9OnTER4ernTMwMCgzHNnz56NOXPmvPD5jh8/DqCkecbzJEkq83hZ1q9fj8DAQBgaGiodDwkJUXzs4eGBxo0bw9vbG6dOnULLli0r9dwAiy8iIiIiIlIxAwODcout540dO7bCzoJOTk44c+YMbt++Xeq+O3fuwMbGpsLX+eOPP3Dx4kV8++23FZ7bsmVL6OnpISUlhcUXERERERG9mFxD9vmysrKClZVVhee1a9cOubm5SEhIQOvWrQEAx44dQ25uLtq3b1/h49etW4dWrVrh9ddfr/Dc8+fPo7CwEHZ2dhX/A57BNV9ERERERKTx3Nzc0L17d4SEhCA+Ph7x8fEICQnB22+/rdTp0NXVFbt27VJ6bF5eHrZv346RI0eWet7Lly9j7ty5OHHiBNLS0vDLL7/gvffeg5eXF3x8fKqUkSNfRERERERaqKa1mgdKOhKOHz8efn5+AIBevXph5cqVSudcvHgRubm5Sse2bdsGSZIwaNCgUs+pr6+P2NhYLFu2DA8fPoSDgwN69uyJWbNmQVdXt0r5WHwREREREVGNULduXWzevPmF50hlTLccNWoURo0aVeb5Dg4O+O2336olH4svIiIiIiItVFYRQq8W13wRERERERGpgLDiS5IkjBo1CnXr1oVMJkNSUlKVn2P27Nlo0aKF4vPhw4ejT58+L/14IiIiIiKiV0VY8RUTE4OoqCj89NNPyMjIgIeHB2QyGXbv3v3Sz7ls2TJERUVV+vxJkyYhNjb2pV+vPGvWrEGnTp1gamoKmUyGnJycUuc4OTlBJpMp3aZNm1btWYiIiIiIyiKXS8Ju2krYmq/Lly/Dzs6uUj33K8vMzKxK5xsbG8PY2LjaXv+p/Px8dO/eHd27d8f06dPLPW/u3LlKu2W/iixERERERKQe/tXI144dO+Dp6YnatWvD0tISXbt2xaNHj1BcXIzw8HCYm5vD0tISU6ZMQVBQkGJK4PDhwzFu3Dikp6dDJpPByckJTk5OAIC+ffsqjlXVs9MOv/rqK9SvXx9yuVzpnF69eiEoKAhA+dMWFy9eDDs7O1haWuKjjz5CYWGh4pyMjAz07NkTtWvXhrOzM7Zs2QInJydERkYqzpkwYQKmTZuGtm3bvjCviYkJbG1tFTcWX0RERESkKpJcEnbTVi9dfGVkZGDQoEEYMWIEkpOTERcXh4CAAEiShCVLlmD9+vVYt24dDh8+jPv37yttZLZs2TLMnTsXDRo0QEZGBo4fP47jx48DADZs2KA49m+89957uHv3Lg4dOqQ4lp2djX379iEwMLDcxx06dAiXL1/GoUOHsHHjRkRFRSlNZRw2bBhu3bqFuLg47Ny5E2vWrEFWVtZLZfziiy9gaWmJFi1a4LPPPsOTJ09e6nmIiIiIiEj9vfS0w4yMDBQVFSEgIACOjo4AAE9PTwBAZGQkpk+fjn79+gEAVq9ejX379ikea2ZmBhMTE+jq6sLW1lbpec3NzUsdexl169ZF9+7dsWXLFnTp0gUAsH37dtStW1fxeVksLCywcuVK6OrqwtXVFT179kRsbCxCQkJw4cIFHDx4EMePH4e3tzcAYO3atWjcuHGV84WGhqJly5awsLBAQkICpk+fjqtXr2Lt2rUv9w8mIiIiIiK19tLF1+uvv44uXbrA09MT/v7+8PPzw7vvvgsdHR1kZGSgXbt2/7xIrVrw9vZW+V4CgYGBGDVqFL788ksYGBggOjoaAwcOfOFO1M2aNVO6387ODmfPngVQsht2rVq10LJlS8X9Li4usLCwqHK2sLAwxcfNmzeHhYUF3n33XcVoWFkKCgpQUFBQ6piBgUGVX5+IiIiItBv3+VK9l552qKuriwMHDmDv3r1wd3fHihUr0LRpU6SlpVVjvH/nnXfegVwux88//4zr16/jjz/+wJAhQ174GD09PaXPZTKZYt1Yed+g1fGN+3R9WGpqarnnzJ8/H2ZmZkq31atX/+vXJiIiIiKiV+9fNdyQyWTw8fHBnDlzkJiYCH19fcTGxsLOzg7x8fGK84qKinDy5MkKn09PTw/FxcX/JpKS2rVrIyAgANHR0di6dSuaNGmCVq1avfTzubq6oqioCImJiYpjqampZbaSr6qnz2lnZ1fuOdOnT0dubq7S7YMPPvjXr01ERERE2keSy4XdtNVLTzs8duwYYmNj4efnB2traxw7dgx37tyBm5sbQkNDsWDBAjRu3Bhubm5YunRppQoUJycnxMbGwsfHBwYGBi81ne95gYGBeOedd3D+/PkKR70q4urqiq5du2LUqFFYtWoV9PT0MHHiRNSuXRsymUxxXmZmJjIzMxWjWGfPnoWJiQkaNmyIunXr4s8//0R8fDx8fX1hZmaG48ePIywsDL169ULDhg3LfX0DA4NSUwwN7t79V/8mIiIiIiJSjZce+TI1NcXvv/+Ot956C02aNMEnn3yCJUuWoEePHpg4cSKGDRuG4cOHo127djAxMUHfvn0rfM4lS5bgwIEDcHBwgJeX18tGU9K5c2fUrVsXFy9exODBg//1823atAk2Njbo0KED+vbti5CQEJiYmMDQ0FBxzurVq+Hl5aXYw6tDhw7w8vLCnj17AJQUUd9++y06deoEd3d3zJw5EyEhIdi6deu/zkdEREREROpJJqlopd3w4cORk5OD3bt3q+LlVObGjRtwcHDAwYMHX9hF8VW5fOWKyl+zqiy3LxUdoUKGL9GxUoQCDfh63zt3WXSECpmHTxUdoVIMs2+KjlChO9HbREeokM27vUVHqJSbLh1FR6hQck75szPURQvjC6IjVIrV1WOiI1Qo9u2FoiNU6M1jK0VHqBTTlt1ERyjTgEnXhL32t4sdhb22SC897VBb/frrr3j48CE8PT2RkZGBKVOmwMnJCR06dBAdjYiIiIiI1Ni/arjxqjVr1gzGxsZl3qKjo4VkKiwsxMcff4xmzZqhb9++qFevHuLi4kp1SSQiIiIiUmeSJAm7aSuVjXxFRUVV+TG//PILCgsLy7zPxsbmXyZ6Of7+/vD39xfy2kREREREpLnUetqho6N2zgUlIiIiInrVJLn2jkCJotbTDomIiIiIiGoKFl9EREREREQqoNbTDomIiIiI6NXgtEPV48gXERERERGRCnDki4iIiIhIC8kluegIWocjX0RERERERCrA4ouIiIiIiEgFOO2QiIiIiEgLseGG6nHki4iIiIiISAU48kVEREREpIU48qV6HPkiIiIiIiJSAY58abj7hXVFR6hQnd7vi45QodxaRqIjVIqh20PRESpk1fm+6AgVyl0dKTpCpdyfuFh0hApZj7YQHaFC2XVsREeoFJmk/legrY0eiY5Qofsya9ERKqWusbnoCBV689hK0REq9EebsaIjVErPwouiI5RJ0oDfOzUNR76IiIiIiIhUgMUXERERERGRCnDaIRERERGRFpLL5aIjaB2OfBEREREREakAR76IiIiIiLQQW82rHke+iIiIiIiIVIDFFxERERERkQpw2iERERERkRaSJDbcUDWOfBEREREREakAR76IiIiIiLQQG26oHke+iIiIiIiIVIAjX0REREREWogjX6rHkS8iIiIiIiIVqDHFlyRJGDVqFOrWrQuZTIakpKQqP8fs2bPRokWLas9GRERERERUY4qvmJgYREVF4aeffkJGRgY8PDwgk8mwe/fuV/aaaWlpZRZ658+fR79+/eDk5ASZTIbIyMhSj509ezZkMpnSzdbW9pVlJSIiIiJ6llySC7tpqxqz5uvy5cuws7ND+/btRUdBfn4+GjVqhPfeew9hYWHlntesWTMcPHhQ8bmurq4q4hERERERkQBqNfK1Y8cOeHp6onbt2rC0tETXrl3x6NEjFBcXIzw8HObm5rC0tMSUKVMQFBSEPn36AACGDx+OcePGIT09HTKZDE5OTnBycgIA9O3bV3GsquRyOebOnYsGDRrAwMAALVq0QExMjOJ+Z2dnAICXlxdkMhk6deoEAHjjjTewaNEiDBw4EAYGBuU+f61atWBra6u41atXr8oZiYiIiIhehiSXhN20ldoUXxkZGRg0aBBGjBiB5ORkxMXFISAgAJIkYcmSJVi/fj3WrVuHw4cP4/79+9i1a5fiscuWLVMUSRkZGTh+/DiOHz8OANiwYYPiWFUtW7YMS5YsweLFi3HmzBn4+/ujV69eSElJAQAkJCQAAA4ePIiMjAx8//33VXr+lJQU2Nvbw9nZGQMHDsSVK1eqnJGIiIiIiDSD2kw7zMjIQFFREQICAuDo6AgA8PT0BABERkZi+vTp6NevHwBg9erV2Ldvn+KxZmZmMDExga6ubql1U+bm5i+9lmrx4sWYOnUqBg4cCAD44osvcOjQIURGRuJ///ufYqTK0tKyyq/Rpk0bbNq0CU2aNMHt27cxb948tG/fHufPn4elpeVL5SUiIiIiIvWlNsXX66+/ji5dusDT0xP+/v7w8/PDu+++Cx0dHWRkZKBdu3aKc2vVqgVvb29I0qsbsszLy8OtW7fg4+OjdNzHxwenT5/+18/fo0cPxceenp5o164dXnvtNWzcuBHh4eFlPqagoAAFBQVKx548KYC+fvlTG4mIiIiIyiLJtbfxhShqM+1QV1cXBw4cwN69e+Hu7o4VK1agadOmSEtLE5pLJpMpfS5JUqlj1aFOnTrw9PRUTGksy/z582FmZqZ0i/oqotqzEBERERFR9VOb4gsoKXR8fHwwZ84cJCYmQl9fH7GxsbCzs0N8fLzivKKiIpw8ebLC59PT00NxcfFLZTE1NYW9vT0OHz6sdPzo0aNwc3MDAOjr6wPAS7/GswoKCpCcnAw7O7tyz5k+fTpyc3OVbsNHl99NkYiIiIioPGy4oXpqM+3w2LFjiI2NhZ+fH6ytrXHs2DHcuXMHbm5uCA0NxYIFC9C4cWO4ublh6dKlyMnJqfA5nZycEBsbCx8fHxgYGMDCwqJKmSZPnoxZs2bhtddeQ4sWLbBhwwYkJSUhOjoaAGBtbY3atWsjJiYGDRo0gKGhIczMzPDkyRP89ddfAIAnT57g5s2bSEpKgrGxMVxcXAAAkyZNwjvvvIOGDRsiKysL8+bNQ15eHoKCgsrNY2BgUKp7or4+h4uJiIiIiDSB2hRfpqam+P333xEZGYm8vDw4OjpiyZIl6NGjB7p164aMjAwMHz4cOjo6GDFiBPr27Yvc3NwXPueSJUsQHh6Or7/+GvXr16/yFMbx48cjLy8PEydORFZWFtzd3bFnzx40btwYQMnas+XLl2Pu3LmYOXMm3nzzTcTFxeHWrVvw8vJSPM/ixYuxePFidOzYEXFxcQCAGzduYNCgQbh79y7q1auHtm3bIj4+XtFshIiIiIjoVZK0eLNjUWTSq+xa8QoNHz4cOTk52L17t+goQh2/mCM6QoUcpKuiI1SooJaR6AiVYlj4UHSECtV+fF90hArlrl0tOkKlFExcLDpChaxzLomOUKFHdWxER6iUx3omoiNU6G6hlegIFTLULaj4JDXgkvWH6AgVKqptKjpChf5oM1Z0hErpWXhRdIQydRmYIOy1Y7e1FvbaIqnVmi8iIiIiIqKaSquKr2bNmsHY2LjM29N1XERERERE2kAul4TdtJXarPmqqqioqCo/5pdffkFhYWGZ99nYaMa0FCIiIiIi0kwaW3y9DDazICIiIiIqwU2WVU+rph0SERERERGJwuKLiIiIiIhIBbRq2iEREREREZWQtLjxhSgc+SIiIiIiIlIBjnwREREREWkhSWLDDVXjyBcREREREZEKcOSLiIiIiEgLcc2X6nHki4iIiIiIaoTPPvsM7du3h5GREczNzSv1GEmSMHv2bNjb26N27dro1KkTzp8/r3ROQUEBxo0bBysrK9SpUwe9evXCjRs3qpyPxRcREREREdUIT548wXvvvYcxY8ZU+jELFy7E0qVLsXLlShw/fhy2trbo1q0bHjx4oDhnwoQJ2LVrF7Zt24bDhw/j4cOHePvtt1FcXFylfJx2SERERESkhSR5zWu4MWfOHABAVFRUpc6XJAmRkZH473//i4CAAADAxo0bYWNjgy1btmD06NHIzc3FunXr8M0336Br164AgM2bN8PBwQEHDx6Ev79/pfNx5IuIiIiIiFSqoKAAeXl5SreCggKV57h69SoyMzPh5+enOGZgYICOHTvi6NGjAICTJ0+isLBQ6Rx7e3t4eHgozqk0iej//f3339KsWbOkv//+W3SUF9KEnMxYfTQhJzNWH03IyYzVRxNyMmP10YScmpCxppg1a5YEQOk2a9asanv+DRs2SGZmZhWed+TIEQmAdPPmTaXjISEhkp+fnyRJkhQdHS3p6+uXemy3bt2kUaNGVSmXTJIktjkhAEBeXh7MzMyQm5sLU1NT0XHKpQk5mbH6aEJOZqw+mpCTGauPJuRkxuqjCTk1IWNNUVBQUGqky8DAAAYGBqXOnT17tmI6YXmOHz8Ob29vxedRUVGYMGECcnJyXvi4o0ePwsfHB7du3YKdnZ3ieEhICK5fv46YmBhs2bIFwcHBpfJ269YNr732GlavXv3C13gW13wREREREZFKlVdolWXs2LEYOHDgC89xcnJ6qRy2trYAgMzMTKXiKysrCzY2Nopznjx5guzsbFhYWCid0759+yq9HosvIiIiIiJSW1ZWVrCysnolz+3s7AxbW1scOHAAXl5eAEo6Jv7222/44osvAACtWrWCnp4eDhw4gP79+wMAMjIycO7cOSxcuLBKr8fii4iIiIiIaoT09HTcv38f6enpKC4uRlJSEgDAxcUFxsbGAABXV1fMnz8fffv2hUwmw4QJE/D555+jcePGaNy4MT7//HMYGRlh8ODBAAAzMzO8//77mDhxIiwtLVG3bl1MmjQJnp6eiu6HlcXiixQMDAwwa9asSg8Bi6IJOZmx+mhCTmasPpqQkxmrjybkZMbqowk5NSEjvdjMmTOxceNGxedPR7MOHTqETp06AQAuXryI3NxcxTlTpkzB48eP8eGHHyI7Oxtt2rTB/v37YWJiojgnIiICtWrVQv/+/fH48WN06dIFUVFR0NXVrVI+NtwgIiIiIiJSAe7zRUREREREpAIsvoiIiIiIiFSAxRcREREREZEKsPgiIiIiIiJSARZfREREREREKsDii4joJXTu3Bk5OTmljufl5aFz586qD1RJZWVWB6mpqdi3bx8eP34MAFC3Rrxz585Ffn5+qeOPHz/G3LlzBSQiIiJNxFbzRNXk1KlT0NPTg6enJwDghx9+wIYNG+Du7o7Zs2dDX19fcMKyFRcX4+zZs3B0dISFhYXoOFi+fHmZx2UyGQwNDeHi4oIOHTpUeV+N6qajo4PMzExYW1srHc/KykL9+vVRWFgoKNk/vvjiCzg5OWHAgAEAgP79+2Pnzp2wtbXFL7/8gtdff11wQuDevXsYMGAAfv31V8hkMqSkpKBRo0Z4//33YW5ujiVLloiOCADQ1dVFRkZGqa/3vXv3YG1tjeLiYiG5AgICKn3u999//wqTVN4333yD1atX4+rVq/jzzz/h6OiIyMhIODs7o3fv3qLjAQBGjBiBZcuWKe3xAwCPHj3CuHHjsH79ekHJNFd2djZSU1NhZ2eHBg0aiI5TpuzsbGzcuBEpKSmws7NDUFAQHBwcRMeiGoabLGup8t7glmX8+PGvMEnlqfsf7NGjR2PatGnw9PTElStXMHDgQPTt2xfbt29Hfn4+IiMjRUcEAEyYMAGenp54//33UVxcjI4dO+Lo0aMwMjLCTz/9pNiAUJSIiAjcuXMH+fn5sLCwgCRJyMnJgZGREYyNjZGVlYVGjRrh0KFDQv4onjlzRvHxX3/9hczMTMXnxcXFiImJQf369VWeqyxfffUVNm/eDAA4cOAADhw4gL179+K7777D5MmTsX//fsEJgbCwMNSqVQvp6elwc3NTHB8wYADCwsLUpviSJAkymazU8dOnT6Nu3boCEpUwMzNTfCxJEnbt2gUzMzN4e3sDAE6ePImcnJwqFWmv0qpVqzBz5kxMmDABn332maJoNTc3R2RkpFr8LgeAjRs3YsGCBaWKr8ePH2PTpk1qU3z17du3zO/LZy9WDR48GE2bNlVpro8//hiffPIJjIyMUFhYiI8++gjr1q1T/Bz17t0bW7ZsgaGhoUpzPc/e3h5nz56FpaUlrl69ivbt2wMAPD09sWfPHixevBjx8fFwdXUVmpNqGIm0kpOTk9KtTp06kkwmkywsLCQLCwtJJpNJderUkZydnUVHlSRJkr788kvJyspKmjdvnlS7dm3p8uXLkiRJ0oYNG6ROnToJTlfC1NRUSk1NlSRJkhYsWCD5+flJkiRJhw8flho0aCAympL69etLx48flyRJknbt2iXZ29tLFy9elP773/9K7du3F5xOkrZs2SJ16tRJ8X8pSZKUkpIide7cWdq2bZt0/fp1ycfHR+rXr5+QfDKZTNLR0ZF0dHQkmUxW6mZkZCStW7dOSLbnGRoaSunp6ZIkSdL48eOlUaNGSZIkSRcvXpTMzc1FRlOwsbGRkpKSJEmSJGNjY8XP9pUrV6Q6deqIjCZJkiSZm5tLFhYWko6OjuLjpzdTU1NJR0dH+vDDD0XHlCRJkqZMmSKNHDlSKioqUhwrKiqSRo0aJU2aNElgsn+4ublJu3btkiRJ+et99uxZydLSUmCyErm5uVJOTo4kk8mk1NRUKTc3V3G7f/++tHHjRsnOzk50TIWgoCDJzMxMcnR0lAICAqS+fftKTk5Okrm5udS/f3+padOmkoGBgXT48GGV5tLR0ZFu374tSZIkffbZZ1K9evWknTt3Sjdv3pR+/PFHqX79+tLcuXNVmqksMplMkXPgwIFSp06dpEePHkmSJEl///239Pbbb0vvvvuuyIhUA7H4Iik6Olry8fGRLly4oDh24cIF6c0335Q2b94sMNk/1P0PtiRJkomJiXTp0iVJkiSpa9euUmRkpCRJknTt2jXJ0NBQZDQlBgYG0vXr1yVJkqSQkBApNDRUkqSSN7smJiYCk5Vo1KiRlJiYWOr4qVOnFBcDjhw5Itna2qo4WYm0tDTp6tWrkkwmk44fPy6lpaUpbrdu3VJ64yuanZ2ddOTIEUmSJKlJkybSd999J0lSyc+3OnytJank5/npz82zP9sJCQlS3bp1RUaTJEmSoqKipA0bNkgymUxatmyZFBUVpbht2bJFOnr0qOiIClZWVkq/x5+6cOGCWvxfSlLJBYG0tDRJkpS/3pcuXVKL35PPXlwp66arqyvNmzdPdEyFqVOnSmPGjJGKi4sVx4qLi6WxY8dK06dPl+RyuTRq1CjJx8dHpbmeLWpatGhR6oLUt99+K7m5uak0U1mezens7CzFxsYq3R8fH69WF0+pZuC0Q8KMGTOwY8cOpWkJTZs2RUREBN59910EBgYKTFfi6tWr8PLyKnXcwMAAjx49EpCoNG9vb8ybNw9du3bFb7/9hlWrVgEoyW5jYyM43T9sbGzw119/wc7ODjExMfjyyy8BAPn5+cLXUQFARkYGioqKSh0vKipSTPGzt7fHgwcPVB0NAODo6AgAkMvlQl6/KgICAjB48GA0btwY9+7dQ48ePQAASUlJcHFxEZyuRIcOHbBp0yZ8+umnAEqmS8nlcixatAi+vr6C0wFBQUEAAGdnZ7Rv3x56enqCE5WvqKgIycnJpaaYJScnq833q7OzM5KSkhQ/R0/t3bsX7u7uglL949ChQ5AkCZ07d8bOnTuVppTq6+vD0dER9vb2AhMqW7duHY4cOQIdnX/6p+no6GDcuHFo3749Pv/8c4wdOxZvvvmmyrM9nQ55/fp1tG7dWum+1q1b49q1ayrPVJanOQsKCkr9rbaxscGdO3dExKIajMUXISMjo8zmAMXFxbh9+7aARKWp+x9sAIiMjERgYCB2796N//73v4o3tzt27FDMI1cHwcHB6N+/P+zs7CCTydCtWzcAwLFjx9RiXruvry9Gjx6NtWvXKgruxMREjBkzRtFF8OzZs3B2dhYZEwBw+fJlREZGIjk5GTKZDG5ubggNDcVrr70mOhqAkvVzzs7OSE9Px8KFC2FsbAyg5Gf+ww8/FJyuxKJFi9CpUyecOHECT548wZQpU3D+/Hncv38fR44cEZotLy9P8bGXlxceP36s6Mb4PFNTU1XFKldwcDBGjBiB1NRUtG3bFgAQHx+PBQsWIDg4WHC6EpMnT8ZHH32Ev//+G5IkISEhAVu3bsX8+fOxdu1a0fHQsWNHACUXzRwcHJSKGnVUVFSECxcuoEmTJkrHL1y4oFhPZ2hoWOa6sFft66+/hrGxMQwMDJCdna10X25uLgwMDFSeqSxdunRBrVq1kJeXh0uXLqFZs2aK+9LT02FlZSUwHdVELL4IXbp0QUhICNatW4dWrVpBJpPhxIkTGD16NLp27So6HgD1/4MNAM2bN8fZs2dLHV+0aJFajCg9NXv2bHh4eOD69et47733FH8AdXV1MW3aNMHpSq7kDh06FK1atVKMMhQVFaFLly5Yt24dAMDY2Fh4I4Z9+/ahV69eaNGiBXx8fCBJEo4ePYpmzZrhxx9/VBS1ohQWFmLUqFGYMWMGGjVqpHTfhAkTxIQqg7u7O86cOYNVq1ZBV1cXjx49QkBAAD766CPY2dkJzWZubl7hm1bp/xsIiOp2+KzFixfD1tYWERERyMjIAADY2dlhypQpmDhxouB0JYKDg1FUVIQpU6YgPz8fgwcPRv369bFs2TIMHDhQdDyFpxf68vPzkZ6ejidPnijd37x5cxGxShk6dCjef/99fPzxx3jjjTcgk8mQkJCAzz//HMOGDQMA/Pbbb0oFhSo0bNgQX3/9NYCSEcNTp04pjb4dOnRI5U1AyjJr1iylz42MjJQ+//HHH4WMGlLNxlbzhDt37iAoKAgxMTGKN7uFhYXo3r07NmzYoDZT5r7++mvMmzcP169fBwDUr18fs2fPxvvvvy84WWkPHz4sNc1HHa6Ma5ILFy7g0qVLkCQJrq6uavGH+lleXl7w9/fHggULlI5PmzYN+/fvx6lTpwQl+4e5uTlOnTpVqviiyvntt98qfe7TERN18XTUTp1/79y9exdyubxU+351cOfOHQQHB2Pv3r1l3q8OxTZQkmPBggVYuXKlYqaKjY0Nxo0bh6lTp0JXVxfp6enQ0dFRq/bu8fHxMDAwKHM5AVFNx+KLFFJSUpCcnAxJkuDm5lZqGoO6UNc/2FevXsXYsWMRFxeHv//+W3Fcna6MP5WQkIC4uDhkZWWVKhKXLl0qKJVmMTQ0xNmzZ9G4cWOl45cuXULz5s2VvgdECQ4OhqenJ8LDw0VHKZezszOGDBmCIUOGqF2BrYmKiooQFxeHy5cvY/DgwTAxMcGtW7dgamqqmHZKFQsMDERaWhoiIyPh6+uLXbt24fbt25g3bx6WLFmCnj17io5YiiYU3ETEaYdaKzw8HJ9++inq1KlT5huzZ6/4qsOb8atXr6KoqAiNGzdWmn+dkpICPT09ODk5iQv3/542Jlm/fj1sbGyEzLGvjM8//xyffPIJmjZtWiqnOmQuLi5GVFQUYmNjyywOf/31V0HJlNWrVw9JSUmliq+kpCS1uTDg4uKCTz/9FEePHkWrVq1Qp04dpfvVYQ+/cePGYevWrfjss8/g5eWFoUOHYsCAAcKnHAIle7p5eHhAR0dHaX+3sqjDNLRr166he/fuSE9PR0FBAbp16wYTExMsXLgQf//9N1avXi0kl5eXV6V/t6jDiDFQ8nvmhx9+wBtvvAEdHR04OjqiW7duMDU1xfz589Wy+NKkois7Oxs//vijYmqkurp8+TJCQkLU5u8O1QwsvrRUYmKioslGYmJiueepw5txABg+fDhGjBhR6o3usWPHsHbtWsTFxYkJ9owzZ87g5MmTan/1ftmyZVi/fj2GDx8uOkqZQkNDERUVhZ49e8LDw0NtvgefFxISglGjRuHKlSto3749ZDIZDh8+jC+++EJt1tesXbsW5ubmOHnyJE6ePKl0n0wmU4viKzw8HOHh4bh06RKio6OxatUqTJ48Gb6+vhgyZIjQN2ctWrRAZmYmrK2t0aJFC8hkMpQ1WURdRrZDQ0Ph7e2N06dPw9LSUnG8b9++GDlypLBcffr0UXz8999/48svv4S7uzvatWsHoGQK2vnz59WmCQwAPHr0SHERpW7durhz5w6aNGkCT09PtSkQAeD27duYNGmS4mLV89+f6vB9WZb09HQEBwerffH18OHDKk0/JqoMTjskjWBqaopTp06Vao+dmpoKb29v5OTkiAn2DF9fX/z3v/9VmyYl5bGzs8Pvv/9eqpBVF1ZWVti0aRPeeust0VFeSJIkREZGYsmSJbh16xaAkhb4kydPxvjx49W2aNQE8fHxGDNmDM6cOSP0zeO1a9fQsGFDyGSyCttiP9+JVQQrKyscOXIETZs2hYmJCU6fPo1GjRohLS0N7u7uyM/PFx0RI0eOhJ2dnWJrgadmzZqF69evY/369YKSKXvjjTcwb948+Pv7o0+fPooRr+XLl2PHjh24fPmy6IgAgB49eiA9PR1jx45VdLB9Vu/evYXkerZTaFnOnDmDjh07Ci8Oly9f/sL7b968icWLFwvPSTULR75II8hksjL3dcrNzVWbX4pr167FBx98gJs3b8LDw6PUfkDqMC0JAMLCwvC///0PkZGRoqOUSV9fX232oHoRmUyGsLAwhIWFKb43TUxMBKcq39PrbOpcFCYkJGDLli349ttvkZubi3fffVdonmcLKnUorioil8vL/H1448YNtfne3L59O06cOFHq+JAhQ+Dt7a02xdeECRMUHSNnzZoFf39/bN68Gfr6+ti4caPgdP84fPgw/vjjD7Ro0UJ0FCUVdQp9uhZatAkTJsDOzg76+vpl3v98l0ui6sCRL9IIb7/9NoyMjLB161ZF2/bi4mIMGDAAjx49KrcjlSrFx8dj8ODBSEtLUxx7Ok1JXaYlASVv0Hr27IlLly7B3d29VJH4/fffC0pWYsmSJbhy5QpWrlypFn+cNd2mTZuwaNEipKSkAACaNGmCyZMnY+jQoYKTlXg63XDLli1IS0uDr68vAgMDERAQoDYFA1Dy//gi6jB9asCAATAzM8OaNWtgYmKCM2fOoF69eujduzcaNmyIDRs2iI4IW1tbzJ8/v9S+Yxs2bMC0adPUZm/J5+Xn5+PChQto2LChWu375O7ujujoaLXrGmhmZob//ve/aNOmTZn3p6SkYPTo0cL/Ljo7O+OLL75A//79y7w/KSkJrVq1Ep6TahaOfJFGWLhwITp06ICmTZsq9tz4448/kJeXpzYLYUeMGAEvLy9s3bpVrRtujBs3DocOHYKvry8sLS3VLufhw4dx6NAh7N27F82aNVOr4lDTGgcsXboUM2bMwNixYxV7kR05cgQffPAB7t69i7CwMNER4erqCm9vb3z00UcYOHAgbG1tRUcqU2hoqNLnhYWFyM/Ph76+PoyMjNSi+IqIiICvry/c3d3x999/Y/DgwUhJSYGVlRW2bt0qOh6AkpGGMWPG4OTJk0obQa9fvx4zZ84Umq0qXUHVoREVAERGRmLatGn46quv1KLx1FMtW7YEUP4WDObm5mWun1S1Vq1a4eTJk+UWX+Wt8yT6NzjyRRrj1q1bWLlyJU6fPo3atWujefPmGDt2LOrWrSs6GgCgTp06OH36tNpPmTMxMcG2bdvUslsXgFJXxJ8n8ur9nDlzKn3u85t3iuDs7Iw5c+aUKgw2btyI2bNn4+rVq4KS/ePSpUtqu61FRVJSUjBmzBhMnjwZ/v7+ouMAAB4/foxt27bh5MmTkMvlaNmyJQIDA1G7dm3R0RS+++47LFu2DMnJyQAANzc3hIaGlvsGWFV8fX0rdZ5MJlObi34WFhbIz89HUVERjIyMSl2sun//vpBcX3/9NR4/flxuU5/bt29j9erVwn9P/vXXX8jPz4e3t3eZ9xcWFuLWrVsaMe2YNAeLL6Jq8s4772D48OHo16+f6Cgv5OjoiH379sHV1VV0FK2wdetW9OrVq1Sbd1UwNDTEuXPnSl0QSElJgaenp1rsRfbUyZMnkZycDJlMBjc3N8WVc3V34sQJDBkyBBcuXBAdBb///jvat2+PWrWUJ7UUFRXh6NGj6NChg6Bk9KpUtP4sKChIRUmIqLJYfJHa0rQ9dtasWYN58+ZhxIgR8PT0LHUFslevXoKSKduwYQNiYmKwYcMGGBkZiY5T45mamiIpKQmNGjVS+Wt7eHhg8ODB+Pjjj5WOz5s3D99++y3Onj2r8kzPy8rKwsCBAxEXF6eYipSbmwtfX19s27YN9erVEx3xhRITE9GxY8cKu7upgq6uLjIyMkrtM3fv3j1YW1ur1bqVZ4ttd3d3tVuzRNqlc+fO+P7772Fubq50PC8vD3369FGbkU6qGVh8kdrS0dFR7LGjo6Oj9nvs6OjolHufumQEStYtXb58GZIkwcnJqVSRKGKtUsuWLREbGwsLC4sK11Wpw1qqqni25beq7dy5EwMGDEDXrl3h4+Oj2IssNjYW3333Hfr27avyTM8bMGAALl++jG+++QZubm4ASqYCBQUFwcXFRW3WKu3Zs0fpc0mSkJGRgZUrV8LBwUEtmv7o6Ojg9u3bpQrWS5cuwdvbWy0KRE0vttVRcXExdu/erVTM9urVS9GcSqTyWrnLZDIYGhrCxcUFHTp0EJ712fcbz8rKykL9+vUV+6ISVQc23CC1dfXqVcUfYnVYm1IRuVwuOkKlPLvhqbro3bs3DAwMFB+rWxMQTdWvXz8cO3YMERER2L17NyRJgru7OxISEtRmpCEmJgYHDx5UFF5ASQe3//3vf/Dz8xOYTNnzPzcymQz16tVD586dsWTJEjGh/l9AQIAi0/DhwxU/S0DJG/MzZ86gffv2ouIpGTduHPLy8nD+/PlSxfb48ePVptjWFKmpqXjrrbdw8+ZNNG3aFJIk4dKlS3BwcMDPP/+M1157TWi+iIgI3LlzB/n5+bCwsIAkScjJyYGRkRGMjY2RlZWFRo0a4dChQ3BwcFB5vmdn1fz111/IzMxUfF5cXIyYmBjUr19f5bmoZuPIF6m9wsJCjBo1CjNmzBAyekD0b4gc+dIEJiYmZe5TpE7T+dTd0yY1GzduRP/+/ZWaa+jr68PJyQkhISFq0SLdzMwMBw8exBtvvKF0PCEhAX5+fsjJyRETTEO99dZbkCQJ0dHRiuZT9+7dw5AhQ6Cjo4Off/5ZaL6tW7dizZo1WLt2raIQTE1NxejRozFq1Cj4+Pgoupzu2LFD5fmezqoBUObMmtq1a2PFihUYMWKEqqNRDcbiizSCubk5Tp06pfZvYH/77TcsXrxYqXHA5MmTFe3x1Ym6rrkIDg7GkCFD0Llz5xoxAia6+FLnKUlAyUhnTk4Otm7dCnt7ewDAzZs3ERgYCAsLC+zatUtwwtLUdcPqOXPmYNKkSUKau1QWi+3qVadOHcTHx8PT01Pp+OnTp+Hj44OHDx8KSlbitddew86dO8v8evfr1w9XrlzB0aNH0a9fP8Wm1qp07do1SJKERo0aISEhQWnaq76+PqytrdXmdyXVHOUvUiFSI3379sXu3btFx3ihzZs3o2vXrjAyMsL48eMxduxY1K5dG126dMGWLVtEx1PIyspC586d8cYbbyhytmrVCl26dMGdO3dEx8O9e/fQs2dPNGjQABMnTkRSUpLoSBorNTUV7u7uGDZsGL7//nvs2LEDQ4YMQbNmzXD58mXR8QAAK1euxIMHD+Dk5ITXXnsNLi4ucHZ2xoMHD7BixQrR8ZSsW7cOHh4eMDQ0hKGhITw8PLB27VrRsRRmzZql1oUXUNLYIDQ0FLdu3VIcu3nzJsLCwtClSxeByTSTgYEBHjx4UOr4w4cPoa+vLyCRsoyMDBQVFZU6XlRUpJjiZ29vX+a/QRUcHR3h5OQEuVwOb29vODo6Km52dnYsvOiV4MgXaYTPPvsMixcvRpcuXdCqVatSbzDK20tEldzc3DBq1KhSG9cuXboUX3/9tWJPG9E0ocFBTk4OvvvuO2zZsgV//PEHmjZtiiFDhmDw4MFqtZFoZXh4eGDv3r1C1jOo+5SkZx04cAAXLlxQrEvr2rWr6EhKZsyYgYiICIwbNw7t2rUDAPz5559YuXIlQkNDMW/ePMEJS+zYsQPfffcd0tPT8eTJE6X71KFZzfXr19G7d2+cO3cODg4OkMlkSE9Ph6enJ3744Qc0aNBAdESNMmzYMJw6dQrr1q1D69atAQDHjh1DSEgIWrVqhaioKKH5evbsiczMTKxdu1YxuyIxMREhISGwtbXFTz/9hB9//BEff/yx8O6rly9fRmRkpNLMldDQUOHr5qgGkog0gJOTU7k3Z2dn0fEkSZIkfX19KSUlpdTxlJQUycDAQECispmamkoJCQmljh87dkwyMzNTfaAKXL9+XVq4cKHk6uoq6erqio6j4OzsLN29e7fU8ezsbLX5njQyMpLOnDlT6nhSUpJUp04dAYk0l6WlpbRly5ZSx7ds2SJZWloKSFTasmXLJGNjY+mjjz6S9PX1pdGjR0tdu3aVzMzMpI8//lh0PCX79++Xli9fLi1btkw6cOCA6DgaKzs7W+rVq5ckk8kkfX19SV9fX5LJZFKfPn2k7Oxs0fGkjIwMqWvXrkr5dHR0pG7dukmZmZmSJEnSr7/+Ku3bt09ozpiYGElfX19q3bq1FBYWJk2YMEFq3bq1ZGBgIO3fv19oNqp52O2QNMKz3Q4lNV1v4eDggNjY2FIb2sbGxgoZ9SiPXC4v1V4eAPT09NSuY2NhYSFOnDiBY8eOIS0tDTY2NqIjKaSlpZW5fUBBQQFu3rwpIFFp6j4l6anY2FhEREQorji7urpiwoQJajX6VVxcDG9v71LHW7VqVea0KhG+/PJLrFmzBoMGDcLGjRsxZcoUNGrUCDNnzsT9+/dFx1PSrVs3dOvWTXQMjWdubo4ffvgBqampSE5OVowcP/93SBRbW1vFqPalS5cgSRJcXV3RtGlTxTm+vr4CE5aYNm0awsLCsGDBglLHp06dyu9VqlacdkgaY926dYiIiEBKSgoAoHHjxpgwYQJGjhwpOFmJVatWYcKECRgxYgTat2+v2FMpKioKy5Ytw+jRo0VHBKAZDQ4OHTqELVu2YOfOnSguLkZAQAACAwPRuXPnF+6npgpP93vq06cPNm7cCDMzM8V9xcXFiI2NxYEDB3Dx4kVRERXUfUoSULLmKywsDO+++65iOl98fDx27NiBpUuXYuzYsYITlhg3bhz09PSwdOlSpeOTJk3C48eP8b///U9Qsn8YGRkhOTkZjo6OsLa2xoEDB/D6668jJSUFbdu2xb1790RHxPjx4+Hi4lJqqvjKlSuRmpqKyMhIMcE0SHh4eKXPff77lcpmaGiIs2fPonHjxkrHL126hObNm+Pvv/8WlIxqIo58kUYob71FWFgY0tLS1GK9xZgxY2Bra4slS5bgu+++A1CyDuzbb79F7969Baf7x8qVK9G7d284OTmVWnOxefNm0fHQoEED3Lt3D/7+/vjqq6/wzjvvwNDQUHQshWf3ewoKClK6T09PD05OTsL3fXpq+fLlCAoKQrt27RSjnUVFRejVqxeWLVsmOF2J+fPnIyIiQqnIGj9+PHx8fPDZZ58JLb6efZMrk8mwdu1a7N+/H23btgVQUiRev34dw4YNExVRia2tLe7du6doGBAfH4/XX38dV69eLbONtgg7d+4stWE1ALRv3x4LFixg8VUJiYmJlTpPHWaHFBcXIyoqCrGxscjKyio1u+LXX38VlExZvXr1kJSUVKr4SkpKKrXxMtG/xZEv0ghWVlZYsWIFBg0apHR869atGDduHO7evSsomeZS1wYHa9aswXvvvQcLCwvRUUo5c+YMmjVrBl1dXTg7O+P48eNqsXdSRVJSUpS+1uoyJQkoaT2emJhYKlNKSgq8vLyEtsqu7HQomUymFm8iR44cCQcHB8yaNQurV69GeHg4fHx8cOLECQQEBGDdunWiI8LQ0BDnzp0r9fVOTU2Fh4cHRxhqmLFjxyIqKgo9e/aEnZ1dqYIwIiJCUDJlc+fORUREBKZNm6Y0c+WLL77AxIkT8cknn4iOSDUIiy/SCBYWFkhISChzSkDr1q3VamNOdd0/i/49XV1dZGZmol69emjUqBGOHz8OS0tL0bE0WmBgIFq0aIHJkycrHV+8eDFOnjypFt03q+LGjRuwt7cXMj1WLpdDLpejVq2SSS3fffcdDh8+DBcXF3zwwQdqsc7Pw8MDH3zwQakRzRUrVmDVqlX466+/BCWjV8HKygqbNm3CW2+9JTrKC0mShMjISCxZskSxDYK9vT0mT56M8ePHq8UoItUcLL5II2jCeousrCwMHDgQcXFxMDc3hyRJyM3Nha+vL7Zt26a0eaNo6rYZdEBAAKKiomBqaoqAgIAXnvv999+rKFVplpaW+OWXX9CmTRulQkydaMJ6kOXLlys+zsvLw+LFi+Hj46O05uvIkSMaecXZ1NQUSUlJar8hvCjr16/H2LFjMXnyZHTu3BlAScOVJUuWIDIyEiEhIYITUnWyt7dHXFwcmjRpIjpKpT1tUmRiYiI4CdVULL5II4wbNw6bNm2Cg4NDmestnu3eJ+oNpSbsnwWUbAYdHByMgIAA+Pj4QJIkHD16FLt27UJUVBQGDx6s8kzBwcFYvnw5TExMEBwc/MJzN2zYoKJUpY0aNQqbNm2CnZ0d0tPT0aBBg3I34bxy5YqK05WwsLCAh4cHatWqBZlMVu5aH5FT5ZydnSt1nkwmE/b/+LJMTExw+vRpIcWXs7MzhgwZgsDAQLi6uqr89Str1apV+OyzzxQjDE5OTpg9e7barJ2j6rNkyRJcuXIFK1eu5OgR0f9j8UUaQRPWXpiZmeHgwYN44403lI4nJCTAz89PbaZGaspm0OoqJiYGqampGD9+PObOnVvu1dHQ0FAVJyuho6ODzMxMWFtbc2qkACKLr6VLl2Lr1q04efIkvLy8MHToUAwYMAB2dnYqz1IZd+7cQe3atWFsbCw6Cr0iffv2xaFDh1C3bl00a9as1DYnImcyeHl5VbogVIcNyqnmYLdD0giHDh0SHaFCmrJ/1pUrV/DOO++UOt6rVy98/PHHAhJplu7duwMoWdsXGhqqdlNTLCwscPXqVVhbWyMtLU2tvvfo1QoPD0d4eDguXbqE6OhorFq1CpMnT4avry+GDBmiViNLWVlZuHjxImQyGZo2bap203epepibm6Nv376iY5Tp2c61RKrEkS+iaqIJ+2cBgIuLCyZPnlxq37GvvvoKixcvVuyjpkq8All9Ro0ahY0bN8Le3l5tp0Y+78aNG9izZw/S09Px5MkTpfs0bZ8ikSNfZYmPj8eYMWNw5syZMjcFV7W8vDx89NFH2Lp1q+LCgK6uLgYMGID//e9/SvvmEambrVu3olevXqhTp47oKKTBOPJFVE3Uff+spyZOnIjx48cjKSmpzM2gRdDEK5AVNQZ5liqn1qxZswYBAQGKqZEhISFqNzr3rNjYWPTq1QvOzs64ePEiPDw8kJaWBkmS0LJlS9Hxqkxd1rUkJCRgy5Yt+Pbbb5Gbm4t3331XdCQAJe3wk5KS8PPPP6Ndu3aQyWQ4evQoQkNDERISotgjkUgdjR49Gm3atFGbiyukmTjyRVTN1HX/rGft2rULS5YsUazvetrtUJ02g66I6CuQw4cPx65du2BmZgZvb28AJVMRc3Nz0adPH6U34aKahDzbyERdtW7dGt27d1esnzt9+jSsra0RGBiI7t27Y8yYMaIjVonIka+n0w23bNmCtLQ0+Pr6IjAwEAEBAWrzPVCnTh3s27cP//nPf5SO//HHH+jevTsePXokKBlVl5YtWyI2NhYWFhYVzmrQtJkM6jayTZqJI19E1aCoqAiGhoZISkpCt27d0K1bN9GRylRUVITPPvsMI0aMwOHDh0XH+VdEX4G0sbFB//79sXr1asW0vuLiYnz44YcwNTXFokWLhOR6lsjOkJWVnJys6ARaq1YtPH78GMbGxpg7dy569+6tFsXXsz/fHh4eLzz3r7/+Ukw7VjVXV1d4e3vjo48+wsCBA2Frayskx4tYWlqWObXQzMxMLTdWp6rr3bs3DAwMFB+ry2gwkbpg8UVUDWrVqgVHR0e1WFPxIrVq1cKiRYsQFBQkOsq/JnrQfv369Th8+LDSeipdXV2Eh4ejffv2alF8aYI6deqgoKAAQMmeQJcvX0azZs0AAHfv3hUZTaEqP98ODg4qSFS2CxcuqP1+Sp988gnCw8MVWzYAQGZmJiZPnowZM2YITkfVYdasWYqPZ8+eLS4IkZrSER2AqKb45JNPMH36dNy/f190lBfq2rUr4uLiRMfQeEVFRWW25U9OTmaHwSpo27Ytjhw5AgDo2bMnJk6cqBidfbqnnzrQhJ/vp4XXyZMnsXnzZkRHR6vdtK5Vq1YhPj4ejo6OcHFxgYuLCxo2bIijR4/iq6++QsuWLRU30nzBwcGIjY0VfrGMSJ1w5Iuomixfvhypqamwt7eHo6NjqbVI6vImqEePHpg+fTrOnTuHVq1alcrZq1cvQck0S3BwMEaMGIHU1FSljb/nz59f4UbR9I+lS5fi4cOHAEqukj98+BDffvstXFxcEBERITjdPzTh5zsrKwsDBw5EXFwczM3NIUkScnNz4evri23btqlFO3dNbK5DL+/evXvo2bMnLC0tMXDgQAwdOhQtWrQQHYtIKDbcIKomc+bMeeH9z07FEElHp/wBb5lMpvZTJ58SvfBZLpdj8eLFWLZsGTIyMgCUTJsbP348Jk6cWG57d3o5ohusaMLP94ABA3D58mV88803cHNzA1CyBi0oKAguLi6KtXVEqpSTk4PvvvsOW7ZswR9//IGmTZtiyJAhGDx4MJycnETHqxIPDw/s3btX6PRi0nwsvohII4kuvh4/fgxJkmBkZIS8vDykpaUhNjYW7u7u8Pf3F5KpJjM1NUVSUpKwr/fw4cMRHByMjh07Cnn9yjAzM8PBgwfxxhtvKB1PSEiAn58fcnJyxAQj+n83btzA1q1bsX79eqSkpKCoqEh0JABAo0aNcPz4cVhaWiodz8nJQcuWLdVmT0SqGbjmi4g0kqOjI/T09IS9fu/evbFp0yYAJaNgfn5+WLp0Kfr06YNVq1YJy1VTib5O+ODBA/j7+6Nx48b4/PPPcevWLaF5yiKXy8v8mdDT0xO6DrFu3bqK5ikWFhaoW7duuTequQoLC3HixAkcO3YMaWlpsLGxER1JIS0trcxZHwUFBbh586aARFSTcc0X0b9gYWFR6Ta66rRQPzY2FrGxscjKyir1pmz9+vWCUpWo7BXIc+fOiYincOrUKcWapB07dsDGxgaJiYnYuXMnZs6cqRYt0qn67Ny5E/fu3cPmzZsRFRWFWbNmoWvXrhgxYgT69Okj9ELAU507d0ZoaCi2bt2qaHd/8+ZNhIWFoUuXLsJyRUREKPYZi4yMFJaDxDh06BC2bNmCnTt3ori4GAEBAfjxxx/RuXNn0dGwZ88excf79u1T2gahuLgYsbGxGjc1ktQfpx0S/QsbN25UfHzv3j3MmzcP/v7+aNeuHQDgzz//xL59+zBjxgyEhYWJiqlkzpw5mDt3Lry9vWFnZ1eqeNy1a5egZCV0dHSQmZkJa2trpeO3b99Gw4YNFW3JRTMyMsKFCxfQsGFD9O/fH82aNcOsWbNw/fp1NG3aFPn5+aIj1iiip5k+LzExEevXr8fatWthbGyMIUOG4MMPP0Tjxo2FZbp+/Tp69+6Nc+fOwcHBATKZDOnp6fD09MQPP/yABg0aCMv2VOfOndGxY8dSa+Sys7PRr18//Prrr4KS0avQoEED3Lt3D/7+/ggMDMQ777wDQ0ND0bEUXrQGWk9PD05OTliyZAnefvttFaaimo7FF1E16devH3x9fTF27Fil4ytXrsTBgwexe/duMcGeY2dnh4ULF2Lo0KGioyh5egWyT58+2LhxY5lXIA8cOICLFy+KiqikefPmGDlyJPr27QsPDw/ExMSgXbt2OHnyJHr27InMzEzREWsUdSq+MjIysGnTJqxfvx43b95Ev379kJGRgUOHDmHhwoXCL7QcOHAAFy5cgCRJcHd3R9euXYXmeZaOjg4sLS3h4+OD6OhoRQOV27dvw97eXmMa/lDlrFmzBu+9955abqB95swZNGvWDLq6unB2dsbx48dhZWUlOhZpARZfRNXE2NgYSUlJcHFxUTqekpICLy8vRTtt0SwtLZGQkIDXXntNdBQlmnYFcseOHRg8eDCKi4vRpUsX7N+/HwAwf/58/P7779i7d6/ghDWL6OKrsLAQe/bswYYNG7B//35F8R0YGKiYUrdt2zaMGTMG2dnZQjJqAh0dHSQmJmL06NF49OgRfvzxRzg5ObH4IpXT1dVFZmYm6tWrV+50d6JXgWu+iKqJpaUldu3ahcmTJysd3717t1r9Qh85ciS2bNmCGTNmiI6icObMGRQWFmrUFch3330X//nPf5CRkYHXX39dcbxLly7o27evwGQ1k+gGK3Z2dpDL5Rg0aBASEhLK3KvI398f5ubmKs/2LHVez/mUnZ0dfvvtN4wYMQJvvPEGtm/frmiNT5ovICAAUVFRMDU1RUBAwAvP/f7771WUqjRzc3NcuXIF9erVw7Vr14Q2pSHtwuKLqJrMmTMH77//PuLi4hRrvuLj4xETE4O1a9cKzRYeHq74WC6XY82aNTh48CCaN29e6g3t0qVLVR0PXl5eiiuQMpms0k1MRLO1tYWtra3SsdatWwtKo5k0pcFKREQE3nvvvReuV7GwsMDVq1dVmEpZRes51cHTTAYGBoiOjsa8efPQvXt3TJ06VXAyqi5mZmaKr/Oz08fVTb9+/dCxY0fY2dkBALy9vcvdn5Gt5qk6cdohUTU6duwYli9fjuTkZMV6i/Hjx6NNmzZCc/n6+lb63EOHDr3CJGWztLTEL7/8gjZt2ihNBaGaT1MarGgCdV3P+ayyvt47d+5EUFAQHj9+zGmHpFIxMTFITU3F+PHjMXfuXMUU4ueFhoaqOBnVZBz5IqpGbdq0QXR0tOgYpYgoqKqCVyC1D1s8V78nT56gffv2omO80NWrV0tdWOnXrx9cXV1x4sQJQalIW3Xv3h0AcPLkSYSGhpZbfBFVJ458EVWjy5cvY8OGDbhy5QoiIyNhbW2NmJgYODg4oFmzZqLjAQBGjBiBZcuWlfoj8+jRI4wbN07YuhBegdQumtZgRRNMnToVxsbGarWek7SPl5dXpae8njp16hWnIVI/LL6Iqslvv/2GHj16wMfHB7///juSk5PRqFEjLFy4EAkJCdixY4foiABKOjxlZGSUmuZ19+5d2NraoqioSFCyEsHBwVi+fDmvQNZgbPH8aoSGhmLTpk1o3ry52qznJO0zZ86cSp/7/H5volTUGORZIpuEUM3AaYdE1WTatGmYN28ewsPDlQoHX19fLFu2TGCyEnl5eZAkCZIk4cGDB0qNA4qLi/HLL7+UKshE2LBhg+gI9IppaoMVdXfmzBlFF8bnG5Tw/5hU5WUKqq1bt6JXr16Kfd9UzdTUFLt27YKZmRm8vb0BlExFzM3NRZ8+ffjzQ9WKxRdRNTl79iy2bNlS6ni9evVw7949AYmUmZubK97oNmnSpNT9MpmsSlcsXxVegaz52OL51VD3tZ1E5Rk9ejTatGkjbB8/Gxsb9O/fH6tXr1asNy4uLsaHH34IU1NTLFq0SEguqplYfBFVE3Nzc2RkZMDZ2VnpeGJiIurXry8o1T8OHToESZLQuXNn7Ny5E3Xr1lXcp6+vD0dHR9jb2wtMWIJXIGs+Nlh5tVJTU3H58mV06NABtWvXhiRJ/LkhtSZ6Bcz69etx+PBhpd9Durq6CA8PR/v27Vl8UbVi8UVUTQYPHoypU6di+/btkMlkkMvlOHLkCCZNmoRhw4aJjoeOHTsCKOk25uDg8MKGByLxCmTNt2bNGgQEBCgarISEhHCNXzW4d+8e+vfvj0OHDkEmkyElJQWNGjXCyJEjYW5ujiVLloiOSKSWioqKkJycjKZNmyodT05O5sg8VTs23CCqJoWFhRg+fDi2bdsGSZJQq1YtFBUVITAwEFFRUeVe2RclPz8f6enpePLkidLx5s2bC0pUol69ejh8+HCpP4IXL15E+/bt1WIKJ1UfNlipPsOGDUNWVhbWrl0LNzc3nD59Go0aNcL+/fsRFhaG8+fPi45IVCYTExPF96sI4eHhiIqKwscff4y2bdsCAOLj4zF//nwEBQWxWQ1VK458EVUTPT09REdH49NPP8WJEycgk8ng5eUFFxcX0dGU3LlzB8HBwdi7d2+Z94ve5JRXILULG6xUn/3792Pfvn1o0KCB0vHGjRvj2rVrglIRqb/FixfD1tYWERERyMjIAADY29tj6tSpmDhxouB0VNOw+CKqRuvWrUNERARSUlIAlLzpmTBhAkaOHCk42T8mTJiA7OxsxMfHw9fXF7t27cLt27cxb948tZiWFBwcjBEjRiA1NbXUFcjg4GDB6ai6scFK9Xn06BGMjIxKHb979y4MDAwEJCLSDAUFBRg7diymTJmCvLw8pKWlITY2Fu7u7mo3a4U0H4svomoyY8YMREREYNy4cWjXrh0A4M8//0RYWBjS0tIwb948wQlL/Prrr/jhhx/wxhtvQEdHB46OjujWrRtMTU0xf/589OzZU2g+XoHULmywUn06dOiATZs24dNPPwUAxdrTRYsWwdfXV3A6ovI5OjqW2pdOlXr37o2AgAB88MEHkMvl8PPzg56eHu7evYulS5dizJgxwrJRzcM1X0TVxMrKCitWrMCgQYOUjm/duhXjxo3D3bt3BSVTZmpqijNnzsDJyQlOTk6Ijo6Gj48Prl69imbNmiE/P19ovsePH0OSJBgZGZW6Aunv7y80G1W/qVOn4v79+2ywUg3++usvdOrUCa1atcKvv/6KXr164fz587h//z6OHDmC1157TXRE0jKNGjXC8ePHYWlpqXQ8JycHLVu2VJtuplZWVvjtt9/QrFkzrF27FitWrEBiYiJ27tyJmTNnIjk5WXREqkHUs90ZkQYqLi5WXLl/VqtWrVBUVCQgUdmaNm2KixcvAgBatGiBr776Cjdv3sTq1asVrb9F6t27NzZt2gQAiiuQS5cuRZ8+fbBq1SrB6ai6rV+/HpMmTSqzxfP69esFJtM87u7uOHPmDFq3bo1u3brh0aNHCAgIQGJiIgsvEiItLa3MdcQFBQW4efOmgERly8/PVzT92b9/PwICAqCjo4O2bdtyvSRVO047JKomQ4YMwapVq0p1RVqzZg0CAwMFpSptwoQJiul8s2bNgr+/PzZv3gx9fX1s3LhRcDrg1KlTiIiIAADs2LEDNjY2SlcgOf2jZmGDlepRWFgIPz8/fPXVV2qxWTpptz179ig+3rdvH8zMzBSfFxcXIzY2Fk5OTgKSlc3FxQW7d+9G3759sW/fPoSFhQEAsrKyYGpqKjgd1TQsvoj+hfDwcMXHMpkMa9euxf79+5UaRVy/fl0t9vl66tlCsEWLFkhLS8OFCxfQsGFDWFlZCUxWglcgtQsbrFQPPT09nDt3jmvkSC306dNH8XFQUJDSfXp6enByclKLBk9PzZw5E4MHD0ZYWBi6dOmiWLe9f/9+eHl5CU5HNQ3XfBH9C5VdxC6TyfDrr7++4jSVp85dGZs3b46RI0eib9++8PDwQExMDNq1a4eTJ0+iZ8+eyMzMFB2RqpFcLsfixYuxbNkypQYr48ePx8SJE9lprAomTpwIPT09LFiwQHQU0mJnzpxBs2bNoKurC2dnZxw/flwtLuxVJDMzExkZGXj99deho1OyKichIQGmpqZwdXUVnI5qEhZfRFqmvK6MK1euRGhoqPCujDt27MDgwYNRXFyMLl26YP/+/QCA+fPn4/fffy93fzLSTGywUn3GjRuHTZs2wcXFBd7e3qhTp47S/dwollRBV1cXmZmZqFevXrkNN4i0GYsvIi2jCV0ZeQVSe/j5+SlaPOfk5MDV1ZUtnl/Si0bi1W30nWouS0tL/PLLL2jTpo1SIUZEJVh8EWkZCwsLJCQkoHHjxkrHL126hNatWyMnJ0dMMNJKbPGsejdu3IC9vb3iwgZRdRo1ahQ2bdoEOzs7pKeno0GDBuVOH1aXVvNEqsSGG0RaRlO6MpJ2YIMV1XN3d0dSUhIaNWokOgrVQGvWrEFAQABSU1Mxfvx4hISEKH7GiYjFF5FW0MSujKQd2OJZ9TjhhV617t27AwBOnjyJ0NBQFl9Ez+C0QyItoKldGanmY4MV1TMxMcHp06c58kVEJACLLyIiEooNVlSLxRepSkBAQKXP/f77719hEiL1wWmHREQklK2tLWxtbZWOtW7dWlAaIqoupqam2LVrF8zMzODt7Q2gZCpibm4u+vTpw03BSSux+CIiItIifMNLqmJjY4P+/ftj9erVio6HxcXF+PDDD2FqaopFixYJTkikepx2SEREpEU47ZBUpV69ejh8+DCaNm2qdPzixYto37497t27JygZkTjc5IOIiEjDFRUVoVatWjh37lyF5/71119wdHRUQSrSdkVFRWXu1ZecnAy5XC4gEZF4nHZIRESk4WrVqgVHR0cUFxdXeK6Dg4MKEhEBwcHBGDFiBFJTU5W2Npk/fz6Cg4MFpyMSg9MOiYiIaoANGzZg+/bt2Lx5M+rWrSs6DhHkcjkWL16MZcuWISMjAwBgb2+P8ePHY+LEiYp1YETahMUXERFRDeDl5YXU1FQUFhbC0dERderUUbr/1KlTgpKRtnr8+DEkSYKRkRHy8vKQlpaG2NhYuLu7w9/fX3Q8IiE47ZCIiKgG6NOnj+gIREp69+6NgIAAfPDBB5DL5fDz84Oenh7u3r2LpUuXYsyYMaIjEqkciy8iIqIa4OrVqwgODkbHjh1FRyECUDLaGhERAQDYsWMHbGxskJiYiJ07d2LmzJksvkgrsdshERFRDfDgwQP4+/ujcePG+Pzzz3Hr1i3RkUjL5efnw8TEBACwf/9+BAQEQEdHB23btsW1a9cEpyMSg8UXERFRDbBz507cvHkTY8eOxfbt2+Ho6IgePXpg+/btKCwsFB2PtJCLiwt2796N69evY9++ffDz8wMAZGVlwdTUVHA6IjFYfBEREdUQlpaWCA0NRWJiIhISEuDi4oJhw4bB3t4eYWFhSElJER2RtMjMmTMxadIkODk5oU2bNmjXrh2AklEwLy8vwemIxGC3QyIiohomIyMDmzZtwvr163Hz5k3069cPGRkZOHToEBYuXIiwsDDREUlLZGZmIiMjA6+//jp0dEqu+SckJMDU1BSurq6C0xGpHosvIiKiGqCwsBB79uzBhg0bsH//fjRv3hwjR45EYGCgYt3Ntm3bMGbMGGRnZwtOS0SkndjtkIiIqAaws7ODXC7HoEGDkJCQgBYtWpQ6x9/fH+bm5irPRkREJTjyRUREVAN88803eO+992BoaCg6ChERlYPFFxERERERkQqw2yEREREREZEKsPgiIiIiIiJSARZfREREREREKsDii4iIiIiISAVYfBEREREREakAiy8iIiIiIiIVYPFFRERERESkAiy+iIiIiIiIVOD/APn44BQ3K/C9AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#This code snippet calculates the correlation matrix for numeric columns in the kings_data DataFrame and visualizes it using a heatmap.\n", - "numeric_columns = kings_data.select_dtypes(include=['int64', 'float64'])\n", - "# Calculate the correlation matrix\n", - "correlation_matrix = numeric_columns.corr()\n", - " \n", - "# Plot heatmap\n", - "plt.figure(figsize=(10, 8))\n", - "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", vmin=-1, vmax=1)\n", - "plt.title('Heatmap of Correlation Matrix for Numeric Columns')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Perfect positive correlation (1): Variables increase together perfectly.\n", - "High positive correlation (0.7 to 1): Variables mostly move in the same direction strongly.\n", - "Moderate positive correlation (0.3 to 0.7): Variables tend to move together moderately.\n", - "Weak positive correlation (0 to 0.3): Variables show a weak, inconsistent relationship.\n", - "No correlation (0): Variables are independent of each other.\n", - "Weak negative correlation (-0.3 to 0): Weak, inconsistent negative relationship.\n", - "Moderate negative correlation (-0.7 to -0.3): Moderate negative relationship.\n", - "High negative correlation (-1 to -0.7): Variables move strongly in opposite directions.\n", - "Perfect negative correlation (-1): Variables decrease together perfectly." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "house_data_clean.drop(columns=['date', 'sqft_lot', 'condition', 'zipcode', 'long', 'sqft_lot15', 'yr_built', 'lat'], inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Analysis for categorical columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code snippet visualizes the relationship between the 'waterfront' feature and the average sale price. A bar plot is used to show the average price for properties with and without waterfront. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Categorical = ['waterfront', 'condition', 'grade'] \n", - "\n", - "# How waterfront relates to saleprice\n", - "# plot the barplot\n", - "plt.figure(figsize = (7,5))\n", - "kings_data.groupby('waterfront')['price'].mean().plot.bar()\n", - "\n", - "# set the axes and title\n", - "plt.xlabel(column)\n", - "plt.ylabel('Average price')\n", - "plt.title('Waterfont vs Sales')\n", - "\n", - "# display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above clearly shows that houses with waterfronts are the most popular and sells the most" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code snippet visualizes the relationship between the 'condition' feature and the average sale price. A bar plot is used to show the average price for properties with different conditions. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# How condition relates to saleprice\n", - "# plot the barplot\n", - "plt.figure(figsize = (7,5))\n", - "kings_data.groupby('condition')['price'].mean().plot.bar()\n", - "\n", - "# set the axes and title\n", - "plt.xlabel(column)\n", - "plt.ylabel('Average price')\n", - "plt.title('Condition vs Sales')\n", - "\n", - "# display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The houses that are in good cnditions are the most popular" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code snippet visualizes the relationship between the 'grade' feature and the average sale price. A bar plot is used to show the average price for properties with different grades. " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# How grade relates to saleprice\n", - "# plot the barplot\n", - "plt.figure(figsize = (7,5))\n", - "kings_data.groupby('grade')['price'].mean().plot.bar()\n", - "\n", - "# set the axes and title\n", - "plt.xlabel(column)\n", - "plt.ylabel('Average price')\n", - "plt.title('Grade vs Sales')\n", - "\n", - "# display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "compairing mansions with the others u can tell its the highest selling " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#This code snippet iterates through the categorical columns specified in the list `Categorical` and creates bar charts to visualize the count of each category\n", - "for column in Categorical:\n", - " plt.figure()\n", - " kings_data[column].value_counts().plot.bar()\n", - " plt.xlabel(column)\n", - " plt.ylabel('Count')\n", - " plt.title(column)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "#This function creates subplots of boxplots for each numeric column in the DataFrame, allowing for visualization of the data distribution and identification of outliers. Adjust the parameters and example usage according to your DataFrame and preferences.\n", - "\n", - "def check_for_outliers_and_plot_boxplot(df, numeric_columns_list, figsize=(12, 12)):\n", - " \n", - " # Calculate number of subplots\n", - " num_plots = len(numeric_columns)\n", - " num_cols = 2 # Number of columns in each row of subplots\n", - " num_rows = (num_plots + 1) // num_cols\n", - " \n", - " # Create subplots\n", - " fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n", - " axes = axes.flatten()\n", - "\n", - "\n", - " # Plot boxplots for each column\n", - " for i, column in enumerate(numeric_columns):\n", - " sns.boxplot(x=df[column], ax=axes[i])\n", - " axes[i].set_title(f'Boxplot of {column}')\n", - " axes[i].set_xlabel(column)\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "check_for_outliers_and_plot_boxplot(house_data_clean, numeric_columns, figsize=(12, 12))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the illustration you can see the data set has outliers " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function removes outliers from the specified column(s) in the DataFrame based on Z-scores, with an option to set a custom threshold. ." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "def remove_outliers_by_zscore(df, col, threshold=3):\n", - " \n", - " # Iterate over each specified column\n", - " for column in col:\n", - " # Calculate Z-scores for the column\n", - " z_scores = (df[column] - df[column].mean()) / df[column].std()\n", - " # Filter rows where Z-score exceeds the threshold\n", - " df_cleaned = df[(z_scores.abs() < threshold)]\n", - " \n", - " return df_cleaned" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
071293005202014221900.031.00118056501.0NONONEAverage7 Average11800.019559817847.5112-122.25713405650
164141001922014538000.032.25257072422.0NONONEAverage7 Average2170400.019519812547.7210-122.31916907639
256315004002015180000.021.00770100001.0NONONEAverage6 Low Average7700.019339802847.7379-122.23327208062
324872008752014604000.043.00196050001.0NONONEVery Good7 Average1050910.019659813647.5208-122.39313605000
419544005102015510000.032.00168080801.0NONONEAverage8 Good16800.019879807447.6168-122.04518007503
...............................................................
215922630000182014360000.032.50153011313.0NONONEAverage8 Good15300.020099810347.6993-122.34615301509
2159366000601202015400000.042.50231058132.0NONONEAverage8 Good23100.020149814647.5107-122.36218307200
2159415233001412014402101.020.75102013502.0NONONEAverage7 Average10200.020099814447.5944-122.29910202007
215952913101002015400000.032.50160023882.0NONONEAverage8 Good16000.020049802747.5345-122.06914101287
2159615233001572014325000.020.75102010762.0NONONEAverage7 Average10200.020089814447.5941-122.29910201357
\n", - "

21173 rows × 20 columns

\n", - "
" - ], - "text/plain": [ - " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", - "0 7129300520 2014 221900.0 3 1.00 1180 5650 \n", - "1 6414100192 2014 538000.0 3 2.25 2570 7242 \n", - "2 5631500400 2015 180000.0 2 1.00 770 10000 \n", - "3 2487200875 2014 604000.0 4 3.00 1960 5000 \n", - "4 1954400510 2015 510000.0 3 2.00 1680 8080 \n", - "... ... ... ... ... ... ... ... \n", - "21592 263000018 2014 360000.0 3 2.50 1530 1131 \n", - "21593 6600060120 2015 400000.0 4 2.50 2310 5813 \n", - "21594 1523300141 2014 402101.0 2 0.75 1020 1350 \n", - "21595 291310100 2015 400000.0 3 2.50 1600 2388 \n", - "21596 1523300157 2014 325000.0 2 0.75 1020 1076 \n", - "\n", - " floors waterfront view condition grade sqft_above \\\n", - "0 1.0 NO NONE Average 7 Average 1180 \n", - "1 2.0 NO NONE Average 7 Average 2170 \n", - "2 1.0 NO NONE Average 6 Low Average 770 \n", - "3 1.0 NO NONE Very Good 7 Average 1050 \n", - "4 1.0 NO NONE Average 8 Good 1680 \n", - "... ... ... ... ... ... ... \n", - "21592 3.0 NO NONE Average 8 Good 1530 \n", - "21593 2.0 NO NONE Average 8 Good 2310 \n", - "21594 2.0 NO NONE Average 7 Average 1020 \n", - "21595 2.0 NO NONE Average 8 Good 1600 \n", - "21596 2.0 NO NONE Average 7 Average 1020 \n", - "\n", - " sqft_basement yr_built zipcode lat long sqft_living15 \\\n", - "0 0.0 1955 98178 47.5112 -122.257 1340 \n", - "1 400.0 1951 98125 47.7210 -122.319 1690 \n", - "2 0.0 1933 98028 47.7379 -122.233 2720 \n", - "3 910.0 1965 98136 47.5208 -122.393 1360 \n", - "4 0.0 1987 98074 47.6168 -122.045 1800 \n", - "... ... ... ... ... ... ... \n", - "21592 0.0 2009 98103 47.6993 -122.346 1530 \n", - "21593 0.0 2014 98146 47.5107 -122.362 1830 \n", - "21594 0.0 2009 98144 47.5944 -122.299 1020 \n", - "21595 0.0 2004 98027 47.5345 -122.069 1410 \n", - "21596 0.0 2008 98144 47.5941 -122.299 1020 \n", - "\n", - " sqft_lot15 \n", - "0 5650 \n", - "1 7639 \n", - "2 8062 \n", - "3 5000 \n", - "4 7503 \n", - "... ... \n", - "21592 1509 \n", - "21593 7200 \n", - "21594 2007 \n", - "21595 1287 \n", - "21596 1357 \n", - "\n", - "[21173 rows x 20 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cleaned_data = remove_outliers_by_zscore(house_data_clean, numeric_columns, threshold=3)\n", - "cleaned_data\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will return a DataFrame with outliers removed from the specified numeric columns based on their Z-scores, using a threshold of 3." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\USER PC\\AppData\\Local\\Temp\\ipykernel_17800\\1295920640.py:3: UserWarning: \n", - "\n", - "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n", - "\n", - "Please adapt your code to use either `displot` (a figure-level function with\n", - "similar flexibility) or `histplot` (an axes-level function for histograms).\n", - "\n", - "For a guide to updating your code to use the new functions, please see\n", - "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n", - "\n", - " price_dist = sns.distplot(kings_data[\"price\"])\n", - "c:\\Users\\USER PC\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Project Submission\n", + "\n", + "* Student name: Solphine Joseph, Grace Rotich, Mathew Kiprotich, Hilary Simiyu, Clyde Ochieng, Derrick Kiptoo. \n", + "* Student pace: full time\n", + "* Scheduled project review date/time: \n", + "* Instructor name: Nikita \n", + "* Blog post URL:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kings County Housing Analysis with Multiple Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "A real estate agency in Kingsway seeks to determine what are the contributing factors that affect the price of houses to make improvements where necessary. They want to employ an analytical approach rather than sentimental before arriving at a decision. Multilinear regression has been used for this project to understand how various features affect their pricing to better their services." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Business Problem\n", + "\n", + "In the face of market fluctuations and heightened competition within the real estate sector, our agency is grappling with pricing volatility, which poses significant challenges for our agents in devising effective business strategies. We seek strategic guidance to optimize our purchasing and selling endeavors, prioritizing informed decision-making to identify key areas of focus that promise maximum returns on investment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objectives\n", + "* To determine the key factors influencing house prices.\n", + "* To develop multilinear regression models to predict house prices based on relevant features.\n", + "* To use insights from the regression analysis to optimize pricing strategies for both purchasing and selling properties.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hypothesis\n", + "* Null Hypothesis - There is no relationship between our independent variables and our dependent variable \n", + "\n", + "* Alternative Hypothesis - There is a relationship between our independent variables and our dependent variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Understanding:\n", + "\n", + "In this project, we utilized the King County House Sales dataset, which serves as the foundational dataset for our analysis. It was sourced Kaggle.The dataset encompasses comprehensive information regarding house sales within King County, Washington, USA. It comprises a diverse array of features, including the number of bedrooms, bathrooms, square footage, as well as geographical and pricing details of the properties sold. This dataset is frequently employed in data science and machine learning endeavors, particularly for predictive modeling tasks such as regression analysis aimed at forecasting house prices based on the provided features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### King County Housing Data Columns \n", + "\n", + "The column names contained in column_names.md are:\n", + "* `id`: A unique identifier for each house sale.\n", + "* `date`: The date when the house was sold.\n", + "* `price`: The sale price of the house, serving as the target variable for predictive modeling.\n", + "* `bedrooms`, `bathrooms`, `sqft_living`, `sqft_lot`: Numerical features representing the number of bedrooms and bathrooms, as well as the living area and lot area of the house, respectively.\n", + "* `floors`: The number of floors in the house.\n", + "* `waterfront`, `view`, `condition`, `grade`: Categorical features describing aspects such as waterfront availability, property view, condition, and overall grade assigned to the housing unit.\n", + "* `yr_built`, `yr_renovated`: Year of construction and renovation of the house.\n", + "* `zipcode`, `lat`, `long`: Geographical features including ZIP code, latitude, and longitude coordinates.\n", + "* `sqft_above`, `sqft_basement`, `sqft_living15`, `sqft_lot15`: Additional numerical features providing details about the house's above-ground and basement square footage, as well as living area and lot area of the nearest 15 neighboring houses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading\n", + "\n", + "#### Import Necessary Libraries\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.stats as stats\n", + "import seaborn as sns\n", + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a function that loads data and return it in a dataframe\n", + "def load_data(file_path):\n", + " house_data = pd.read_csv(file_path)\n", + "\n", + " #shape\n", + " shape = house_data.shape\n", + " print(f\"The dataset contains {shape[0]} houses with {shape[1]} features\")\n", + " print()\n", + " \n", + " #Data Types\n", + " data_types = house_data.dtypes\n", + " print(\"Columns and their data types:\")\n", + " for column, dtype in data_types.items():\n", + " print(f\"{column}: {dtype}\")\n", + " print()\n", + "\n", + " return house_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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" ], - "source": [ - "# Plotting a histogram with kernel density estimate (KDE) of the \"price\" variable\n", - "plt.figure(figsize=(10,4))\n", - "price_dist = sns.distplot(kings_data[\"price\"])\n", - "price_dist.set(xlabel=\"Price in Millions\", title=\"Price Density of Houses in King's County\")\n", - "plt.savefig('Visualization2') # Save the plot as an image file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The observation reveals that the distribution of house is right-skewed.we look at house prices, we notice that most houses are not very expensive, but there are a few that are very pricey." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "text/plain": [ + " id date price bedrooms bathrooms sqft_living \\\n", + "0 7129300520 10/13/2014 221900.0 3 1.00 1180 \n", + "1 6414100192 12/9/2014 538000.0 3 2.25 2570 \n", + "2 5631500400 2/25/2015 180000.0 2 1.00 770 \n", + "3 2487200875 12/9/2014 604000.0 4 3.00 1960 \n", + "4 1954400510 2/18/2015 510000.0 3 2.00 1680 \n", + "... ... ... ... ... ... ... \n", + "21592 263000018 5/21/2014 360000.0 3 2.50 1530 \n", + "21593 6600060120 2/23/2015 400000.0 4 2.50 2310 \n", + "21594 1523300141 6/23/2014 402101.0 2 0.75 1020 \n", + "21595 291310100 1/16/2015 400000.0 3 2.50 1600 \n", + "21596 1523300157 10/15/2014 325000.0 2 0.75 1020 \n", + "\n", + " sqft_lot floors waterfront view ... grade sqft_above \\\n", + "0 5650 1.0 NaN NONE ... 7 Average 1180 \n", + "1 7242 2.0 NO NONE ... 7 Average 2170 \n", + "2 10000 1.0 NO NONE ... 6 Low Average 770 \n", + "3 5000 1.0 NO NONE ... 7 Average 1050 \n", + "4 8080 1.0 NO NONE ... 8 Good 1680 \n", + "... ... ... ... ... ... ... ... \n", + "21592 1131 3.0 NO NONE ... 8 Good 1530 \n", + "21593 5813 2.0 NO NONE ... 8 Good 2310 \n", + "21594 1350 2.0 NO NONE ... 7 Average 1020 \n", + "21595 2388 2.0 NaN NONE ... 8 Good 1600 \n", + "21596 1076 2.0 NO NONE ... 7 Average 1020 \n", + "\n", + " sqft_basement yr_built yr_renovated zipcode lat long \\\n", + "0 0.0 1955 0.0 98178 47.5112 -122.257 \n", + "1 400.0 1951 1991.0 98125 47.7210 -122.319 \n", + "2 0.0 1933 NaN 98028 47.7379 -122.233 \n", + "3 910.0 1965 0.0 98136 47.5208 -122.393 \n", + "4 0.0 1987 0.0 98074 47.6168 -122.045 \n", + "... ... ... ... ... ... ... \n", + "21592 0.0 2009 0.0 98103 47.6993 -122.346 \n", + "21593 0.0 2014 0.0 98146 47.5107 -122.362 \n", + "21594 0.0 2009 0.0 98144 47.5944 -122.299 \n", + "21595 0.0 2004 0.0 98027 47.5345 -122.069 \n", + "21596 0.0 2008 0.0 98144 47.5941 -122.299 \n", + "\n", + " sqft_living15 sqft_lot15 \n", + "0 1340 5650 \n", + "1 1690 7639 \n", + "2 2720 8062 \n", + "3 1360 5000 \n", + "4 1800 7503 \n", + "... ... ... \n", + "21592 1530 1509 \n", + "21593 1830 7200 \n", + "21594 1020 2007 \n", + "21595 1410 1287 \n", + "21596 1020 1357 \n", + "\n", + "[21597 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_data('data/kc_house_data.csv') # Assuming 'data' folder is in the same directory\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset contains 21 columns, each representing a distinct feature, and 21,597 rows, with each row corresponding to a specific house sale entry.\n", + "\n", + "The dataset contains a mix of data types, including integers (int64), floating-point numbers (float64), and objects (strings). For instance, numerical features such as bedrooms, bathrooms, and sqft_living are represented as integers or floating-point numbers to facilitate mathematical computations, while categorical features like waterfront and view are stored as objects to accommodate text-based categories." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 21597 houses with 21 features\n", + "\n", + "Columns and their data types:\n", + "id: int64\n", + "date: object\n", + "price: float64\n", + "bedrooms: int64\n", + "bathrooms: float64\n", + "sqft_living: int64\n", + "sqft_lot: int64\n", + "floors: float64\n", + "waterfront: object\n", + "view: object\n", + "condition: object\n", + "grade: object\n", + "sqft_above: int64\n", + "sqft_basement: object\n", + "yr_built: int64\n", + "yr_renovated: float64\n", + "zipcode: int64\n", + "lat: float64\n", + "long: float64\n", + "sqft_living15: int64\n", + "sqft_lot15: int64\n", + "\n" + ] + } + ], + "source": [ + "kings_data = load_data('data/kc_house_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function that takes in a column and returns the column statistics as a dictionary\n", + "def descriptive_analytics(column):\n", + " stats_dict = column.describe().to_dict()\n", + " \n", + " print(\"Descriptive Statistics for Column '{}':\".format(column.name))\n", + " print(\"The count of the column is:\", stats_dict['count'])\n", + " print(\"The mean of the column is:\", stats_dict['mean'])\n", + " print(\"The standard deviation of the column is:\", stats_dict['std'])\n", + " print(\"The minimum value of the column is:\", stats_dict['min'])\n", + " print(\"The 25th percentile of the column is:\", stats_dict['25%'])\n", + " print(\"The median of the column is:\", stats_dict['50%'])\n", + " print(\"The 75th percentile of the column is:\", stats_dict['75%'])\n", + " print(\"The maximum value of the column is:\", stats_dict['max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Descriptive Statistics for Column 'price':\n", + "The count of the column is: 21597.0\n", + "The mean of the column is: 540296.5735055795\n", + "The standard deviation of the column is: 367368.1401013945\n", + "The minimum value of the column is: 78000.0\n", + "The 25th percentile of the column is: 322000.0\n", + "The median of the column is: 450000.0\n", + "The 75th percentile of the column is: 645000.0\n", + "The maximum value of the column is: 7700000.0\n" + ] + } + ], + "source": [ + "descriptive_analytics(kings_data['price'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the maximum price of a house is 7700000 dollars and the minimum price is 78000 dollars\n", + "\n", + "There are 21597 prices regarding to the houses in the dataset\n", + "\n", + "Average price of a house is 540296.57 dollars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preperation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 21597 entries, 0 to 21596\n", + "Data columns (total 21 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 21597 non-null int64 \n", + " 1 date 21597 non-null object \n", + " 2 price 21597 non-null float64\n", + " 3 bedrooms 21597 non-null int64 \n", + " 4 bathrooms 21597 non-null float64\n", + " 5 sqft_living 21597 non-null int64 \n", + " 6 sqft_lot 21597 non-null int64 \n", + " 7 floors 21597 non-null float64\n", + " 8 waterfront 19221 non-null object \n", + " 9 view 21534 non-null object \n", + " 10 condition 21597 non-null object \n", + " 11 grade 21597 non-null object \n", + " 12 sqft_above 21597 non-null int64 \n", + " 13 sqft_basement 21597 non-null object \n", + " 14 yr_built 21597 non-null int64 \n", + " 15 yr_renovated 17755 non-null float64\n", + " 16 zipcode 21597 non-null int64 \n", + " 17 lat 21597 non-null float64\n", + " 18 long 21597 non-null float64\n", + " 19 sqft_living15 21597 non-null int64 \n", + " 20 sqft_lot15 21597 non-null int64 \n", + "dtypes: float64(6), int64(9), object(6)\n", + "memory usage: 3.5+ MB\n" + ] + } + ], + "source": [ + "kings_data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def identify_issues(dataset):\n", + " # Identify missing values as a percentage of the whole dataset\n", + " missing_values = (dataset.isnull().sum())/len(dataset) * 100\n", + "\n", + " # Identify duplicates\n", + " duplicates = dataset.duplicated().sum()\n", + " \n", + " #return a dictionary \n", + " return {'duplicates': duplicates,\n", + " 'missing values': missing_values.round(2)} \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 0,\n", + " 'missing values': id 0.00\n", + " date 0.00\n", + " price 0.00\n", + " bedrooms 0.00\n", + " bathrooms 0.00\n", + " sqft_living 0.00\n", + " sqft_lot 0.00\n", + " floors 0.00\n", + " waterfront 11.00\n", + " view 0.29\n", + " condition 0.00\n", + " grade 0.00\n", + " sqft_above 0.00\n", + " sqft_basement 0.00\n", + " yr_built 0.00\n", + " yr_renovated 17.79\n", + " zipcode 0.00\n", + " lat 0.00\n", + " long 0.00\n", + " sqft_living15 0.00\n", + " sqft_lot15 0.00\n", + " dtype: float64}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(kings_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examination indicates that there are no duplicate entries within the dataset, ensuring the integrity of the records. However, attention is warranted to address missing values present in certain columns. Specifically, the 'waterfront' feature exhibits 11% of null values, representing a negligible portion of the dataset. Similarly, the 'yr_renovated' feature shows a relatively higher percentage of missing values, accounting for approximately 17.79% of the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Before making changes make a copy instead of overwriting data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean = kings_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Changing the date to date time\n", + "house_data_clean['date'] = pd.to_datetime(house_data_clean['date'])\n", + "\n", + "# Extracting only the year from the column Date\n", + "house_data_clean.date = house_data_clean['date'].dt.year\n", + "\n", + "# Changing the dates for the year built \n", + "house_data_clean['yr_built'] = pd.to_datetime(house_data_clean['yr_built'],format='%Y').dt.year\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code converts the 'date' column data to only contain the year the house was sold, for the purpose of analysis we will use only the year since the changes month by month will be minor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dealing with the missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def missing_values(dataset):\n", + " # drop the rows from views\n", + " dataset.dropna(subset=['view'],inplace=True)\n", + "\n", + " # Filling the NaN values for waterfront with NO\n", + " dataset.waterfront.fillna('NO',inplace=True)\n", + " \n", + " # Dropping the yr_renovated column \n", + " dataset.drop('yr_renovated',axis=1,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "missing_values(house_data_clean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'yr_renovated' has the highest percentage of NaN values 17%. This will be dropped since it will not be used within our model inline with the business problem.\n", + "\n", + "'Waterfront' feature has 11% null values, this was filled with NO on the assumption that these cells were not filled since they lacked waterfronts\n", + "\n", + "For the 'View' column, the null values were dropped by row since the overall percentage impact would be minute" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'duplicates': 2,\n", + " 'missing values': id 0.0\n", + " date 0.0\n", + " price 0.0\n", + " bedrooms 0.0\n", + " bathrooms 0.0\n", + " sqft_living 0.0\n", + " sqft_lot 0.0\n", + " floors 0.0\n", + " waterfront 0.0\n", + " view 0.0\n", + " condition 0.0\n", + " grade 0.0\n", + " sqft_above 0.0\n", + " sqft_basement 0.0\n", + " yr_built 0.0\n", + " zipcode 0.0\n", + " lat 0.0\n", + " long 0.0\n", + " sqft_living15 0.0\n", + " sqft_lot15 0.0\n", + " dtype: float64}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_issues(house_data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtzipcodelatlongsqft_living15sqft_lot15
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\n", + "
" ], - "source": [ - "#This code snippet creates a scatter plot matrix for the house data, with each numeric variable plotted against the 'price'.\n", - "# creating a list of all column names\n", - "plot_list = list(house_data_clean.columns)\n", - "len(plot_list)\n", - "fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(19,20))\n", - "axes = axes.flatten() # flatten the array to make it easier to iterate over\n", - "\n", - "for i, xcol in enumerate(plot_list[0:19]):\n", - " fig.patch.set_facecolor('whitesmoke')\n", - " house_data_clean.plot(kind='scatter', x=xcol, y='price', ax=axes[i], alpha=0.4, color='Purple', marker='^')\n", - " \n", - "plt.show() # add this to display the plot" - ] + "text/plain": [ + " id date price bedrooms bathrooms sqft_living sqft_lot \\\n", + "3947 1825069031 2014 550000.0 4 1.75 2410 8447 \n", + "20038 8648900110 2014 555000.0 3 2.50 1940 3211 \n", + "\n", + " floors waterfront view condition grade sqft_above sqft_basement \\\n", + "3947 2.0 NO GOOD Good 8 Good 2060 350.0 \n", + "20038 2.0 NO NONE Average 8 Good 1940 0.0 \n", + "\n", + " yr_built zipcode lat long sqft_living15 sqft_lot15 \n", + "3947 1936 98074 47.6499 -122.088 2520 14789 \n", + "20038 2009 98027 47.5644 -122.093 1880 3078 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" + ], + "source": [ + "house_data_clean[house_data_clean.duplicated()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploratory Data Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### King County Housing Analysis and Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the exploratory phase of data analysis, histograms and box plots play pivotal roles in understanding the distribution patterns of variables. Histograms provide a visual representation of the frequency and spread of values within each variable, offering valuable insights into the data's central tendencies and variability. Meanwhile, box plots offer a concise summary of the data's distribution, including measures of central tendency, variability, and the presence of potential outliers. Together, these visualizations provide a comprehensive overview of the dataset's characteristics, laying the groundwork for deeper analysis and informing subsequent modeling and decision-making processes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_statistics(df, column_name):\n", + " #calculate mean\n", + " mean_value = df[column_name].mean()\n", + " #calculate mode, and pick the first if multiple mode exist\n", + " mode_value = df[column_name].mode()[0]\n", + " #calculating median\n", + " median_value = house_data_clean[column_name].median()\n", + " #calculating std\n", + " std_value = df[column_name].std()\n", + "\n", + " # Create a dictionary to store the statistics\n", + " statistics = {\n", + " \"Mean\": mean_value,\n", + " \"Mode\": mode_value,\n", + " \"Median\": median_value,\n", + " \"Standard Deviation\": std_value\n", + " }\n", + " return statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Mean': 540057.663833937,\n", + " 'Mode': 350000.0,\n", + " 'Median': 450000.0,\n", + " 'Standard Deviation': 366059.58123129635}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "calculate_statistics(house_data_clean, \"price\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet categorizes columns with numeric values from the DataFrame house_data_clean and prints them out" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "These are columns with numerical values:\n", + " ['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'sqft_above', 'yr_built', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15']\n" + ] + } + ], + "source": [ + "# Initialize lists for numeric columns\n", + "numeric_columns = []\n", + "\n", + "\n", + "# Iterate through columns and categorize them based on data type\n", + "for column in house_data_clean.columns:\n", + " if house_data_clean[column].dtype in ['int64', 'float64']: # Check if the column data type is numeric\n", + " numeric_columns.append(column)\n", + " \n", + " \n", + "print (\"These are columns with numerical values:\\n\",numeric_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#This code snippet calculates the correlation matrix for numeric columns in the kings_data DataFrame and visualizes it using a heatmap.\n", + "numeric_columns = kings_data.select_dtypes(include=['int64', 'float64'])\n", + "# Calculate the correlation matrix\n", + "correlation_matrix = numeric_columns.corr()\n", + " \n", + "# Plot heatmap\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", vmin=-1, vmax=1)\n", + "plt.title('Heatmap of Correlation Matrix for Numeric Columns')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect positive correlation (1): Variables increase together perfectly.\n", + "High positive correlation (0.7 to 1): Variables mostly move in the same direction strongly.\n", + "Moderate positive correlation (0.3 to 0.7): Variables tend to move together moderately.\n", + "Weak positive correlation (0 to 0.3): Variables show a weak, inconsistent relationship.\n", + "No correlation (0): Variables are independent of each other.\n", + "Weak negative correlation (-0.3 to 0): Weak, inconsistent negative relationship.\n", + "Moderate negative correlation (-0.7 to -0.3): Moderate negative relationship.\n", + "High negative correlation (-1 to -0.7): Variables move strongly in opposite directions.\n", + "Perfect negative correlation (-1): Variables decrease together perfectly." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "house_data_clean.drop(columns=['date', 'sqft_lot', 'condition', 'zipcode', 'long', 'sqft_lot15', 'yr_built', 'lat'], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analysis for categorical columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet visualizes the relationship between the 'waterfront' feature and the average sale price. A bar plot is used to show the average price for properties with and without waterfront. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Categorical = ['waterfront', 'condition', 'grade'] \n", + "\n", + "# How waterfront relates to saleprice\n", + "# plot the barplot\n", + "plt.figure(figsize = (7,5))\n", + "kings_data.groupby('waterfront')['price'].mean().plot.bar()\n", + "\n", + "# set the axes and title\n", + "plt.xlabel(column)\n", + "plt.ylabel('Average price')\n", + "plt.title('Waterfont vs Sales')\n", + "\n", + "# display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above clearly shows that houses with waterfronts are the most popular and sells the most" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet visualizes the relationship between the 'condition' feature and the average sale price. A bar plot is used to show the average price for properties with different conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# How condition relates to saleprice\n", + "# plot the barplot\n", + "plt.figure(figsize = (7,5))\n", + "kings_data.groupby('condition')['price'].mean().plot.bar()\n", + "\n", + "# set the axes and title\n", + "plt.xlabel(column)\n", + "plt.ylabel('Average price')\n", + "plt.title('Condition vs Sales')\n", + "\n", + "# display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The houses that are in good cnditions are the most popular" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code snippet visualizes the relationship between the 'grade' feature and the average sale price. A bar plot is used to show the average price for properties with different grades. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# How grade relates to saleprice\n", + "# plot the barplot\n", + "plt.figure(figsize = (7,5))\n", + "kings_data.groupby('grade')['price'].mean().plot.bar()\n", + "\n", + "# set the axes and title\n", + "plt.xlabel(column)\n", + "plt.ylabel('Average price')\n", + "plt.title('Grade vs Sales')\n", + "\n", + "# display the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "compairing mansions with the others u can tell its the highest selling " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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7ry5tE8py3/aIiOigJoJkLLA/cLbt1wK/pxzG6ke7cQ8P0P7sF5BmSeqR1LN27dpNrTciIgbQRJCsBlbbvrk8X0wVLI+Uw1WUn4+2bD+pZf+JwJrSPrFN+7PYnmd7qu2pXV1dW+yDREREA0Fi+xfAQ5L2Kk1HACuBJcDM0jYTuLQsLwFmSNpO0mSqQfVl5fDXE5IOLrO1Tm7ZJyIiOmSwl5Hf0j4AXCBpW+Be4N1UobaonDX/INXdGLG9QtIiqrBZB5xue315ndOA+VSXa7miPCIiooMaCRLbP6U6Q76vI/rZfi4wt017DxtOlIwGdM++vOkSuP+Mo5ouIWJUy10OIyKilgRJRETUkiCJiIhaEiQREVFLgiQiImpJkERERC0JkoiIqCVBEhERtSRIIiKilgRJRETUkiCJiIhaEiQREVFLgiQiImpJkERERC0JkoiIqCVBEhERtSRIIiKilgRJRETUkiCJiIhaEiQREVFLgiQiImppLEgkjZF0m6TLyvNdJF0t6Z7yc+eWbedIWiXpbklHtrQfIGl5WXemJDXxWSIiRrMmeyT/ANzV8nw2sNT2FGBpeY6kvYEZwD7ANOAsSWPKPmcDs4Ap5TGtM6VHRESvRoJE0kTgKOBbLc3TgQVleQFwdEv7QttP2b4PWAUcJGk8sKPtG20bOK9ln4iI6JCmeiRfAT4BPN3StrvthwHKz91K+wTgoZbtVpe2CWW5b/uzSJolqUdSz9q1a7fIB4iIiErHg0TSW4FHbd8y2F3atHmA9mc32vNsT7U9taura5BvGxERgzG2gfc8BHi7pL8Ftgd2lPRvwCOSxtt+uBy2erRsvxqY1LL/RGBNaZ/Ypj0iIjqo4z0S23NsT7TdTTWIfo3tk4AlwMyy2Uzg0rK8BJghaTtJk6kG1ZeVw19PSDq4zNY6uWWfiIjokCZ6JP05A1gk6VTgQeBYANsrJC0CVgLrgNNtry/7nAbMB8YBV5RHRER0UKNBYvs64Lqy/EvgiH62mwvMbdPeA+w7dBVGRMTG5Mz2iIioJUESERG1JEgiIqKWBElERNSSIImIiFoSJBERUUuCJCIiakmQRERELQmSiIioJUESERG1JEgiIqKWBElERNSSIImIiFoSJBERUUuCJCIiakmQRERELQmSiIioJUESERG1JEgiIqKWBElERNTS8SCRNEnStZLukrRC0j+U9l0kXS3pnvJz55Z95khaJeluSUe2tB8gaXlZd6YkdfrzRESMdk30SNYBH7X9SuBg4HRJewOzgaW2pwBLy3PKuhnAPsA04CxJY8prnQ3MAqaUx7ROfpCIiGggSGw/bPvWsvwEcBcwAZgOLCibLQCOLsvTgYW2n7J9H7AKOEjSeGBH2zfaNnBeyz4REdEhjY6RSOoGXgvcDOxu+2GowgbYrWw2AXioZbfVpW1CWe7bHhERHdRYkEjaAfgu8CHbvx1o0zZtHqC93XvNktQjqWft2rWbXmxERPSrkSCR9ByqELnA9iWl+ZFyuIry89HSvhqY1LL7RGBNaZ/Ypv1ZbM+zPdX21K6uri33QSIiopFZWwLOAe6y/aWWVUuAmWV5JnBpS/sMSdtJmkw1qL6sHP56QtLB5TVPbtknIiI6ZGwD73kI8C5guaSflrZPAWcAiySdCjwIHAtge4WkRcBKqhlfp9teX/Y7DZgPjAOuKI+IiOigjgeJ7R/RfnwD4Ih+9pkLzG3T3gPsu+Wqi9h83bMvb7oE7j/jqKZLiFEoZ7ZHREQtCZKIiKglQRIREbUkSCIiopYESURE1JIgiYiIWhIkERFRS4IkIiJqSZBEREQtCZKIiKglQRIREbUkSCIiopYESURE1JIgiYiIWhIkERFRS4IkIiJqSZBEREQtCZKIiKglQRIREbUkSCIiopYESURE1DLig0TSNEl3S1olaXbT9UREjDZjmy6gDkljgH8F3gysBn4iaYntlc1WFjG6dc++vOkSuP+Mo5ouYdQY6T2Sg4BVtu+1/SdgITC94ZoiIkYV2W66hs0m6Rhgmu33lOfvAl5n+/19tpsFzCpP9wLu7mih7e0KPNZ0EcNEvotKvocN8l1sMFy+i5fY7mq3YkQf2gLUpu1ZyWh7HjBv6MsZPEk9tqc2XcdwkO+iku9hg3wXG4yE72KkH9paDUxqeT4RWNNQLRERo9JID5KfAFMkTZa0LTADWNJwTRERo8qIPrRle52k9wM/BMYA59pe0XBZgzWsDrU1LN9FJd/DBvkuNhj238WIHmyPiIjmjfRDWxER0bAESURE1JIgiYiIWkb0YHvESCTpa7Q536mX7Q92sJwYZsqlnxbYPqnpWgYrQdJBkg4Fptj+tqQuYAfb9zVdV6dI2n+g9bZv7VQtDespPw8B9gYuLs+PBW5ppKKGSdoGuMP2vk3X0jTb6yV1Sdq2XPpp2MusrQ6R9BlgKrCX7ZdLejHwHduHNFxax0i6tixuT/Vd3E51dYJXAzfbPrSp2ppQvo+32P5zef4c4Crbb2q2smZIugCYY/vBpmtpmqRvAPtTnRf3+952219qrKgBpEfSOe8AXgvcCmB7jaTnN1tSZ/X+gpS0EJhle3l5vi/wsSZra8iLgecDj5fnO5S20Wo8sELSMp75y/PtzZXUmDXlsQ3Vv5FhLUHSOX+ybUkGkPS8pgtq0Ct6QwTA9p2S9muwnqacAdzW0lP7K+CzzZXTuM81XcBwYftzAOWPTdv+XcMlDSiHtjpE0seAKVT3TvlH4BTgQttfa7SwBki6iOovzn+jGnQ+iWq86IRGC2uApBcBr6P6HpbZ/kXDJcUwUHrp5wO7lKbHgJOH65U7EiQdJOnNwFuoxgV+aPvqhktqhKTtgdOAN5amG4CzbT/ZXFXNkPR2NnwP19v+QZP1NEHSj2wfKukJnjmbTVR/je/YUGmNkfRj4NO2ry3PDwM+b/sNTdbVnwRJNKJcZHMvql8cd/cOOI8mks4ADgQuKE0nAD225zRXVQwHkm63/ZqNtQ0XCZIOafPXFsBvqKaCftT2vZ2vqhnlr6sFwP1Uf3VOAmbavqG5qjpP0h3AfrafLs/HALfZfnWzlTVL0m5UM/sAGI2zuCR9j2pizvml6SRgqu2jGytqABls75wvUc3CuJDql+cM4EVUd2s8Fzissco674tU017vBpD0cuAi4IBGq2rGC9gwa2unButoXDnM90WqmWuPAi8B7gL2abKuhpxCNfngEqrfFzcA7260ogGkR9Ihkm62/bo+bTfZPng4d1mHgqQ7+v7V3a5tayfpBKqZW9dS/bJ4I9V5FAsbLawhkm4HDgf+3fZrJb0JOMH2rI3sutWStCPw9HCftZVrbXXO05KOk7RNeRzXsm60pXmPpHMkHVYe32QUntFt+yLgYKq/Oi8BXj9aQ6T4s+1fAttI2qYMNO/XcE2NkPQqSbcBy6nOrbmlzOQalnJoq3NOBL4KnEUVHDcBJ0kaB7y/ycIacBpwOvBBNnTbz2q0ouYcyIZZW08Do27WVotfS9qB6t/DBZIeBdY1XFNTvgF8pM+srXlAZm1F9Mqsrcza6iVpD9sPlpN0/0h1pOREqjGjC0ovZVTJrK1oq5w7cSrVwGHrjJRTGiuqIZm1VcmsrYqkW23vX5a/a/vvmq6paSNt1lbGSDrnfKpZWkcC1wMTgScarag5vbO2/sr2G6m+ky83XFNTXtCyPFpnball+aWNVTG8nAJ0sWH8bFeG8aytjJF0zp62j5U03fYCSRcCP2y6qIY8p3fqL4Dtn5cr3442/8iGa239ZdZWsyU1wv0sjzrlyMX7gD2pBto/OhIO+yZIOqf3H8Ovy+yLXwDdzZXTqB5J5/DMbvuonLUl6TqqcRIBnxyl19p6jaTfUn0H48oyjM5LpCyg+l3xH8DfAK8EPtRkQYORMZIOkfQe4LvAq4D5VJcM/9+2v9FkXU2QtB3VrK1DaJm1NVJu4lNXGQsZ13tugKSDgW3L6ttsj9ZDnqOepOW2X1WWx1JdyHPAG8INB+mRdEC5+9tvbf+K6pfmqDwOLGk6MNH2vwJfkjSD6jjwfsBqYHGD5XXSF6jO3P6n8vxC4E5gHNUA6ycbqiua95fDWLbXSRpo22EjPZIOkXRDGVgetST9JzDD9kPl+U+pzmTeAfi27SMaLK9jyolmB9pe1/u8nMkt4D9G250iYwNJ69lwUy9R/XHxB4b5Yb70SDrn6nJPkot55t3fHu9/l63Otr0hUvyofP7HR9mNvrbpDZHik1D9lign5MUoZXtM0zVsjvRIOkTSfW2abXvUHOaStMr2nv2s+y/bL+t0TU2QdBdwUN+xEEk7Ud27/hXNVBaxeXIeSYfYntzmMWpCpLhZ0nv7Nkr6H8CyBuppyjeBiyXt0dsg6SVUV0D+ZmNVRWym9Eg6RNJzgY8Ae9ieJWkKsJftyxourWPKfSa+DzxFNagM1aXjtwOOtv1IQ6V1nKT3AZ8Cnkd17sTvgTNsn91oYRGbIUHSIZIupjpX4mTb+5aLNd5oe79mK+s8SYez4R4TK2xf02Q9TSpjIsqU3xjJEiQdIqnH9tTeGTqlbdhehC0iYrAyRtI5fyq9EANIehnVIZ6IiBEt038757PAlcAkSRdQndX9900WFBGxJeTQVgdJeiHVHfEE3GT7sYZLigZJ6gG+DVxYrnoQMSLl0FaHSFoCvAW4zvZlCZEAZgAvBn4iaaGkIzVSrokR0SI9kg6R9FfA8cBRVOdMXAxcZvvJRguLxpVrsb0VOJvqdrvnAl8dZVc9iBEsQdJh5cqvhwPvBaYN12vnRGdIejXVDYv+lur+NBcAhwLvGo1Tw2NkymB7B5VZW2+j6pnsT3U5+RilJN0C/Bo4B5htu3cW382SDmmssIhNlCDpkHJC4uuoZm59HVhPFSgxCpXDWd+1/fl2622/s8MlRWy2DLZ3zreBY4HfluXPAXc1WlE0xvbTwLSm64jYEtIjGWKSXk41O+cE4JdUg+yy/aZGC4vhILcWiK1CBtuHmKSnqe6/fKrtVaXt3lF45d/oI7cWiK1FeiRD7++oeiTXSroSWEh1QmKMcrYnN11DxJaQHkmHlDsAHk11iOtwYAHwPdtXNVlXNCe3FoitRYKkAZJ2oRp4P9724U3XE83IrQVia5EgiWhIbi0QW4tM/41oTm4tEFuFDLZHNOez5NYCsRXIoa2IDpP0dapLx/84txaIrUF6JBGddw/wRUnjqU5GvMj2T5stKWLzpUcS0RBJL6E6x2gGsD1wEbDQ9s8bLSxiEyVIIoYBSa+lug/Jq22PabqeiE2RWVsRDZH0HElvKwPtVwA/p7oSQsSIkh5JRIdJejPVFQ5675a5EPi+7d8PuGPEMJUgiegwSdcCF1LdjyRX+o0RL0ESERG1ZIwkIiJqSZBEREQtCZKIYUDSfEnHlOVvSdq7LH+qz3Y/bqK+iIFkjCRiGJA0H7jM9uI+7b+zvUMzVUUMTnokEZtB0smS7pB0u6TzJb1E0tLStlTSHmW7+ZLOlPRjSfe29Dok6euSVkq6HNit5bWvkzRV0hnAOEk/LeeaIOl3Lfv/s6Q7JS2XdHxpP6zsv1jSzyRdICl35IwhlWttRWwiSfsAnwYOsf1YuVHZAuA82wsknQKcSXVHTIDxwKHAK4AlwGLgHcBewKuA3YGVVGe2/4Xt2ZLe38+Nrt4J7Ae8BtgV+ImkG8q61wL7AGuA/6S6qvCPan/wiH6kRxKx6Q4HFvdeqbecC/J6qnNDAM6nCo5e37f9tO2VVKEB8EaqizWut70GuGYTazi0Zf9HgOuBA8u6ZbZX234a+CnQvYmvHbFJEiQRm06Um1ENoHV9682q1M82m1NDf1rfbz058hBDLEESsemWAseVe4lQDm39mOoqvgAnsvFDSTcAMySNKZeTf1M/2/1Z0nP62f/4sn8XVQ9n2SZ+jogtIn+pRGwi2yskzQWul7QeuA34IHCupI8Da4F3b+Rlvkd1iGw51cUar+9nu3nAHZJutX1in/1fD9xO1bP5hO1fSHrF5n6uiM2V6b8REVFLDm1FREQtCZKIiKglQRIREbUkSCIiopYESURE1JIgiYiIWhIkERFRS4IkIiJq+f9fHjPkiNGHwwAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#This code snippet iterates through the categorical columns specified in the list `Categorical` and creates bar charts to visualize the count of each category\n", + "for column in Categorical:\n", + " plt.figure()\n", + " kings_data[column].value_counts().plot.bar()\n", + " plt.xlabel(column)\n", + " plt.ylabel('Count')\n", + " plt.title(column)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "#This function creates subplots of boxplots for each numeric column in the DataFrame, allowing for visualization of the data distribution and identification of outliers. Adjust the parameters and example usage according to your DataFrame and preferences.\n", + "\n", + "def check_for_outliers_and_plot_boxplot(df, numeric_columns_list, figsize=(12, 12)):\n", + " \n", + " # Calculate number of subplots\n", + " num_plots = len(numeric_columns)\n", + " num_cols = 2 # Number of columns in each row of subplots\n", + " num_rows = (num_plots + 1) // num_cols\n", + " \n", + " # Create subplots\n", + " fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n", + " axes = axes.flatten()\n", + "\n", + "\n", + " # Plot boxplots for each column\n", + " for i, column in enumerate(numeric_columns):\n", + " sns.boxplot(x=df[column], ax=axes[i])\n", + " axes[i].set_title(f'Boxplot of {column}')\n", + " axes[i].set_xlabel(column)\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "check_for_outliers_and_plot_boxplot(house_data_clean, numeric_columns, figsize=(12, 12))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the illustration you can see the data set has outliers " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function removes outliers from the specified column(s) in the DataFrame based on Z-scores, with an option to set a custom threshold. ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_outliers_by_zscore(df, col, threshold=3):\n", + " \n", + " # Iterate over each specified column\n", + " for column in col:\n", + " # Calculate Z-scores for the column\n", + " z_scores = (df[column] - df[column].mean()) / df[column].std()\n", + " # Filter rows where Z-score exceeds the threshold\n", + " df_cleaned = df[(z_scores.abs() < threshold)]\n", + " \n", + " return df_cleaned" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cleaned_data = remove_outliers_by_zscore(house_data_clean, numeric_columns, threshold=3)\n", + "cleaned_data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will return a DataFrame with outliers removed from the specified numeric columns based on their Z-scores, using a threshold of 3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting a histogram with kernel density estimate (KDE) of the \"price\" variable\n", + "plt.figure(figsize=(10,4))\n", + "price_dist = sns.distplot(kings_data[\"price\"])\n", + "price_dist.set(xlabel=\"Price in Millions\", title=\"Price Density of Houses in King's County\")\n", + "plt.savefig('Visualization2') # Save the plot as an image file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The observation reveals that the distribution of house is right-skewed.we look at house prices, we notice that most houses are not very expensive, but there are a few that are very pricey." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#This code snippet creates a scatter plot matrix for the house data, with each numeric variable plotted against the 'price'.\n", + "# creating a list of all column names\n", + "plot_list = list(house_data_clean.columns)\n", + "len(plot_list)\n", + "fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(19,20))\n", + "axes = axes.flatten() # flatten the array to make it easier to iterate over\n", + "\n", + "for i, xcol in enumerate(plot_list[0:19]):\n", + " fig.patch.set_facecolor('whitesmoke')\n", + " house_data_clean.plot(kind='scatter', x=xcol, y='price', ax=axes[i], alpha=0.4, color='Purple', marker='^')\n", + " \n", + "plt.show() # add this to display the plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Linear Model\n", + "\n", + "From our EDA we can see that 'sqft_living' has the highest correlation with price, thus we'll use this as the independent variable for our analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create variables for our independent and dependent features\n", + "X = house_data_clean['sqft_living']\n", + "y = house_data_clean['price']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function to create regression models\n", + "def regression(X_value, y_value):\n", + " # create the model\n", + " model = sm.OLS(y_value, sm.add_constant(X_value))\n", + " \n", + " #Fit the model\n", + " results = model.fit()\n", + "\n", + " return results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#call the simple_regression function and store output in results variable\n", + "simple_model_results = regression(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#create a function for analysing regression results\n", + "def regression_analysis(regression_results):\n", + " #Models P_Value\n", + " results_pvalue = regression_results.f_pvalue\n", + "\n", + " #Model Rsquared\n", + " r_squared = regression_results.rsquared\n", + "\n", + " #Parameters\n", + " coeffecients = regression_results.params\n", + "\n", + " return {'Model R-Squared': r_squared,\n", + " 'Model P_value': results_pvalue,\n", + " 'Coeffecients': coeffecients, \n", + " }\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#calling the simple regression analysis function to get summary\n", + "simple_regression_analysis(simple_model_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the results above we get that:\n", + "Our model is statistically significant, with a p-value well below the standard alpha of 0.05\n", + "\n", + "Our model explains about 43.0% of the variance in home price, the dependent variable\n", + "\n", + "For a house with 0 squarefoot of living area, our model would predict a home price of about 15203.833 dollars. \n", + "\n", + "An increase of 1 square foot in living area is associated with an increase of about 280.86 dollars in house price." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assumptions of linear regression\n", + "\n", + "Linear regression makes several assumptions about the data, such as :\n", + "\n", + "Linearity of the data. The relationship between the predictor (x) and the outcome (y) is assumed to be linear.\n", + "\n", + "Normality of residuals. The residual errors are assumed to be normally distributed.\n", + "\n", + "Homogeneity of residuals variance. The residuals are assumed to have a constant variance (homoscedasticity)\n", + "\n", + "Independence of residuals error terms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# creating a function to check the assumptions of linear regression\n", + "def check_regression_assumptions(results):\n", + " \n", + " # Residuals\n", + " residuals = results.resid\n", + " \n", + " # Create subplots\n", + " fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + " \n", + " # Linearity Check (Fitted Values vs. Residuals Plot)\n", + " sns.residplot(x=results.fittedvalues, y=residuals, lowess=True, line_kws={'color': 'red', 'lw': 1}, ax=axes[0, 0])\n", + " axes[0, 0].set_title('Linearity Check: Fitted Values vs. Residuals')\n", + " axes[0, 0].set_xlabel('Fitted values')\n", + " axes[0, 0].set_ylabel('Residuals')\n", + " \n", + " # Independence of Residuals (Durbin-Watson Statistic)\n", + " dw_statistic = sm.stats.stattools.durbin_watson(residuals)\n", + " axes[0, 1].text(0.5, 0.5, f'Durbin-Watson statistic: {dw_statistic}', fontsize=12, ha='center')\n", + " axes[0, 1].axis('off') \n", + " \n", + " # Homoscedasticity Check (Residuals vs. Fitted Values Plot)\n", + " sns.residplot(x=results.fittedvalues, y=residuals, lowess=True, line_kws={'color': 'red', 'lw': 1}, ax=axes[1, 0])\n", + " axes[1, 0].set_title('Homoscedasticity Check: Fitted Values vs. Residuals')\n", + " axes[1, 0].set_xlabel('Fitted values')\n", + " axes[1, 0].set_ylabel('Residuals')\n", + " \n", + " # Normality of Residuals Check (Histogram and Q-Q Plot)\n", + " sns.histplot(residuals, kde=True, color='blue', bins=20, ax=axes[1, 1])\n", + " axes[1, 1].set_title('Histogram of Residuals')\n", + " axes[1, 1].set_xlabel('Residuals')\n", + " axes[1, 1].set_ylabel('Frequency')\n", + " \n", + " # Show plot\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# call our function to check on our simple regression model\n", + "check_regression_assumptions(simple_model_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the output above we can interpret the results as:\n", + "\n", + "* Durbin-Watson statistic of approximately 1.98, shows that there is no significant autocorrelation in the residuals, which is a favorable result for your regression analysis\n", + "* The linearity check plot shows a random scatter of points around the horizontal axis, which suggests that the linearity assumption is reasonable, indicating that the relationship between the independent variables and the dependent variable is approximately linear.\n", + "* In the homoscedasticity plot we can observe a pattern of a 'funnel shape', it suggests that the homoscedasticity assumption may be violated, indicating heteroscedasticity.\n", + "* The Q-Q plot, shows the points fall close to the diagonal line, it suggests that the residuals follow a normal distribution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Shortcomings of the simple linear regression\n", + "\n", + "Simple linear regression assumes that the residuals are normally distributed with constant variance. However, in real-world data, the assumptions of normality and homoscedasticity may be violated, leading to biased estimates and unreliable inference\n", + "\n", + "House prices are influenced by a multitude of factors beyond just square footage. Ignoring these factors in favor of a simple linear model may result in omitted variable bias and inaccurate predictions.\n", + "\n", + "The r-squared value indicates that our simple regression model only explains 43.0% variation in home price and this is not extremely high" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From 2f65aa7e919c1ca2af622147b86789fba5da251e Mon Sep 17 00:00:00 2001 From: HSimiyu Date: Wed, 1 May 2024 16:19:55 +0000 Subject: [PATCH 48/53] create model results --- student.ipynb | 76 +++++++++++++++++++++++++-------------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/student.ipynb b/student.ipynb index 7005a4f3..79a6c82d 100644 --- a/student.ipynb +++ b/student.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ "[21597 rows x 21 columns]" ] }, - "execution_count": 3, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -563,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -713,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -731,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -762,7 +762,7 @@ " dtype: float64}" ] }, - "execution_count": 9, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -826,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -843,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -863,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ " dtype: float64}" ] }, - "execution_count": 14, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1015,7 +1015,7 @@ "20038 2009 98027 47.5644 -122.093 1880 3078 " ] }, - "execution_count": 15, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1080,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1092,7 +1092,7 @@ " 'Standard Deviation': 366059.58123129635}" ] }, - "execution_count": 17, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1138,12 +1138,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1184,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1207,12 +1207,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1256,12 +1256,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1303,12 +1303,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1343,12 +1343,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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7ry5tE8py3/aIiOigJoJkLLA/cLbt1wK/pxzG6ke7cQ8P0P7sF5BmSeqR1LN27dpNrTciIgbQRJCsBlbbvrk8X0wVLI+Uw1WUn4+2bD+pZf+JwJrSPrFN+7PYnmd7qu2pXV1dW+yDREREA0Fi+xfAQ5L2Kk1HACuBJcDM0jYTuLQsLwFmSNpO0mSqQfVl5fDXE5IOLrO1Tm7ZJyIiOmSwl5Hf0j4AXCBpW+Be4N1UobaonDX/INXdGLG9QtIiqrBZB5xue315ndOA+VSXa7miPCIiooMaCRLbP6U6Q76vI/rZfi4wt017DxtOlIwGdM++vOkSuP+Mo5ouIWJUy10OIyKilgRJRETUkiCJiIhaEiQREVFLgiQiImpJkERERC0JkoiIqCVBEhERtSRIIiKilgRJRETUkiCJiIhaEiQREVFLgiQiImpJkERERC0JkoiIqCVBEhERtSRIIiKilgRJRETUkiCJiIhaEiQREVFLgiQiImppLEgkjZF0m6TLyvNdJF0t6Z7yc+eWbedIWiXpbklHtrQfIGl5WXemJDXxWSIiRrMmeyT/ANzV8nw2sNT2FGBpeY6kvYEZwD7ANOAsSWPKPmcDs4Ap5TGtM6VHRESvRoJE0kTgKOBbLc3TgQVleQFwdEv7QttP2b4PWAUcJGk8sKPtG20bOK9ln4iI6JCmeiRfAT4BPN3StrvthwHKz91K+wTgoZbtVpe2CWW5b/uzSJolqUdSz9q1a7fIB4iIiErHg0TSW4FHbd8y2F3atHmA9mc32vNsT7U9taura5BvGxERgzG2gfc8BHi7pL8Ftgd2lPRvwCOSxtt+uBy2erRsvxqY1LL/RGBNaZ/Ypj0iIjqo4z0S23NsT7TdTTWIfo3tk4AlwMyy2Uzg0rK8BJghaTtJk6kG1ZeVw19PSDq4zNY6uWWfiIjokCZ6JP05A1gk6VTgQeBYANsrJC0CVgLrgNNtry/7nAbMB8YBV5RHRER0UKNBYvs64Lqy/EvgiH62mwvMbdPeA+w7dBVGRMTG5Mz2iIioJUESERG1JEgiIqKWBElERNSSIImIiFoSJBERUUuCJCIiakmQRERELQmSiIioJUESERG1JEgiIqKWBElERNSSIImIiFoSJBERUUuCJCIiakmQRERELQmSiIioJUESERG1JEgiIqKWBElERNTS8SCRNEnStZLukrRC0j+U9l0kXS3pnvJz55Z95khaJeluSUe2tB8gaXlZd6YkdfrzRESMdk30SNYBH7X9SuBg4HRJewOzgaW2pwBLy3PKuhnAPsA04CxJY8prnQ3MAqaUx7ROfpCIiGggSGw/bPvWsvwEcBcwAZgOLCibLQCOLsvTgYW2n7J9H7AKOEjSeGBH2zfaNnBeyz4REdEhjY6RSOoGXgvcDOxu+2GowgbYrWw2AXioZbfVpW1CWe7bHhERHdRYkEjaAfgu8CHbvx1o0zZtHqC93XvNktQjqWft2rWbXmxERPSrkSCR9ByqELnA9iWl+ZFyuIry89HSvhqY1LL7RGBNaZ/Ypv1ZbM+zPdX21K6uri33QSIiopFZWwLOAe6y/aWWVUuAmWV5JnBpS/sMSdtJmkw1qL6sHP56QtLB5TVPbtknIiI6ZGwD73kI8C5guaSflrZPAWcAiySdCjwIHAtge4WkRcBKqhlfp9teX/Y7DZgPjAOuKI+IiOigjgeJ7R/RfnwD4Ih+9pkLzG3T3gPsu+Wqi9h83bMvb7oE7j/jqKZLiFEoZ7ZHREQtCZKIiKglQRIREbUkSCIiopYESURE1JIgiYiIWhIkERFRS4IkIiJqSZBEREQtCZKIiKglQRIREbUkSCIiopYESURE1JIgiYiIWhIkERFRS4IkIiJqSZBEREQtCZKIiKglQRIREbUkSCIiopYESURE1DLig0TSNEl3S1olaXbT9UREjDZjmy6gDkljgH8F3gysBn4iaYntlc1WFjG6dc++vOkSuP+Mo5ouYdQY6T2Sg4BVtu+1/SdgITC94ZoiIkYV2W66hs0m6Rhgmu33lOfvAl5n+/19tpsFzCpP9wLu7mih7e0KPNZ0EcNEvotKvocN8l1sMFy+i5fY7mq3YkQf2gLUpu1ZyWh7HjBv6MsZPEk9tqc2XcdwkO+iku9hg3wXG4yE72KkH9paDUxqeT4RWNNQLRERo9JID5KfAFMkTZa0LTADWNJwTRERo8qIPrRle52k9wM/BMYA59pe0XBZgzWsDrU1LN9FJd/DBvkuNhj238WIHmyPiIjmjfRDWxER0bAESURE1JIgiYiIWkb0YHvESCTpa7Q536mX7Q92sJwYZsqlnxbYPqnpWgYrQdJBkg4Fptj+tqQuYAfb9zVdV6dI2n+g9bZv7VQtDespPw8B9gYuLs+PBW5ppKKGSdoGuMP2vk3X0jTb6yV1Sdq2XPpp2MusrQ6R9BlgKrCX7ZdLejHwHduHNFxax0i6tixuT/Vd3E51dYJXAzfbPrSp2ppQvo+32P5zef4c4Crbb2q2smZIugCYY/vBpmtpmqRvAPtTnRf3+952219qrKgBpEfSOe8AXgvcCmB7jaTnN1tSZ/X+gpS0EJhle3l5vi/wsSZra8iLgecDj5fnO5S20Wo8sELSMp75y/PtzZXUmDXlsQ3Vv5FhLUHSOX+ybUkGkPS8pgtq0Ct6QwTA9p2S9muwnqacAdzW0lP7K+CzzZXTuM81XcBwYftzAOWPTdv+XcMlDSiHtjpE0seAKVT3TvlH4BTgQttfa7SwBki6iOovzn+jGnQ+iWq86IRGC2uApBcBr6P6HpbZ/kXDJcUwUHrp5wO7lKbHgJOH65U7EiQdJOnNwFuoxgV+aPvqhktqhKTtgdOAN5amG4CzbT/ZXFXNkPR2NnwP19v+QZP1NEHSj2wfKukJnjmbTVR/je/YUGmNkfRj4NO2ry3PDwM+b/sNTdbVnwRJNKJcZHMvql8cd/cOOI8mks4ADgQuKE0nAD225zRXVQwHkm63/ZqNtQ0XCZIOafPXFsBvqKaCftT2vZ2vqhnlr6sFwP1Uf3VOAmbavqG5qjpP0h3AfrafLs/HALfZfnWzlTVL0m5UM/sAGI2zuCR9j2pizvml6SRgqu2jGytqABls75wvUc3CuJDql+cM4EVUd2s8Fzissco674tU017vBpD0cuAi4IBGq2rGC9gwa2unButoXDnM90WqmWuPAi8B7gL2abKuhpxCNfngEqrfFzcA7260ogGkR9Ihkm62/bo+bTfZPng4d1mHgqQ7+v7V3a5tayfpBKqZW9dS/bJ4I9V5FAsbLawhkm4HDgf+3fZrJb0JOMH2rI3sutWStCPw9HCftZVrbXXO05KOk7RNeRzXsm60pXmPpHMkHVYe32QUntFt+yLgYKq/Oi8BXj9aQ6T4s+1fAttI2qYMNO/XcE2NkPQqSbcBy6nOrbmlzOQalnJoq3NOBL4KnEUVHDcBJ0kaB7y/ycIacBpwOvBBNnTbz2q0ouYcyIZZW08Do27WVotfS9qB6t/DBZIeBdY1XFNTvgF8pM+srXlAZm1F9Mqsrcza6iVpD9sPlpN0/0h1pOREqjGjC0ovZVTJrK1oq5w7cSrVwGHrjJRTGiuqIZm1VcmsrYqkW23vX5a/a/vvmq6paSNt1lbGSDrnfKpZWkcC1wMTgScarag5vbO2/sr2G6m+ky83XFNTXtCyPFpnball+aWNVTG8nAJ0sWH8bFeG8aytjJF0zp62j5U03fYCSRcCP2y6qIY8p3fqL4Dtn5cr3442/8iGa239ZdZWsyU1wv0sjzrlyMX7gD2pBto/OhIO+yZIOqf3H8Ovy+yLXwDdzZXTqB5J5/DMbvuonLUl6TqqcRIBnxyl19p6jaTfUn0H48oyjM5LpCyg+l3xH8DfAK8EPtRkQYORMZIOkfQe4LvAq4D5VJcM/9+2v9FkXU2QtB3VrK1DaJm1NVJu4lNXGQsZ13tugKSDgW3L6ttsj9ZDnqOepOW2X1WWx1JdyHPAG8INB+mRdEC5+9tvbf+K6pfmqDwOLGk6MNH2vwJfkjSD6jjwfsBqYHGD5XXSF6jO3P6n8vxC4E5gHNUA6ycbqiua95fDWLbXSRpo22EjPZIOkXRDGVgetST9JzDD9kPl+U+pzmTeAfi27SMaLK9jyolmB9pe1/u8nMkt4D9G250iYwNJ69lwUy9R/XHxB4b5Yb70SDrn6nJPkot55t3fHu9/l63Otr0hUvyofP7HR9mNvrbpDZHik1D9lign5MUoZXtM0zVsjvRIOkTSfW2abXvUHOaStMr2nv2s+y/bL+t0TU2QdBdwUN+xEEk7Ud27/hXNVBaxeXIeSYfYntzmMWpCpLhZ0nv7Nkr6H8CyBuppyjeBiyXt0dsg6SVUV0D+ZmNVRWym9Eg6RNJzgY8Ae9ieJWkKsJftyxourWPKfSa+DzxFNagM1aXjtwOOtv1IQ6V1nKT3AZ8Cnkd17sTvgTNsn91oYRGbIUHSIZIupjpX4mTb+5aLNd5oe79mK+s8SYez4R4TK2xf02Q9TSpjIsqU3xjJEiQdIqnH9tTeGTqlbdhehC0iYrAyRtI5fyq9EANIehnVIZ6IiBEt038757PAlcAkSRdQndX9900WFBGxJeTQVgdJeiHVHfEE3GT7sYZLigZJ6gG+DVxYrnoQMSLl0FaHSFoCvAW4zvZlCZEAZgAvBn4iaaGkIzVSrokR0SI9kg6R9FfA8cBRVOdMXAxcZvvJRguLxpVrsb0VOJvqdrvnAl8dZVc9iBEsQdJh5cqvhwPvBaYN12vnRGdIejXVDYv+lur+NBcAhwLvGo1Tw2NkymB7B5VZW2+j6pnsT3U5+RilJN0C/Bo4B5htu3cW382SDmmssIhNlCDpkHJC4uuoZm59HVhPFSgxCpXDWd+1/fl2622/s8MlRWy2DLZ3zreBY4HfluXPAXc1WlE0xvbTwLSm64jYEtIjGWKSXk41O+cE4JdUg+yy/aZGC4vhILcWiK1CBtuHmKSnqe6/fKrtVaXt3lF45d/oI7cWiK1FeiRD7++oeiTXSroSWEh1QmKMcrYnN11DxJaQHkmHlDsAHk11iOtwYAHwPdtXNVlXNCe3FoitRYKkAZJ2oRp4P9724U3XE83IrQVia5EgiWhIbi0QW4tM/41oTm4tEFuFDLZHNOez5NYCsRXIoa2IDpP0dapLx/84txaIrUF6JBGddw/wRUnjqU5GvMj2T5stKWLzpUcS0RBJL6E6x2gGsD1wEbDQ9s8bLSxiEyVIIoYBSa+lug/Jq22PabqeiE2RWVsRDZH0HElvKwPtVwA/p7oSQsSIkh5JRIdJejPVFQ5675a5EPi+7d8PuGPEMJUgiegwSdcCF1LdjyRX+o0RL0ESERG1ZIwkIiJqSZBEREQtCZKIYUDSfEnHlOVvSdq7LH+qz3Y/bqK+iIFkjCRiGJA0H7jM9uI+7b+zvUMzVUUMTnokEZtB0smS7pB0u6TzJb1E0tLStlTSHmW7+ZLOlPRjSfe29Dok6euSVkq6HNit5bWvkzRV0hnAOEk/LeeaIOl3Lfv/s6Q7JS2XdHxpP6zsv1jSzyRdICl35IwhlWttRWwiSfsAnwYOsf1YuVHZAuA82wsknQKcSXVHTIDxwKHAK4AlwGLgHcBewKuA3YGVVGe2/4Xt2ZLe38+Nrt4J7Ae8BtgV+ImkG8q61wL7AGuA/6S6qvCPan/wiH6kRxKx6Q4HFvdeqbecC/J6qnNDAM6nCo5e37f9tO2VVKEB8EaqizWut70GuGYTazi0Zf9HgOuBA8u6ZbZX234a+CnQvYmvHbFJEiQRm06Um1ENoHV9682q1M82m1NDf1rfbz058hBDLEESsemWAseVe4lQDm39mOoqvgAnsvFDSTcAMySNKZeTf1M/2/1Z0nP62f/4sn8XVQ9n2SZ+jogtIn+pRGwi2yskzQWul7QeuA34IHCupI8Da4F3b+Rlvkd1iGw51cUar+9nu3nAHZJutX1in/1fD9xO1bP5hO1fSHrF5n6uiM2V6b8REVFLDm1FREQtCZKIiKglQRIREbUkSCIiopYESURE1JIgiYiIWhIkERFRS4IkIiJq+f9fHjPkiNGHwwAAAABJRU5ErkJggg==\n", 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", 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\n", 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", 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" ] @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1426,7 +1426,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ From 514e95c21c577327813992b423b725bc702aa7d9 Mon Sep 17 00:00:00 2001 From: HSimiyu Date: Wed, 1 May 2024 17:01:26 +0000 Subject: [PATCH 49/53] simple linear regression done --- student.ipynb | 174 ++++++++++++++++++++++++++++++++++---------------- 1 file changed, 118 insertions(+), 56 deletions(-) diff --git a/student.ipynb b/student.ipynb index 79a6c82d..8e7566cf 100644 --- a/student.ipynb +++ b/student.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ "[21597 rows x 21 columns]" ] }, - "execution_count": 30, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -563,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -713,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -731,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -762,7 +762,7 @@ " dtype: float64}" ] }, - "execution_count": 36, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -826,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -843,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -863,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ " dtype: float64}" ] }, - "execution_count": 41, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1015,7 +1015,7 @@ "20038 2009 98027 47.5644 -122.093 1880 3078 " ] }, - "execution_count": 42, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1080,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1092,7 +1092,7 @@ " 'Standard Deviation': 366059.58123129635}" ] }, - "execution_count": 44, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1138,12 +1138,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -1184,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1207,12 +1207,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -1256,12 +1256,12 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -1303,12 +1303,12 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -1343,12 +1343,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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", 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\n", 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", 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\n", 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" ] @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1426,7 +1426,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1449,7 +1449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1467,9 +1467,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'sqft_lot'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2894\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2896\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'sqft_lot'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcleaned_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mremove_outliers_by_zscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhouse_data_clean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumeric_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mcleaned_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36mremove_outliers_by_zscore\u001b[1;34m(df, col, threshold)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Calculate Z-scores for the column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mz_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;31m# Filter rows where Z-score exceeds the threshold\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mdf_cleaned\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2900\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2901\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2903\u001b[0m \u001b[1;32mif\u001b[0m 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\u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2899\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'sqft_lot'" + ] + } + ], "source": [ "cleaned_data = remove_outliers_by_zscore(house_data_clean, numeric_columns, threshold=3)\n", "cleaned_data\n" @@ -1504,9 +1527,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#This code snippet creates a scatter plot matrix for the house data, with each numeric variable plotted against the 'price'.\n", "# creating a list of all column names\n", @@ -1533,7 +1567,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1544,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1561,7 +1595,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1571,7 +1605,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1595,12 +1629,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Model R-Squared': 0.4922247077876325,\n", + " 'Model P_value': 0.0,\n", + " 'Coeffecients': const -42152.946806\n", + " sqft_living 279.932115\n", + " dtype: float64}" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#calling the simple regression analysis function to get summary\n", - "simple_regression_analysis(simple_model_results)" + "regression_analysis(simple_model_results)" ] }, { @@ -1610,11 +1659,11 @@ "From the results above we get that:\n", "Our model is statistically significant, with a p-value well below the standard alpha of 0.05\n", "\n", - "Our model explains about 43.0% of the variance in home price, the dependent variable\n", + "Our model explains about 49.2% of the variance in home price, the dependent variable\n", "\n", - "For a house with 0 squarefoot of living area, our model would predict a home price of about 15203.833 dollars. \n", + "For a house with 0 squarefoot of living area, our model would predict a home price of about -42152.94 dollars. \n", "\n", - "An increase of 1 square foot in living area is associated with an increase of about 280.86 dollars in house price." + "An increase of 1 square foot in living area is associated with an increase of about 279.93 dollars in house price." ] }, { @@ -1636,7 +1685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1679,9 +1728,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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zW9Wt8+0NTANjVfY31Qe!bP?Gl7wMpMsNv_Ie_ji*|Lw`~E?rV~BLk)_kK1dk=kcV( zDvx3-S!k*2Gh^n_$Q%HGTjd%kxTsK&D?c;c@" ] @@ -1156,7 +1156,7 @@ ], "source": [ "#This code snippet calculates the correlation matrix for numeric columns in the kings_data DataFrame and visualizes it using a heatmap.\n", - "numeric_columns = kings_data.select_dtypes(include=['int64', 'float64'])\n", + "numeric_columns = kings_data.select_dtypes(include=['int64', 'float64','object'])\n", "# Calculate the correlation matrix\n", "correlation_matrix = numeric_columns.corr()\n", " \n", @@ -1184,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1207,12 +1207,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1256,12 +1256,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1303,12 +1303,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1343,12 +1343,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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" ] @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1424,15 +1424,6 @@ " " ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "check_for_outliers_and_plot_boxplot(house_data_clean, numeric_columns, figsize=(12, 12))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1449,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1465,39 +1456,6 @@ " return df_cleaned" ] }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'sqft_lot'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2894\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2896\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'sqft_lot'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcleaned_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mremove_outliers_by_zscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhouse_data_clean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumeric_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mcleaned_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m\u001b[0m in \u001b[0;36mremove_outliers_by_zscore\u001b[1;34m(df, col, threshold)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Calculate Z-scores for the column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mz_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;31m# Filter rows where Z-score exceeds the threshold\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mdf_cleaned\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2900\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2901\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2903\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2904\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2895\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2896\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2899\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'sqft_lot'" - ] - } - ], - "source": [ - "cleaned_data = remove_outliers_by_zscore(house_data_clean, numeric_columns, threshold=3)\n", - "cleaned_data\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1507,9 +1465,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\admin\\anaconda3\\envs\\learn-env\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plotting a histogram with kernel density estimate (KDE) of the \"price\" variable\n", "plt.figure(figsize=(10,4))\n", @@ -1527,12 +1506,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1567,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1578,7 +1557,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1595,7 +1574,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1605,7 +1584,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1629,7 +1608,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1685,7 +1664,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1728,12 +1707,12 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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ERM4By55BN7PQzB4wsy8s91hERERERERElsuyB+jAW4CHl3sQIiIiIiIiIstpWQN0M9sCvAq4ZTnHISIiIiIiIrLcljuD/gHgd4B4uieY2U4z22dm+/r7+5dsYCIiIiIiIiJLadkCdDN7NdDnnLtvpuc55252zl3unLt8w4YNSzQ6ERERERERkaW1nBn0HwauMbMngM8ALzWzTy/jeERERERERESWzbIF6M6533XObXHOXQi8DrjbOfeLyzUeERERERERkeW03HPQRURERERERARILfcAAJxze4A9yzwMERERERERkWWjDLqIiIiIiIjICqAAXURERERERGQFUIAuIiIiIiIisgIoQBcRERERERFZARSgi4iIiIiIiKwACtBFREREREREVgAF6CIiIiIiIiIrgAJ0ERERERERkRVAAbqIiIiIiIjICqAAXURERERERGQFUIAuIiIiIiIisgIoQBcRERERERFZARSgi4iIiIiIiKwACtBFREREREREVgAF6CIiIiIiIiIrgAJ0ERERERERkRVAAbqIiIiIiIjICqAAXURERERERGQFUIAuIiIiIiIisgIoQBcRERERERFZARSgi4iIiIiIiKwACtBFREREREREVgAF6CIiIiIiIiIrgAJ0ERERERERkRVAAbqIiIiIiIjICqAAXURERERERGQFUIAuIiIiIiIisgIoQBcRERERERFZARSgi4iIiIiIiKwACtBFREREREREVoDUcg9ARGQ2ew70sXtvL4cG82ztbmXXVdvYsb1nuYclIiIiIrKglEEXkRVtz4E+rr99P32jRbpa0vSNFrn+9v3sOdC33EMTEREREVlQCtBFZEXbvbeXdGi0ZlKY+d/p0Ni9t3e5hyYiIiIisqAUoIvIinZoME9LOmza15IOOTyYX6YRiYiIiIgsDgXoIrKibe1upVCJmvYVKhFbuluXaUQiIiIiIotDAbqIrGi7rtpGJXLky1Wc878rkWPXVduWe2h1ew70ce3N93LljXdz7c33an68iIiIiJwWBegisqLt2N7DDddcRk9HjuFChZ6OHDdcc9mK6eKuJnYiIiIislCWbZk1M8sBe4FsMo7bnHN/sFzjEZGVa8f2nhUTkE/W2MQOoDWTIl+usntv74ods4iIiIisTMuZQS8BL3XOPR/4fuDlZnbFMo5HRGTe1MRORERERBbKsgXozhtLNtPJj1uu8YiInA41sRMRERGRhbKsc9DNLDSzbwF9wL87574+xXN2mtk+M9vX39+/5GMUEZnJamhiJyIiIiKrgzm3/ElrM+sCPgf8hnPuoemed/nll7t9+/Yt2bhEZGXYc6CP3Xt7OTSYZ2t3K7uu2rai5nfXxnd4MM+WFTg+WV3M7D7n3OXLPQ4RERFZesvWJK6Rc27IzPYALwemDdBF5NxT65KeDq2pS/oNsGKC4JXcxE5EREREVo9lK3E3sw1J5hwzawGuBg4s13hEZGVq7JJu5n+nQ2P33t7lHpqIiIiIyIJazgz6ecAnzCzE3yj4J+fcF5ZxPCKyAh0azNPVkm7apy7pIiIiInI2WrYA3Tn3beAFy3V9EVkdtna30jdarK8zDuqSLiIiIiJnp2Xt4i4iMht1SRcRERGRc4UCdBFZ0XZs7+GGay6jpyPHcKFCT0eOG665TE3ZREREROSssyK6uIuIzERd0kVERETkXKAMuoiIiIiIiMgKoAy6yFlmz4E+du/t5dBgnq3drey6apuyzyIiIiIiq4Ay6CJnkT0H+rj+9v30jRbpaknTN1rk+tv3s+dA33IPTUREREREZqEMushZZPfeXtKh1Zcka82kyJer7N7by47tPcqui4iIiIisYMqgi5xFDg3maUmHTfta0iGHB/PKrouIiIiIrHAK0EXOIlu7WylUoqZ9hUrElu7Wpuy6mf+dDo3de3uXabQiIiIiItJIAbrIWWTXVduoRI58uYpz/nclcuy6atuM2XUREREREVl+CtBFziI7tvdwwzWX0dORY7hQoacjxw3XXMaO7T0zZtdFRERERGT5qUmcyFlmx/aeKRu/7bpqG9ffvp98uUpLOqRQierZdRERERERWX7KoIucI2bKrouIiIiIyPJTBl3kHDJddl1ERERERJafMugiIiIiIiIiK4ACdBEREREREZEVQAG6iIiIiIiIyAqgAF1ERERERERkBVCALiIiIiIiIrICKEAXERERERERWQEUoIuIiIiIiIisAArQRURERERERFaA1HIPQGS57DnQx+69vRwazLO1u5VdV21jx/ae5R6WiIiIiIico5RBl3PSngN9XH/7fvpGi3S1pOkbLXL97fvZc6BvuYcmIiIiIiLnKGXQ5Zy0e28v6dBozfh/Aq2ZFPlyld17e9mxveeMs+vKzouIiIiIyHwpQJdz0qHBPF0t6aZ9LemQw4P5enY9HVpTdv0GmFOQPdfjFcSLiIiIiEgjBeiyqixUULu1u5W+0SLVyDEwVqIcxYSBceHaVnbv7aUSRZwYq1KOYjJhQGdLqp5dn81s2fna6ziTmwAiIiIiInL20Rx0WTUWct74rqu2MVyocGSoQCWKMaAaOU6Ml3noyBADo2WqsSMMjGrsGBgt82jf6JzOfWgwT0s6bNpXy87XNAbxZv53OjR27+2d92sREREREZGzgwJ0WTUWMqjdsb2HDe1ZUoHhgHQYsKW7hc6WNPlKDAaBGYYRmIFBuRrP6dxbu1spVCJGixV6+8c4cGyEx/rGaM9OFKzMJYgXEREREZFziwJ0WTUWOqgdLVW5uKed7Zs62bahnY5cmpZ0iHMOgDh2OOeIY7+dCW1O59111TZGChUODzZk52NH/1ipnu2vBfGNCpWILd2tp/VaRERERERk9VOALqtGeybksSQj3ds/xkihckZB7XRBcns2xbq2DKnQiJwjFRrr2jJcsrFzzuceK1Wpxo5K5AjMOL+rhTUt6Xq2f9dV26hEjny5inP+dyVy7Lpq22m9lpVoz4E+rr35Xq688W6uvfleLWEnIiIiIjILBeiyKuw50MeJ8TLVyGFAJYo5MlRguFA57aB2uiD5DVdeRCYVsmlNjmdt7GDTmhyZVDin6+w50MfbbnuQYiUphzc/VrPmbP+O7T3ccM1l9HTkGC5U6OnIccM1l501DeK0zryIiIiIyPypi7usCrv39tLZkqYtm6J/1HddTwXGhvbsGa1P/toXns89vSc5PJhnS0NX+Odt6WL33t5T9s/mfXc8zFC+Ut92DiLg6aECW9a2NmX7d2zvOWsC8snm0sleRERERESaKUCXVaG2brmZ0ZHz65c75xguVGY5csJUS5vddv+RKTPXpxs8HzyRJzAIwoBK5LPoDihF7qwrYZ/JTOvMi4iIiIjI1FTiLqvCQjRVW8qlzcLASIcBlvSVMzirSthnoyZ4IiIiIiLzpwBdltVcG4ktRFO1pVjabNv6NmIHsXMEgV++LRUY2zd1rPrgfD5N386FJngiIiIiIgtNAbosm/k0EluIpmpLkdV9x8u3092a9kurJUusdbemecfLty/YNZbDfJu+ne1N8EREREREFoPV1nxe8gubbQU+CWwCYuBm59wHZzrm8ssvd/v27VuK4ckSuPbme+kbLRLFrt74LTTjovVt3PGbV8147ORmb3Np4tY4B70lHVKoRFQit+CBY21s820wt5LVPqta0zeAfLlKT0eOW3desYwjW/1O57ssZzczu885d/lyj0NERESW3nI2iasCv+2cu9/MOoD7zOzfnXPfXcYxyRI6NJgnNHh6uISZn7cdx45H+sbYc6Bv2iBlqmZv19++nxtgxsBmx/YeboAzDp5nC6jOxu7savq2OE73uywiIiIiZ6dlC9Cdc08DTyd/j5rZw8D5gAL0c8TW7lYeeGoQMwiSbmoGpENmXI5rLkt4TRdEn2nwfK4GVFu7W0/JoKvp25nTcnQiIiIi0mhFzEE3swuBFwBfn+KxnWa2z8z29ff3L/nYZPHsumoblTjGOYdzjjh2xDg2dmTrmdmpGpPN1uxtvvOl52O2TvDzaaS2mqjp2+JYisaFIiIiIrJ6LHuAbmbtwD8Dv+mcG5n8uHPuZufc5c65yzds2LD0A5RFs2N7D5dsaCcIjMg5UqGxeU0LqTBgS3frtIF2eyacsdnbYi6nNlNAtZg3Bpabmr4tDi1HJyIiIiKNlnMOOmaWxgfnf++c+5flHIssvsll5y/ZthYzoxI50qGxvj1DKrR6Zna68l9/TEy+XG1q9rbrqm3sOdDH/U8NEsUx2VTIho4sHbn0KVnJ023MNVWp98BYiXw5Yten78MMNnbksIyddeXKZ+Pc+uW266ptXH/7/im/yyIiIiJy7lm2DLqZGfBR4GHn3F8t1zhkaUzOLh8cGOODdz/GUKHMlq4cODg8VCAdWD0zO122eqxUnTKbC3D97fvrc9qrsePoUJHRYqUpK3kmme7Jpd79o0X6x8q0ZUOiOCaOHUeHC4wUKvXxng3lymdr6f5yU2WCiIiIiDSadwbdzAKgfapy9Hn6YeD1wHfM7FvJvt9zzn3xDM8rK9DkbPhosUpgMFKosm1DO50tGfLlKt1t2XpwMlNjsqmyudfefC/p0NjYkePIUIEoinHAkyfyrG/P8O5XPWfKscwn0z25E3y+HNHTkWF9e46RQpVq7DDns+qdLemzolz5XG2Mt1RUmSAiIiIiNXPKoJvZP5hZp5m14busf8/M3n4mF3bOfc05Z8655znnvj/5UXB+lpqcDS9HMYH53zWTs83zbUxWu4YZOOd8S/iEm2EsU117Jju293Drziv46jteSmdLmnVtWQA2dGRxDhyOclKCfzaUKy/mnH4REREREZkw1xL35yQZ858EvghcgM9+i8zJ5GZYmTAgdv53zeRs81zLf2vl1/2jJR7rH+PpoQKpMCCXCsmGAa2ZkDUt6XpAuZCNuRrP1ZFLs7krR2BGYHbWlCur07iIiIiIyNKYa4l7Omno9pPAh5xzFTNzsxwjUje5GVZHLkX/WJnOlhTOuWmbY81W/ttYfr2pM8uRoSLV2JEKHFhAjGN9e64poFzIxlyTzxUGRk/n2RGY12gNdBERERGRpTHXDPpu4AmgDdhrZs8AznQOupxDJmfDL1rfzlteejEXrms/7eZYew70cd1nHuDIUJ5jw0XMjC3dLRgQxdSXbZs8F3whG3OdC02+tAa6iIiIiMjSMOdOLxFuZinnXHWBxzOjyy+/3O3bt28pLykrVC1zfnSogK+SN6pRTDoMqMSOKHZ0taSoRI5SNSYMjF/f8Uyuu/rS5R76qlRblu7wYJ4t81iWTkTmz8zuc85dvtzjEBERkaU3Y4m7mf3WLMdreTRZFrXGZdlUQDVyxM4ROYirMenQIIChQhUDWtIBa1rT3Hb/EZ63pWveWfrTWS/9bKNO4yIiIiIii2+2OegdSzIKWRVWUrB6aDBPV0ua9e1Zjg4XqMa+EsT/fyMdGIE5MmHAtg3tAHNeSq1Gy4uJiIiIiMhSmjFAd869d6kGIivbVMHq2257kA3tWUZL1SUP2GuNyzpb0gA8ddI3gAsMNnf5ddBxjnw54sCxETJhwPr2zCmdx2e66XAm66WLiIiIiIjM11zXQc+Z2a+b2V+b2d/VfhZ7cLJyTF4Luxo5hvIVDg6MN2WX9xzom/VctWXRrrzxbq69+d45HTNZY+OyjlyKbDogFRgXrG2lI5cmNKMS+4y6f17E4cECbZmJ5cJqNx36RotTvgYtLyYiIiIiIktprsusfQo4APw4cAPwC8DDizUoWXlqJeU1A2MlAoPIOcxsztnlmcrGgVOy2VPtq82HviF57PBgngvXtnJivEwYGM45KnFcv6YlvyMH4+WJ9c937+2lEkWcGKtSjmIyYUBnS6r+GrS8mIiIiIiILKW5BugXO+d+xsxe45z7hJn9A/ClxRyYrCyNweposUK+HOHwJeWjxQodufScssvTlY2/746HyVfipsD97bc9iAPWtKSnnAM+uXFZY6fx2PnA3Ayc878DoG+0VH/+o32jDOcrBIERBkY1dgyMlqlEo4DP0r/ttgc5MlQgih1hYLRnU7z7Vc9Z2DdXRERERESEuQfoleT3kJk9FzgGXLgoI5JlMVsDuF1XbeP62/czMFZkYLRc3x+YcXSoyOYuCAObNbs8ORMPvmz80b4xtnS3NAXuRwYLYHDempb6vpmy9I0B+7PedQfOOcJgYhZH1JBVByhXYzD/GsAH8bE5vz9hAA6cc+Csno1fTiupWZ+IiIiIiCycOc1BB242s27g3cDtwHeBP1u0UcmSmm0uNvjg94ZrLmO85DPnmdBnnQHKUcwTJ/L0Doyz/+jwjPPKt3a3UqhETftq25Pne1fjmCjpzl4z1zngF61rJXYQxw7nHHHsiJ3fX5MO/fgbn0Py2sBn+ztb0lyysYNnn7eGSzZ20NmSZvfe3lmvv1jm8lmJiIgsFjNzZnbxNI/9gpl9eanHdC4ws4+Y2btP47gLzGzMzMLZny0iK8GcAnTn3C3OuUHn3Fecc9uccz3OuY8s9uBkaUxuANeaSZEO7ZRAdMf2Hjpb0mzf1MGlmzpZ15puCqADoFCOeOLEWFPQ2NgUbnC8xEihQr5cxTnf5K0SObatbzslcE8FQf0mAPhS+sf6xugbLZ1yE2By47lXft95tKQDKnFMsRpTiWNa0gHvfMWz68dcurGTdW0ZUqEROUcqNNa1ZbhkYyewMpvEzfWzEhGRc5uZPWFmBTMbNbMhM/tvM3uTmc01OTNvzrm/d869bL7Hmdl5SeC/sWHf70+z7845nO8JM7t6vuNYLGb2cTP7o3k8/1fM7GuN+5xzb3LO/eEcjm167c65p5xz7c65aKbj5nDe88zsdjM7mnwuF87y/B8ys28k379vm9mVkx7/DTM7aGYjZrav8XEz+9nk+5o3sz2TjltvZv9lZieS7/U9ZvbDk57zVjM7ZmbDSWPrbMNj/ye5XsnMPj7FuH/MzA4k1/5PM3vGpMdfaGZ7k5sex83sLcn+HjO7NXl/hpMx/uCkYzeY2T8k4x40s79veGytmf2jmQ0kP39vZp0Nj4dm9kfJ+UfN7AEz60oe+0gyntpPycxGJ137dWb2sJmNm9njZvYjU7z2P0g+26sb9nWZ2SfMrC/5ec/k42ThzanE3cyun2q/c+6GqfbL6jJd2flUgWjjXPTxckQmFdRLwjOpkNg5RgpVNq1J1YPG62/fT7kaMVqscmw4xgyyyXFbGprBXX/7fvLlKi3pkEIloiOXwuHXL69GMUeGigCc35U7pbnc5MZzn7r3SQzIhAHVOCYVBOQmBdu1sv1Na1L1a1YiVx9PRzbFY31jRM6vp76hIztlGf9SlpzP57MSEZFz3k845+4yszXAjwIfBH4Q+N/zPZGZpZxz1YUeIIBz7mkzewy4CvhssvsqfIPiyfu+shhjkFnFwJ3AnwL/PdMTzWwtvuL2/wP+BbgW+LyZbXPODSaB6/vwn+f9wJuAz5nZpuRGwkngA8B24KWTTj8G/CrwKH6xntck5+5xzlXN7MeBdybHHQU+B7w32Uey74/wja9bJo17fTLeNwCfB/4Q+EfgiobH7wTeCtwGZIAtyeHtwDeB3wL6gF8D/p+ZXeicG0ue8y/Jc54B5IHnNlz+j4BuYBt+huU/A+9JzkfyGn4IeAnwFHAZUAR/8yZ5D2uv4+P4z6u2/T+BG4GfA74BnDfpPcXMngm8Fnh60kPvB1rxU5t7gP8wsyedcx+bfA5ZOHO9izre8BMBr0Bz0M8a05Wd1wLRxuz0UL7McJIBL1UjnHM4IBVMzOMuR3E9aNy9t5dyNeLEeJlq5OrP6x8r84eveS637rxioiv7NZfR05FjuFChpyPHn7/2+fzFa59PT0eOYyMlUqFxflcLYBwbLnJ0qMB1n3mAd33u2/SNFnnqZJ6DA+NUI8dosUo5djOWp091zRuuuYwd23vYc6CP/rES1dhhQCWKOTxYYKRQqQfwtfdmKUvOa5/VaLFCb/8YB46N8FjfGO3ZubaTEBGRc41zbtg5dzv+P9B/2Xw/Icxsj5m9ofa8yZnbJJv262b2KD4gqnmlmfUmmb4/tyQrP83xbzKzR5OM4YfNbLp2LnvxARvmy7FfgL+h0LjvJcBeM3ummd2dZFFr2cau5HmfAi7AB21jZvY75pcL/nRD1vWblmTmzWyz+czwSTN7zMze2DD+95jZP5nZJ5Os5X4zu3yqwZv3/iTLOGw+a/xcM9uJX/3od5LxfD55/juTTOaomX3XzP5Xsv/ZwEeAlyTPH0r217Pw5rPIX0hey0kz+6qZBdO89guTzyGVHLvWzD6WZGIHzexfp/k8mjjnjjvn/hofYM7mh4DjzrnPOuci59yngX7gp5LHLwT2O+fuc8454JPAenwAiHPuLufcP+GD6cnjKDrnvueci/GBbIQPbNcmT/ll4KPOuf3OuUF8kP0rDcf/i3PuX4ETU4z7p5JxfdY5V8QHyM83s+3J478FfCmpFCk550adcw8n5+11zv2Vc+7p5DXfjA/gnwVgZi8DtgJvT/49VpxzDzRc+yLgX51zI865YfyNhcuSY7uB3wTe6Jx70nkPJWNsYmZtwE8Dn2jY/V7gBufcvc652Dl3xDl3ZNKhHwLeAZQn7f8J4M+cc3nn3BPAR/E3SGQRzbXE/S8bfv4Y2AGcv6gjkyXTuKZ4Y9n5rqu2nRKAlqMYA9KBYfjO5wDV2BHFDud81roW4B8azDNarBJgBIFhVuuYHk9ZQn/rziv46jte2hS437rzCjZ0ZLl4QzsAR4cLVCNHGMB4qcrh4RKVakyYrM9+dLhAuTq3+etTXRPgfXc8zFipSuycf23Od6wfK1V51789VC+xX+qS811XbWOkUOHwYIFK8llUY0f/WEnz0EVEZEbOuW8Ah4FTyltn8JP4rHvjEib/C7gceCE+gznTf7C/Gngx8HzgZ/GZy6nUA3R8cH4A+I9J+9L4DKDhM7mbgWfjA5/3ADjnXo/PMP5EUtr9Z/igbU3yvHX4bGMhOe+t+PdkMz6D+Cdm9mMN47oG+AzQhc8Kf2ia8b8sGeulyXN/DjiRBGp/jw9y2p1zP5E8/3H857AGH0B92szOSwK+NwH3JM/vmuJav52MeQOwEfg9/9KnfO2TfQqfEb0MHxC/v/ZAEvBfOcUx82VwSl9dYyJjfAcQmtkPJjdefhX4Fr4J9dwuYPZtfAb5duAW51ztP4IuAx5seOqDwEYzWzeH0zYd65wbx39OlyW7rgBOmi+/7zOzz5vZBdOM7/vxAfpjDcd+D/hEcqPom2b2ow2HfBh4tZl1JwH5T+PfJ4DvA6rAa82X7j9iZr8+zWv4afzNkL3JOEL8v9UNyQ2ow2b2ITOrVw+Y2c8AZefcF6c5p036+7nTPE8WyOnOQ2rFl2DIWWCmTPJUAWhnSxozo6s1TWBGaL7GqBzFVKOYzpZUPcDf2t1KqerL2mucg2wYzKssu5Y5Hhgr1YN9MGoheAyY+f0BBkbT/HWY+xrmew708Wj/GHHsS9vTQYBzjsg5ylHclCl/5PjIks5T37G9pz5v3gHpMOD8rhbWLHPzOhERWTWOMpFtnIs/dc6ddM4VGvbdmOx7Cl+KfO0Mx7/POTeUPPc/ge+f5nlfAZ6bBCc/AnzVOfcosL5h373OubJz7jHn3L8nWcx+4K/wJfzTqeAD84uT7OZ9zrkRM9sKXAm8I8nMfgu4BXh9w7Ffc859MSm9/hT+RsN01+jAl2Wbc+5h59zkcuG6JEt7NMlo/iO+QuEHZngNk691HvCMJBP71SQTPSMzOw9fBfumpLdUxTlXnzLgnOtyzn1t+jPM2X8Dm83sWjNLm9kvA8/Exw8Ao/gS7q8BJeAPgJ1zeQ0NY30e0An8fHKemnZguGG79nfHHE47+dja8bVjt+Bv9rwFX6lwEH+Dp4n5ueOfAt6bZMNrx74M/29gE/CXwL+ZL5sHX+qfwWf2T+ArA/664dg1+Js/F+FvJL3HfOn6ZL8MfLLhvdyIv7H1Wvy/oe/H3+x6VzLWduBP8Bn6qdwJvNPMOsw3h/xVJj5HWSRzCtDN7DtJqc63zWw//g7QBxd3aLKUpsskT9corXdgnM6WNFu6W8ilQwIj+TEuXNdeD/B3XbWNMPBN2ByO2Pks+5rW9JyC5Zpalr9UjcEmzhOYkQ580B8n13BJ2N6eTU1ZFTCb3Xt7SQcBZlYP+p2DKIZcKmzKlFciN+P0gMUwVo64eEM72zd1sm1DO50tc1uDXkREBF8BeXIezz80y74n8dnn6TRmRfP4IIikXLzW1OpHkvLZw/iA+Srgq8kx9zTsq2UFe8zsM2Z2xMxGgE/jS6Sn8yngS8BnktLuPzOzdDLuk865xoZaT9JcJTp5/LlauXgj59zd+Oz6h4HjZnazNTT5mszMfsnMvpVkrYfwWcmZXkOjP8dnZr9sfqrBO2c7ILEV/3oH5/j80+KcO4GvrPgt4DjwcuAu/OcLfo73r+Iz0xngF4EvmNlM36OprlN0zt2KDyBrN07G8IF7Te3vpqZp05h8bO342rEF4HPOuW8m5eXvBX7IfI8HAJLM9OfxN5P+tOE8BeAJ59xHkxsjn8H/O6o1uPss8Aj+ZkAnPnP/6YZjwZepF5xz38ZXdbyycaDJDacfxU8ZYNKx/zcpvx/A39CqHfte4FPOuYPTvCfXJed4FPg3JipOZBHNdeLqqxv+ruLnlSxKoxBZfo1Nz0YKFaI4Zn17jpFChYGxEqVqTORcki3P0JHzTctGCmWOjZQ4lMw9//bhIe7pPUk6hPGSX8YsmwpY05YmHYanzOWerdFaWyb0161CLmWc15Wjf7REOYrJpXzpfDnype7P2tjOO16+nd17ezk8mK83o5tL87ZDg3k2dmZ5erhEjMOSCgGADR31RqC0pEMyqYDhQoUjQwWi2BEGRns2xbtf9ZypT74AGhv11Sz2TQEREVn9zOzF+OCzlnEcpzkbtmmKw6bKam4F9id/X8AUc4Vn45y7bIrdX8UH4i/BZwIb913JRHn5nybjep5z7oSZ/STNpedNY3bOVfCByHvNdx//Ij7Z9GVgrZl1NATpFwCT5+fO9TXdBNxkZj3APwFvxy9R3DQe853B/xb4MXwpe2Rm32KilHjGTHIy1t8GftvMLgP+08y+6Zz7j1mOPYR/vV3OuaH5vr75SDLzLwbfYBAfcP5l8vDzgc875x5Jtu80s6fxc9dvO43LpfGVvQ/iv5fPx7//tWsdT24azGY/E9+72nzuZzLxXf82ze9v7W9Lnp8F/hX//dk16dzfxs/nns7zgTcnZfWY2UeY+Hf67UnXm84vAf/tnKuXVDrflO/wDMf+GLDFzN6cbG8A/snMbnTO3eicO4nvoUAyrj/BTzORRTRjgG6+CyOcetep08xIPjQ5i9TmnNc6olejmL7RMsVKxFjJZ4oNny1/8mSBwAq0pENaMyGD+QqpwB/3xIkxvn7wBKEBZqQC/7s1m+LCde1NwfLkazZ2aK81bKs9fkF3C0eGilSTtcs7W1L0jZZZ15ZmfXu23on9HS/fXp/DPl+1AHhzww2AwHwjvNrNCPBB8Yb2LANjJXDgnANnp0y6Wmi17vONHe/nWh0wk6XsRi8iIksnyeReha9+/LRz7jvJQ98CfsrMbsFnk38Nn/GczdvN7Ov4bPhb8Bm5hbAX39TriHNuJNn3NXxAvQafTQefZRwGhszsfHwg3Og4DVMxzex/AAPAd4ERfIl45Jw7ZGb/Dfypmb0NX0L8a/iM7rwkNz8CfKnyOH5+dK3Ermk8QBs+YOpPjv3fNM/rPY4PmjLOuclNuzCzV+Pn6D+evJ5ohmvVOd8t/w7gr5M5zGPAS5xze+f4GnNArawya2a5qRqVJc99AfAQvlP6DcBh59yXkoe/Cfy+mf1ffJn41fj3/qHk2BAfdKeAILlu5JyrmNkVyf5vJGO5Dl/G/fXk3J8EPm5+CbOn8aXcH28YVyo5PsTPg88B1STx+Dngz83sp4H/B1wPfNs5dyA5/GPAP5vZTfig/d34KRBDSUXGbfhs8y8538Su0eeAv0jK/T+N7+NwPvBfDe/JG8zsd5LtnSTz4Z1zj5vZV5P37Dr85/tznDq15Jfw3don+xjwG+aXKKzgy9m/kDz2Y8l7XVPrRH9H8n49ExhKfl6WjGum6SSyAGYrcb8P2Jf87seXXjya/H3f4g5NlsPkOecbOnJsaM8wUoyIk+XG1rZlqIWhzvkO5wNjZZxzbFqTw8wYHK8QO6jGEJoRWIBzkAv9cfNptNb4eGdLhvO7WkiFxrGREheua+ctL72Yi9a3nzJ/vqbWhf7yP/p3nveeL/GiP/zyKeuoN6qV04eBcdH6Ni5Y28ratgxrWtKnlMz7mwTpGbvFL7SZegacrqXuRi8iIkvi8+bXQz4E/D4+kG5cYu39+K7Nx/Fdn//+lDNM7d/w/x34LXwg89EFGu9X8I3LGucUfwsf5N3nnKvN5XovvkHdcHL9f5l0nj8F3pWUj78NXxlwGz6YfTi5Tq18+Fp8V/Haklx/4Jz799MYeyc+Kz6IL5M/AfxF8thHgeck4/lX59x38dnke/Dv/fcxEagB3I0PAI+Z2cAU17oEXzI+lpzjr51ze6Z57ZO9Hh+kHcAvB/abtQdq0w1meI2F5Jokx9f7Ephfi/sjDc/9HfxNkUP4+fL/q+GxT+JLtPfgP5ObgF0NgfDrk3P/DX7edAH/3gJk8dMITuAz1a8EXuWcOwrgnLsT+DP8XO8nk58/aLj2u5LzvRN/I6aQ7CPpZ/DTwB/jP8cfBF5XOzCZxvB7+O9cH3Axfg48+Oz/q/FB7FDj9I3k2JP4hoNvw39v3wm8Jik5B1/yfyG+fPwIPgj/lYZxX4tfnu1Ecv13JxUTAJjZS/Bz1T/Lqf4QH3g/gv/+P5C8RpxzJ5xzx2o/+Bs9g25iabgXAd/BJ2v/FPgF59z+yReQhWVz6ceQ/IO7vdbdz8xeAVztnPvtRR5fk8svv9zt27dvKS95zrnyxrvpSprA1TjnePjYKM/e1IGZ0ds/RjX2JevV2JFK5mJnQ+PSTX7qzkNHhnH4bHtt/fFyNSJy8MwNbU2Z3/FShfPWtJxyzeFCha++46XTjunYSJFLejrqGd+XbFvLPb0nmzLA4NdIr0QRA6PlevHYurYMmVQ4bWBbu3HQWB4PnLLvXf/20JRjq419tbj25ntPKZvPl6v0dOS4decVyzgykXOPmd3nnJtyKScRERE5u811DvqLnXNvqm045+4wsz9cpDHJMmrPhDzWP0aUdDBf354lFRptGR9Qt2ZSfp53YL6k26Aa+Zs8pcgxWqw0lYE3dm+vrXpWCwJbM6l6Jrp27prGOdWT51yPFCo8PVygHDnGSoNs7MhycGCMbzxxkp6ODOvasvUMcFsmJB0aJ8aqvsO7GXHs10nftCbF7r29Uwbo05XHT963de/ZMR/80GCerpZ00z41nhMRERERWVpzXWZtwMzeZWYXmtkzzOz38SUWssLUyrmvvPHuGcu4pzv2xHiZauQwfOn6kaECw4UKb7jyovpa6enAKFdjqs6XuDfWYDxxIs/x4QLppJQ9MCa6twPZsHmGdq3R2nTrsEPzOu0jhTJHhnxwbkCpEvPkyQLHR0o45xgpVJvK5HsHxmlJh3799uTSZn5JuIUIQGdaQ3629/oVH9jLs951B8961x28/P1fWdZy8toydo1W440GEREREZHVbK4B+rX4rn6fw3cn7GHmNS9lCTXOsd716fs4ODB2WvOId+/trS+dlg4DHL4x2ob2LNddfWl93nNrNsVMMyP6xsps7MzRmQ1JBQHVKMaAdGisbc80PbdQibikp+OUOdWvfeH57N7by5U33s3uvb289oXn09OR4+hwkShJxTsmbg44fIa+McisLQ9XqERkwqA+5mrsiGLHgWOjDBcqZxQYn8588D0H+nj7bQ/yaN8Yzvl57I/1j/O22x5ctiD9dG80iIiIiIjIwpnTHPSVQnPQT9XY4fzpoQKVJHjdvKaFzqSp2VznEU8117u2dNqGjmzTvO5f/cQ36yXrkxlwxbZ17LpqW9Oc7ZdsW8tt9x8hHVrTHPSpmrrVXlM1ijk+WqISOTavyXFkqEAqMMqRa7pebSswuGyzX46ylu3PV+L6HPTYOSLnnxeasb4jQzqcfi76Yrj25nt54KlBHH4dd4A4dlgAL9javWxzvmead6/O7iJLR3PQRUREzl2zLbP2Aefcb5rZ55li/Tzn3DWLNjKZk8YO55VkHW4Xw8BYic6W9LzKuCfP9R4tVjgyVCQwGBwvc2y4yP1PDbKhPUMmFVCsTF5BwnPAPb0nuPfgCVJmXNwzsaza87Z0zbo2ee01VSPH0eEiAUZocHjQNwudfGOgcTN28HjfKGta/VrrtfXId+/tpRKNMpivYM7Rkg7Z0JGlI+dvYkw3F30mp7ss2aHBPNU4JhVOFLCYQRS7eZXcL/SyaJPn3U+1/N3bb3uQdW0ZxsqRAnYRERERkQU2W5O4TyW//2LGZ8myaWzulQkDqrGrz7GGqecR33TXI9zytYOMlyPaMiFvuPIirrv60lPW1z42XMQ5R9WB4UgFRuQch4eKbGhPTxug1zgHFed45Pgob7/tQV5/xTNO6bLeuBZ6LdjsHy2xqTPLwFiZACMIzJezR44wCWQbs+Y1AYBBOXKcHK/w6zsuqJ+/9nuqKoHTmYs+29rtM9na3crAaAnnJproOQdhYHOe830m15+rxps/4N/3wXyF0VKVize0L8o1RURERETOZTMG6M65+5LfX6ntM7NuYKtz7tuLPDaZg8as94aOLEeHisQ4AjMePT5KJY5JB8aeA33s2N7DTXc9wgfvfozAIBX4AP6Ddz8G4OeZM7GUWG0OOkmQDBAaVCLHaCkiGxqlaPopEpZE0bGDoXyZD+95nC3dLadkYwfGy4wWq3S3plnfnmVgtMThwQK1UwexLwUPjKRCwJEOmq9da0rXWNp/T+9Jrpvh/ao5nWZok4PXWkf6uWTid121jbff9iCD+QrO/GuIHXRl03Oe830m15+ryZ3d+0dLBLUbJEkjvoW+poiIiIjIuWxOTeLMbI+ZdZrZWuBB4GNm9leLOzSZi8bmXu3ZFOva08TOUarGlKoxKTOGCpV6s7hbvnYwCc4DAguS33DL1w42ZbG3dLeybX0bkWteKs0534m9Ejm62zLY9EPzzGe6qzFU45godhwcGOeJE3n6x8r0DoyTL1WJneNEEqhnUwGNcX/sfGO3XCogdpBJBVyysYNNnVmyKT/+TBjUg3M4NStea6T3aN8ohwcL9I8Wz6gZ2qHBfL0JXc1cM/E7tvfw5699Ppf0tGNmmBkXb2jjL177/DkHumdy/bna2t3KifESvf1jHDg2Qr4cETu//N5CXfNMVh0QERERETnbzHUd9DXOuREzewPwMefcH5iZMugrwI7tPU1Z766WDP2jJdKhEZrhHJwYL7OuLcPuvb2MlyNSk27LBAZjpeopJdPDhYqfG+18ablLllVb156hqyVDV2uG4yM+q2rm10U/pXGcm2jiljLj6FARM6g1JyxH/phU6OfOHxspUqpOXTqfr8R0ZkM2d7UwXKhw0fp2fn7b2qRcv8rAWAkz6MilKVR8+f61N9/LI8dHGCtFrG1L05YJGSlUODZSYmCsxCU9Hbz7VdtPCYxnm9/dkU3xWN8YURKwbujIzqtEfbp11ufqTCoB5jp3/SXb1vKNJ04SWG25PH+jpTszcWPgTJZiW4oyfRERERGR1WSuAXrKzM4Dfhb4/UUcj8xiuuCqFtBce/O99dJ0w3z2O4bRYpXDg3kyoSVzxyMs6WROEmBPLpkGyIVG/3iFShSTDQPWtPnma+94uQ9qr7353nqgOFqs8OSJ/MTSZ8kfoVGfR27my9Vrj5nh10h3fqzFaYLzmjWtGbrbsoyV8wyOl/jUvU8SGMQxjJcj8ifydLWmCIPAr+UeO4qVmNg5+kfLfjyBkQn9mMbL0SnXmC1w3HOgj/6xEtXYEZhfL/7wYIHu1nS9Kd1im9wvoNYRfy7rr881KL6n9yQb2jOMFquUo5hcKqBcjRktVdno3JyvOZ2lKNMXEREREVlN5roO+g3Al4DHnXPfNLNtwKOLNyyZSi246hstTrvO+aHBPNmGNb/BB8GlakxbJiSTCurrh8fOB7DVyNGStqaS6ZFChWPDRZ4eLXPRulYu7elgXXuWC9e1Ny1JNnn97GCKmvfNXS38xv+4mChZ89s51xTEx87Pa47msOTf0eFi/fU/3j9G/1iZoUK1/rgDBvNVTo6VGS1VqUaOchQTmtWv4eez++10aOze29t0jcbAsTbXuvF5u/f2sqYlzdrWNNXYJVUAjmwqOKUL+mKVb5/O+utzeW2NDg3mWd+eZduGdrZv6uSSjR1sXduCc8zrmtNZijJ9EREREZHVZE4ZdOfcZ4HPNmz3Aj+9WIOSqc2WcdxzoI+RQoVSNSZ2EAaQCgNfop5ki0uV2GecaxlsYEt3Cy6O+e7TI1Oubf7EyTzt2dSUc6QbS+zvf2qQdBiwLpdivBz5wDgw2jIh1119KV/8ztM8cTJPZYrGclHsSIWzzmgnih3Hhou0ZkIam8hPUVlPHDuODhfqGXsHJD3ZcM7PW58qIJzcHA2aA8dDg3lCg6FClXQY+CqA2PH0SKnejG8pyrdPp0x+ttfWaKoy+lQY8MILFmat9vmW6S/0snIiIiIiIivNXJvEXWpm/2FmDyXbzzOzdy3u0GSymTKOtYCwLRv6DHEAUUwSkBuvfO5Gjg4XfZOvVJCUwNfOW+DwcGnK4BygWIkZHC/zvjseBk7NDAPcuvMKNnRk2diZZbBQoRo5QjPi2PFo/xh7DvTxzlc8m56OHNPF4Rs7smzf2D7t4wDpAKqRY2Cs3LS/sZGd1fcZAX7yfJyE8LXAPcaxvj1LoRLRnk01vZ6ObIqBsYnmaL39YwyMleqB49buVo6PlOrl+rX/Cw2u+8wDXHnj3Vz3mQcYK/oqhO8dH+XYcJFKFE2ZqV5KW7tbKVSay/qnC4onV0ecbkO96czn/HOpHhERERERWe3mWuL+t8DvAhWAZIm11y3WoGRqUwVXJ8ZLDBcq7Pr0ffSNFMmmQs7vbiGXCgkC6Mil2P2LL+Lhp0dxzpdjlyox1djVS93nInLwaN/YtIHSTXc9wkihwlMnC1Qjl8w39x3K00FQz/LfcM1lVJI7AYFBOvCl9enQ6B8r885XPJvutgyZSVF6GEAuFeAwKnF86rhd85+psJY1d8TAurYM6dAv1WYBbF6TIxUaI4UK/WOlptdzeDBP32iJcuSrDcpRTP9YmZdsWwskgWUc18v146R0vho58uWIrpY0Y6UqJ/OVehVBNXYMjJZ5tG90ju/44phPUHy6ZfRzNZ/zz6c0X0RERERktZprk7hW59w3zJqCpup0T5bFMbkx2InxEn2jZTpzIeWqD1qfPJGnp8PPG3bOMVyoAPBo/1g9szzXoHyyyMGNdx5oKrOvRo5jwwX+6q5HyYRWP3cliomdn+u9aU22XkK9Y3sPmTDAOUcYnHp/aMf2Hv7itc+vd6Xf0t3Ko32jbOrMcehknmJx6q/d5NcUx4717RmGCxUccNH6dt73Uz4IrZ27pyNHOjAqsWuaNlCJHanASIcB5SgmEwZ0tqTq66rv2N7DJRvaebx/rGktdjPIpgIa/51EsSMVJGXw5ijP0gTvdMyn9Hty1/8tc3j+YpaRz/X88ynNFxERERFZreYaoA+Y2TNJ4iAzey3w9Jle3Mz+Dng10Oece+6Znm+1ON25tDu29/Daw0PJsmI+k55LGeOluL6WmQOOj5boHyvhkuXLrvvMAwRAEAa4asyZhIgPHxvFgExorGlJ18vZgaTQ2zU0oXNs6W4hDIyejlz9HBvaMxweKkIUYfiu6gAXr2+tv87G9+Pam+/l4MAYI1ME543z6WtSSdf44UKFns5Ts7KNf195492nBH5R7DPj2za01/c555qCwVd+33l88O7HSIV+DKWqwzloTZZxq40pdlCN/DQDkvdtIZ3OXPfFDroXw5ksKyciIiIislrMtcT914HdwHYzOwL8JvCmBbj+x4GXL8B5Vo25zqWdqgP4ngN93Hb/ETZ0ZHn2pg7AkS/HxC4m1VzdQJwE61HkGC9VifGBoluA+NABpcjRN1auN18LkqA49Ku2kUnKycPAmkqo9xzoq2f1a+eqxo7WdMA7X/HsKa+366ptDOZ9Jtwafhq/vG2ZkGesbeUZa1vJpsP6TYLZSrKnmjZAsp57bf75SKFySjB4T+9JejoyZMKA2PnXH5rvfn90uEDj21yJ/WLw69oyXLKxc5Z3d37OldLvxZ4PLyIiIiKyEsy1i3svcLWZteHjogLwc8CTZ3Jx59xeM7vwTM6x2sxl7efpsqJtmbDp2FwqZLwcETtfWl2JmwPNdGAEgVFJlhkLA6N0hiXWk8vkq7FryoKnwoA48teIYjg8WOCidROB7fvueJhCJSYV+GXOaufpzKVmzPh25FKUx8r1mwGpwJfJV2KHGfUl2jpb0nS2pMmXq/R05KY8Z2MFQ3uS8QZfMj0wViJyEzcBKlHMkaECXZPWOD80mGddW5b17b4yYLRY4chggXLkl26rzTtPh74Zn3OOwXyFyvERrr353gXrQD659HukUGFgrMQTJ/ILep3lNt/SfBERERGR1WjGAN3MOvHZ8/OBfwPuSrbfBjwI/P1iD/Bs82jfKPlSlUrsyIQBGzqytGdTTeXT0wXxj/WNkQ6tfmx7NlUP0N2kNcQN36TNzwWHOHJU8cHsdMuNm8EVF3az/+lRRovVevCcVM9jQDr0eetKNNGoracjy2C+Qpw0TQvNz1fftCbLujbfKb1Wdn3wRD7JrAekQ3+eauw4PFziee/5Em+48iKuu/rSU8Z2SU8HcTySXGfi+gasb0szmK9yZKgAOFJhcErWfvfeXh45PkKhElOsRGRSARs7slSSmwSZMGC4UCFfjtjYmSWbCjk2XKSUzO3Pl5tvfkwuue7IpVnfEXFsuJSsiR6yLptirFSlWIn8+9GZYX17dkGXXGscRy17D5ANbVGWdpurxVgSbTWW5ouIiIiIzMdsJe6fAp4FfAd4I/Bl4GeAn3TOvWaRxwaAme00s31mtq+/v38pLrlo9hzoY7Tog/NahvXoUJET46Wm8umpllOrRjGV2DUdO5iv0J4NMfOZ7FqZteGzzJUobgrG/bzw6ceXCeDeg4OMl6u0pAPWtqbJpgKS5HhSjh4TN5zUgCCArpYU5WpMOXJEDtbkUqxvz81Ydl0LzmsKlYgP3v0YN931SP39qpX5Hx7MM5iv+C7sTGTwO7Mhm9a0sqW7hVRgHBspNXUDr1Uj1Oaw55MbGtXIcXS4yEihwlipSu/AOFu6W8mkAta1ZSF5rzKpgGzKKFfjpqkIU5Vcp8OQ7Zs6eMa6NrZtaKenM8e2De2kw4BcOmBDx8zvx+loHMfAWCn5TIyeztyylbtrSTQRERERkdMzW4n7Nufc9wGY2S3AAHCBc27J1opyzt0M3Axw+eWXn24D8hXhxjsPEMcxldgHiKkk2jw5XuHaF6/l2pvv5dBgnpFChSiO6+XTAMdHSqST2ykuJgnKY8ZLJEG64VxMOYLWTMBQvjptt/bGALcmAEpJktg5yFdiCpWY1kxAFEOIz4rHjqYAvTUTMDBWJood2bTPSh8ZKjJarDJa9KXjx4aLFKsxj/ePk00FRLHDzDUF50B9ebaP7O3leVu6msr8H+sbS0rbLZlP748dLUccODZCJgzY2Jkldn5N9ppaNcKJsWrTzYpq7DP9A2Nl0qERO0ffqB93OiwxUqjW1zmPY8imrB7s1jK5U5VcA7z9tgc5MligGsekAt8Jfmt3S9NrXagO5I3jeOJEnmzog/OOXHpBrzMfc5nGISIiIiIip5otQK9383LORWZ2cCmD89VqqvJegEf6xgjNSAeOqvPNw7KpACPmw3se90FuKiAdGn2jZYB6iXgljtnS1QIYA2MlitW4ng1/xro2/5zI8aIL1vAfB/rntZSaQVNn99p5HTBejgkNtnS3cnQoT6XhianAKEcOnC+5v6SnA/BBbzmKOTZcpBpPBOIGVJKS8ckl+fUnOF9OPnk5t8j5yoFUGLC+PcuTJ33Q6RyESUb8yFCRize0NZ3y0GCe0Kh3vW9UWyHNzMiGAa2ZFN2taU6OV5Lg2gfnMY717blTgt2pSq73HOhLljrw58VqXd6b5/4vZAfy2jiuvfneFdHpXEuiiYiIiIicntkC9Oeb2UjytwEtyXbS98qdUUtqM7sV2AGsN7PDwB845z56JudcbtM1eGtN+8DbxRCGASl8JjqOY6oxpEKfUa9GjnIU05ENGS9FVKq+8ViAcXy0xMYOXzb98NHhehD+3adHWNeWIQyMLz50nO7WNGOl6rTl7JN3zxbMRw4GxkoEQQDxRKAZOz+uKIYojuntH6OcLCkWxY7KpCC8NmccoBI1P+a/UBPbteXcsqmAjZ05MmFAJYopVuN6l/TGRnXJ1PimNcgB2jMhj/WPz/j6qlHMeWt8ELu+PUs1iilHAflyRDZlrG/P1RvPNQa7U92I2b23lzUtac5bM5ExHxgrcnK8Qls2RUs6rN9MWegO5Luu2sb1t+8nX64u6nVmoyXRREREREROz4wBunMunOnxM+Wcu3Yxz78cpivvPXgiz6bOLEeHi5CUqPsu5P64SuSIzBGaEZhRKEdUY8dI0lE8DKBccRwZKjA4XqTaEMzGDvrHyqQC3zn9+EhpXhn0uZgqA+0cVJOo2uGz2KEZzk0E4pPVwvtUAJ25NCfzlfrxp5wfKFZjnjyZJ2g4Nm54fi1Qj51ja3cLY6XmtdInB+xTSYdBvSS8UIm4ZGNnPdhNh5Z0dy8yMFbmyRPjPOtdd7ChLU0pcnS2pJtuxIyXKvXgvNZRvZx0tU8na7MvVgfyldLpfKXcKBARERERWW3mtMyazF2tpLqWTc6EAevbM4BfgmzzmhYGxkoUKtEp3dQn5nefmnmuxn6euOEYKU2UjENjJnmxXtXU89Ynb5ei5gHUOsZbcgI36abC6KRgeia15dVq67s3yoSGw7+/PR25psdGS1XO78pxaLBwSkVBwMQ67M65pkCyMdh9tG+U4UKFKHKkQsM533U+ANqyE+uP19bmLlSipAldgQBLmvYZ+UrMH77muYsaMK+ETucr5UaBiIiIiMhqowB9Aew50Mf77niYgyfylJM51qnAB4zV2M+NPm9NjkqyRvb69gxHhor17PNMGoNbB2RSIZVyVF/KbKmczqVqL89NcYI4aSk/VeA/ncYseuO4yskb8dTJPK95/uamx2vl1lu7W3nqZL4p8x6GRns2JIqZMrPdOLf7gacGCVK+ugGAKMIB/aOlpoZsmZRf4q1vtFj/7HDGpjU5wsCmbZS2GMuSLaeVcKNARERERGS1sSmbda1Ql19+udu3b99yD6PJngN9vO22BxnKV3DONQXNAb58uhL5hmPOJrqP+87hnHLMbEKbfbm0lSYd0NRcDqbOyC/c9YyLe9p5x8u3A3D97fupRBH9I6X61IB0aKxry5BJhfUl2SarBc3feOIkcexIBWAWJEvN+ecEBpdtXgNAvlylpyPHrqu2sevT9xHFMdlUyIaOLB25NM45hgsVvvqOl55yncZy+lomf7pxicjZzczuc85dvtzjEBERkaWnDPoZ2r231897niLQjknWIgfKsSMT+qW6ypEjih3pMKBUnV+YupRZ8zOVStZrN7MkSJ8Y/GK+jErsePjYKL/2yW9yXkeWoWKF8XJMgG86FycBdndrhne8fPu0wXktaE4ZlKjdZGi+0xA7GCmUSYVBU3n8Cy/onrVRWu0GwP1PDWIGGztyWMa0LJmIiIiIyDlKAfoZOjSYJ4rdKaXXNY37q7EjFQSY+WA+zeIGqsstSgLy8jLdVYhiODJcIhMaqcAwjPPW+DXC8+UqXa2ZaQPgWrO/KJ6+wiEAUqFxbKTECy/obipLn61RWuMNgCj2ne+PDhcA6GxJn/ayZGdbqbyIiIiIyLlEAfoczBT0bO1u5fjwqQ3IpuIc9Y7e4DuUn80cLPlc+anGUI78+vKNc8ZnC4Bra3kfHBhnukbwGzqybOjIMlyocOvOK5oem61RWmO3/2wq9JUGyXJ2nS3p01qWbLol/m5IxiMiIiIiIiubAvRZTBX0vO22B9nQnvVdyBuWSpvN5Dh1MedhrxS14Hxyx/mlUnuPazdDSsCjx0fJpQNiB1feePeUmeZac7lyFBNN08xurFSlPZeaNpCeqVFa7QYA+ED/6FARcJQjV+8GP99lySYv8VdNmtXt+vR9p2T4RURERERk5QmWewArXWPQY2ZUI8dQvsLBgfEkYC+d1nlTgdHTkV3g0a5cpy4et3TXnaxYjRkqVAkDmjLNew701Z+z66ptVJJ13ada2s0MitWJsvU9B/q49uZ7ufLGu7n25nubzjWVrd2tFCp+bfmOXJrNXTkC813iezpy824Qt+dAH/c/NchTJ/P09o9xbLjA0eECceyI4njK1ygiIiIiIiuLurhP46a7HuGWrx1kpOjX6jagNRNSTZq+VWNHSzpkvBzNeq5U4OcyN77TYeDnSMvyacuEbNvQDkx0YG8sVd9zoI8b7zzAw8dGMfzSeQ6rZ9TbcynecOVF3PHQMR7pGyMdGhs7svWGcTMF2QvZub12rr6RIrFzGEY5igkDIwz8/PttG9qnfI0isvKoi7uIiMi5SyXuTMwxf7RvlHI1phLFFCoxjVOPHVAoR8RMlGuXpphD3lgKbfhluMLASIVGqRrX1wY/W4Lz1VKmb/gl78LAKFaiZG76xIcw1Zz0Won6TXc9wof3PE4U+7nsHbkUmVTIa194Prfdf4S+kaJf/i6Go8NFNq9pIR1Ov+Z57dwzzVGfj1qVx6Y1OV8qnyzFV40dgRkbkkqNmebdT+6z8JJta7mn96SazYmIiIiILKFzPkCvZR8rUcRwvkLcsFza5MCzFs7Vm59N0RnOTf7bfHAYxY5sGJx1jeGWOjjPhkYuHTJSrM752oFBOggIguTWSnJXIRNOzPCYqSnbdVdfyvO2dJ0STNc7vTtHmHSJJ/aN3i5a3zZrF/bGGwC3fO0gv/bJfbRlQt5w5UVcd/Wlc3x1E/PZzYzNXb4RXtm/RDZ3+a71M73GyX0WnjgxxjeeOMmG9gzr27NqNiciIiIiskTO+QD9xjsP0DdSrGfD5xr0zbUzeRQDLmZLdwvlKObp4dObs36uSwVgZpgZ+SQDPlexg8g5XHJDJQCcQUcuhXPulCXQpjJVw7d3/dtDdLWkyYRBst67n5tejuI5d2G/6a5H+ODdjxGYf42FSsQH734MYM5Beq2hXWsmRUcuTUcuTf9okcF8hTCwWV/j5OZyI4UqgcFoscqGjpzWZRcRERERWSLndIC+50Afj/SNgXOLmgmOHBweLExkcGXeqjGEOFzgS7fn6+INbRw84TPal25s5xXP3cQ9vSdPq7y8Vg7eP1ryy6JlUwwVqsQ4XJJNn2sX9lu+djAJzn02PzCoxjEf2ds75xLzqdZcz6RCfn3HBXN6jY0d5cHfYAhs9ikAIiIiIiKysM7pAP3GOw9MWaa+GCIH0XIuCH4WiIH1rWn6x8rzPvbOt/7oKfuuO40xNJaDb+rMcmSoyMl8he7WFKPFiKpzbFvbxjtf8ew5Bfzj5YjU5LUUnCNfjugbLc5pPfOZ5rPP5TU2ZuDBl/6Xo3jOUwAW2uT58Jr/LiIiIiLninM2QL/prkd4+Njocg9D5sHBaQXnM9UtzBYMTn58KF9uKgc3M44NFxkpRqe11nhbxme8G4srKrHPpNeuMZcS85nWXJ/N5Ax8Z0uKvtHyvKYALJTJ8+EXe/67bgaIiIiIyEpyzi2ztudAH++742EOHB9boFHJSteZS3HZ5jWnBGGzLXXW+Hg1ijk+UqJYjcmGxnldLfXma845hgsVvvqOl857bI1z0APz8+UrkWNDe5pNayYy1mdyjbmoBaq1DHyti/uZdpifr2tvvrcpmw9TL4G3EBZyqTuRhaRl1kRERM5d50wGvRaYP9I3xhJVtcsKMVKs8vWDJ1jX1pyRndwcbXKmut6lPXY8PVzyTeCAUuQ4OlRkcxd05NJzKv+eLlNbawR3y9cOMl6OaMuEbGwPyWaa/2kudon5VBn405kCcKYmz4eHxZv/PtvnLyIiIiKy1M6JAL2WKTs2XFBwfo6KHfSPVTCMjpZ0PVjuakkzUqgwMFaqz7sezvsy+trjBwfGMYPAjFTgS9Adzq9/PoeGcLOVbV939aVNHdtrz29s+rZUJebLbfJ8eFi8mxNLeTNARERERGQuJrenOivVMmVlNWk75w2Ml+tB2NbuVg4P5nnqZJ7xckQU+/nWo6WIPQf62NrdSqESUY5iLJkjHlhAKvDr2ucrMf2jJV77wvNnzLg2ZmrN/O90aOze2zvl83ds7+GGay6jpyPHcKFCT0funCm73nXVNiqRI1+u4pz/vVg3J2qfb6OlbIYnIiIiIjLZWZlBn1xO/J0jgxQqCs7FZ9JrQZifZ32i6TGA1kzA7r299eZpoRlx7DCg6hzOQRgYucDY0JHltvuP8LwtXdMG0KeTqT2Tpm+r2Uwd6RfaVMvTnSuVCiIiIiKyMp11AfrkcuInTowxVopnP1BWrFRgpEOjUFmYz3FgrMRrnr+ZW752sGl/kJSxl6uOw4P5erB4450HeKRvjHQIqRhiMwyjpzM3p3nLS1m2fTZYqpsTS3kzQERERERkLs6qLu57DvRx3WceYLxcJR346v1iVcH52SCbCigt0GcZGKxty3BirMx03/4wMLpb01zS01HPqO7e28s3njhJNjQ6cmmGCxVK1RgHpAPjb3/p8imDO3ULF5H5UBd3ERGRc9dZMwd9z4E+3nbbg4wUq0SxD8wVnJ89ogXq7ldbymxghuC8dr2BsTL3PXmSt9/2IAC37ryCH7hwLWta05wYLzd9v6qx4+23PcieA31N56lNtxgvVegfLXFspHhOzSkXEREREZG5W9Ul7o1zzU+MlShXYwxmDLxkdaouUIA+39NUIsdgvsKNdx5gx/Yedl21jV2fvq8+ntrpwgBGi82l7o2Z8/PWtDTNcVZwLiIiIiIik63aAP2mux7hw3sepxrHpMwoqUO7LJLAoHdgvH5DqNyQOQ8MQjPC0KhGMY/2jfLy93+FgyfylKOYdGBs7mqpd2+fPF99uvXRRURERETk3LMqS9z3HOjjw3seJ3YOHArOZdHUlleL4pi33/YgDzw12FShEZqRCgOc8w3mhgsVHusfxyXd3suR4/BggdFiBWju3l7LsPeNFpvWR59cJi8iIiIiIueGVZVB7+0f58ob72akUKESxfMuVxaZr8CM2PlAfDBfIQyMTGiUk5tCldhRif1a2mHg73iFFhAERhBHxM7PZ+8fLdGRSzd1b29cHx2YU0d4WHlZ98UYz0p7jSIiIiIiS2FVZdCrcUxXS5rxUlXBuSy40CBs2A4MUqHR1ZomdhPLsIVBQCqw+vMMyIRGHEPkJrLuYfKHA0rViHy52rTO9qHBPC3pxivOvj76fLLuew70ce3N93LljXdz7c33LkpmfjGqAFRZICIiIiLnqlUVoJeqMQ8dHUEV7XImAnxQHRp05lJ8/FdezG9dfQlt2RQERks6YGt3C+d3tfCCrd380hXPoOoc5chRqkZEsfPTK5LzPPf8NTxrUyfZlP/nVFu5MBUGpIJknnoQnNK9fWt3K4VK1DS22dZHb8y61+a1p0Nj997epuctVZA71/Es9zlFRERERFaDVVXiLrIQfGY8IHKOUjXm24eHuO3+I2zoyHLBpHXKAa6/fT8pg4rzXeDL0USTuEw4kUnf2Jnl0GCByDlcvcTDWNeW5s9f+/xTSrR3XbWN62/fT75cbVofvZZhh1NLvR85PsJ5a1qazjNV1v10y+fn69Bgnq6W9KzjWe5zioiIiIisBgrQ5ZwSmi9DD53Pfhvw4T2P092aZk1LDmgOZgHSobG5q5VDg3mSvoSAz8RvagiWU2HAszZ24Jzj4AkfTF6yoY1XPHcTu/f28q5/e+iU+dRtmZDegXEALlrXyrtf9Zwpl2mrZcHHShEDYyU2dOTq150q675UQe7W7lb6Rov1GwHTjWe5zykiIiIishqsqhJ3kTMVJQF25GLA2LQmRzWOGS1Wm55XC2YPDeapRjEDYyXMzEf0iSCAchThnKvPL3/Hy7dz51t/lO/90SvY/YsvAuCDdz/GA4cGCY16qflNdz3C9bfvpxzFXNLTztq2NAdP5Pntz36rPl98qlLvtW1pBvMV8uVq03Ubs+5weuXzp2PXVduoRG7W8Sz3OUVEREREVoNVlUHPVUqkowqVMD37k+WcVsuUT6caw5qs0ZFLkw0DStWYkUKFgbES5Sj2c9QDoxLFVGNIBUbAxDkzAaxtz3JyvEIlclzS01HPjO850Mf77niYR/vHiGNfUu9i4+hwkc1rWkiHxi1fO8iGjiytmRSjxQonxio4HMVKXA/ix0uVU8rZ17VlqUSOno4chwfzbJmmw3mtfH5grMhwvkIpikkFAa95/uYFfZ93bO/hBnxJ/UzjWe5zioiIiIisBubc6um49tx01n0T4+DazXy3Z5v/2XgR3+3ZxkiufbmHJytICESzPMeAC9a2Uo4i+kdLgBEYVCKHa3hO498k2+nQyCSBfSYMuGBtC2PliPZMyInxMqOlKnHsG8sZkA4DDN8V/qL1bTx8bJQtXTkGxsqMl6OmMT1jXSthYPSPlmjLhowUqpQjf53OlhQXrmvn1p1XzPoe3HTXI3x4z+NEsSObCujIpcikwqZGdSKy8pjZfc65y5d7HCIiIrL0VlUG/dH1F/D9P38jlw48xXP6enlOXy+v+N5/8ez+gwzlOvjuxm18t+ci9m98Jt/t2caRzg0Ta17JOWW24Bx8oH18tEhPR46uljQn8xWq8anPgeZAHZIg3sUYjnwl4rH+cc7vyvHEyTzVJMBPB0ZgDuf8EoGZMKAcxRQqEdkw4MhQccpxHR0qct6aLAB9o+VkeTffnK5vtMy1L1475XGTG8oNjpfY0t3SNJd7MRrFicg0KhUYH4exsYnfc/lbREREzlmrKkAHKKWzfOe8S/jOeZfU95mLuWDoGJcd90H7td+6k+f09dJSKSVB+0S2/bF1W1UiL3WVyBFHEQPjlXkfW40nlloLzRgYKxPFjsD8Y84ZqcAH5c5BsRoTGAwXKqxtS3NspEQUN1ewOPw9peMjJTKpgA3tGUaLExn0jlyKe3pPct2ksew50MfbbnuQsVKVKHYMjJUoVWIuWDt7x3eRc161On2wPJ/AevLfUQTt7dDW5n/P9veGDf73Rz+63O+IiIiILJNVF6BPxVnAk92bebJ7M1/cfmV9/7rxIZ7dd5Dn9PXyI0/cz66v/zNbh4/Tu/b8esC+f+MzebjnIkazbcv4CmSxhAFE8fSPR7HjyHBp1vPMNBEkMB9U14LoSnLBmMbl1rzY+QC9lAo4vyvHUycLp17LOarO0RaGrG/PNnVsd85NGWC/746HGcpXCM0IzXCxH/PRoQKdLZn689QNXVa1anVhAufJf1ercw+i29th/fqJv2d6bjZ7elVcO3cu/HsnIiIiq8KqCtCDef6Hzom2Lr520Qv42kUvqO/LVYo8q/9JnpME7q9++Kts73+CE61rTsm2H+1QifxqNqnp+rTOtAtDDDgHmTBgfXuWI0MFMmFQXxcdqDedS4VGFDs/Xz0X05oJqUaOOAnKcRAExra1bXS3Zee83NjBE3lfCh9Y/bWnHFRiZlxnXWRR1ALphQiiG/dVKnMPotvbYd26uT33dANpERERkQW2rAG6mb0c+CC+p9ctzrn3LfY1i+kcD25+Fg9uflZ9XxBHPGPoGM9JSuR/4Vtf5LLjvWSiShKwX8R3N25j/8Zn8vjaLVTDVXVf45y1vi3NwNj8S9enkg7AYaQCI3bUs+QOH5xHzrGpPUsqNLpa02xoz9I/VsI1TGIPzDDMd5jHcXK8Qndrmv6xUj3LnwmNjmyKd77i2QBcf/v+0w6wAzPSAU0d31+ybe20a7LLOSiKFr6se3wcyuW5Bca13xdcMLeAW4G0iIiInOWWLdI0sxD4MPA/gcPAN83sdufcd6c7ZrE6zsdByMG153Nw7fn8v2f/SH3/hrFBnt3Xy3P6DrKj9z7efM9nOX+kn8fWbUmy7D54f7hnG2NZlQ2vNP0LFJwDdOZqTeT8HPNa07jQIJsOuaC7hfFyRE9Hjne/6jmAD67H0lWKFV9v7oN63829JR0SmGOkWK0H54bPgNe+5fNZbmzb+jYe7RvDnMPM3zSIHVzSM9Hxfc+BPq6/fT/p0OhqSdeXc7shuZasUM75gLeWka795PNnHlCXy3MPotvbobt7bhnsXE6BtIiIiMhpCJbx2j8APOac63XOlYHPAK+Z6YAwf4Ls0Qf8RhyxZt/HyD79oN+OyqzZ9zEyxx4CwKpFv93n430rj/vt/u/57dIoa/Z9jPTAowAExWG/feJxv50/SfnAv/Lf3ev4yBWv5a0/9qv89At+kB/4pffx7pe9me+s3cRzH/4yv3/Xbr7x4dfzlb/539zydzt5y3/+LS997BtsOfIQa775d4RjxwFIDT3Fmn0fIxwf8NuDT7Bm38cI8icBSJ943G8Xh/32wKOs2fcxrDQKQKb/e367PO63+77rt6u+E3jm2EOs2fcxiMoAZJ9+0G/Hvp959ugDfjuRPbKPzvs/Ud/OHfoGnQ98amL7qXvo+NY/1LdbnvwvOh78zMT2E1+l4zufndju3UP7Q/9c3259/G7a939uYvuxf6f94dsnth/5Em0HvlDfbvveHbR9746J7QNfoPWRL9W32x++ndbH/n1ie//naH387onth/6Zlt499e2O73yWlie+OrH94GdoefK/Jra/9Q/knrqnvr3mgU+RO/SN+nbn/Z8ge2Qf4APn6Ks31797cRTRse9jdJ94iPO7W/nATz+HCx7+e979giq37ryCy7e08u63XwdHH2JjR46gPE7nvo+R7v8elSjGlUbIff2jRMceJY4d2fIIXfd9jOxQLxs7cnREI7z77ddx3333AdD/9CFG7/prvvXgg9x45wFu/fdvsHPnTvbv3w/A9773PTr2fYyucr+/cXDyEK1f/yhd1RO84+XbefDBB9m5cyf/9/Z7SIdGePIJTn7pQ2RKQ6RD4/3/8EV27tzJsWPHAPjv//5vdu7cycCA/67u3buXnTt3MjQ0BMDdd9/Nzp07GRsbY8+BPl79jg/z4lf8LD/313vZc6CPL37Rn69arQLw+c9/np0Nc2o/97nP8eY3v7m+/dnPfpbrrptoe3frrbfy1re+tb79qU99ire//e317Y9//OP87u/+bn37lltu4d3vfnd9+yMf+Qjvfe9769sf+tCH+OM//uP69gc+8AFuvPHG+vZf/uVf8pd/+Zf17RtvvJEPfOAD9e0//uM/5kMf+lB9+73vfS8f+chH6tvXv+tdfPzDH4b+fnjySd7/xjfy+T/4A9i7F+64g0++5jV87U1vgt274a/+ii/+0A+x/zWvgbe8Bd74RvY961kcefGL4WUvgx/+YQ6tX8/Y5s2weTOsWUMUhsTt7fDMZ+Je8hKOXXEFwz/3c/B7v0f0V3/Ffb/zOxz99Kfhu9+lfOwY//bFL/JooQCXX874j/4oHy4U+ParXw0f+ACDf/M3/O5LXsI3Pv5xOHmSY4cPs/N1r+Prn/scPPIIhz//eXY++9nc90d/BLfdxhPveQ87KxUe/Pmfh3e/m8euuYad+/ax//nPh9e8hu9t2cLOj36U76XTcOGF7D9+nJ1veQuPPe7/d7T23XviiScAuO+++9i5cyeHDx8G4Otf//ppf/cAvvzlL7Nz506KRf+/g2fbd09ERETOPctZq30+cKhh+zDwg5OfZGY7gZ0ALW0dpAJj9pZei6eQzvKtDRfwnc4uPp/KM37Jy4g7NvHMww/ywm9/gYsqJX75/i9w2bFHCaslvvvIw+w/bzvfa+/kifwwD0XVOS0BJksnmbZNJhXQvSbL+vYchx+AdBBAGFCJfYo7DIxcKvCpaWC8HFEYLfK7//JtWp8e4btHh3lZcs5iNWZNGNDSkmZdW4Zyw/W6WzOUYkdHLuSkcwT4bHwUOQ4N5jk/HVOqxnzrqSH+Zv9JRk4WaAVwjkf7xvjLo0+yLd9cHdDVmuat//NSvvCk8VSljyCX4s3/81ns2N7Dgw8+DcDTIwXW9qxtGktLOqR/rMTG03jfvvpIP3961xO4QoVUYPQnGfnXrB05jbMtEucIy2Va8nl46inI59lw6JDPIH/pS5DP8+z77iNVqcAHPgDj4/zIf/wHuSiChx+G8XF++oEHaHEO/vEfIZ/nzU895R9/17sgn+c9hQJxJgN/8AfQ1sYvjI8TtLfDf/4ntLbygt5e2gYGfCa8tZV0tUo1l4OLLoLWVr771FOUnvc8zr/6amhr41Mf/CAv3rGD//mTPwltbfyft72NV77mNfzET/wEUbXK9W9+Mz/5kz/JK1/5SirFIruvu47Xvva1bH7ZyyiPjfH/fuu36PiZn+GSl76UytAQD+7bx/e96EVwxRVEAwOc6Oig2t0NLS0wPLzcn5CIiIiINLDFKhuf9cJmPwP8uHPuDcn264EfcM79xnTHdG59lnvBb3yEJ0+ujmWiNowN8py+Xi47/jjP6TvIs/t62TwywKPrtzbNbT/YfT4nWteoJHSZbN/Y3jTnOx0aBwfGSXq2AX5N8zA0qpEjMCN2MdUY0qH/zNa1ZcikQm645jJ2bO/h2pvvbWrwNlqscGy4iANeeEE3j/aNsqkzx2N9YxSrcdM66wakAqMlE1IoV3EYsfPrqWN+LC96xtp66fpcTR4T+AZyPR25eZ9rtvPtumpb05rsp5TmRxEUCqf+5POzb0/+qZV7T/V3oeDnLbe2Tvy0tc3+93ye19ICwXIWI8nZxszuc85dvtzjEBERkaW3nBn0w8DWhu0twNGZDnAOUqGRDY1StDw3Fuajv72br7S/iK9se1F9X2u5wPb+J3wX+eO9/NT+/+SCoWNkq2We6trEoa5NPNm1yf+9xv8+vGYj5ZTWbl8MYWD14Hz33l6G8mVGS1Ua71sZUIkdDoidIwyMKEo6pQcBlWpE32gJHPzaJ/fRnTSJGy74LHdLOiQMjJ7O3CkB/FQc4PDz0yf2TPxZjhyP9o3O+7Xuumrb9E3nagFzsdj8e6ogOdn3o3c+RKerkq2WyJZLZColMuUSrpCn9S/K/G61TK5aJl0ukqmUKAUR2UrJH1+p+HnKtQC39jPbdkuLnwc91yC6pQXCcN7vlYiIiIjIcljODHoKeAT4MeAI8E3g551z+6c75lnP/X73wus+wqPHRxgqVKjOsL71atNRGueCoWNsHTrGM4aeTv4+zgVDx9g80s94poX+tm7627sYaO2mv62L/vZuvy/5OdG6hpFcO6VUZvYLCuCbvF3S006+ElOJIo4Nl2Zd8zwVGOXIkQ4AjMqktc7TgbG+I0M1cqxryzBejk5p8FZr2HZkKE8AVGPfBDEdV2mLKqQqJXLVig9+qxVyye/a9hoq/Nmrn3VqQN34e4p9Y0OjjAyNERaL5OIKbXGFVKnoA/SWFh80N/6eIXC+/XuDDLoQl2uhlMlSTmcZDVL0VwNyHe3Q2lrfP0yKju5O/vqNV/rj1Y1bZFrKoIuIiJy7li2D7pyrmtn/Ab6EX2bt72YKzgE6cqmmjtTvu+NhHjk+xtkQp49m29i/8Zns3/jMUx4zF9NVGGXD+CAbxof877FBNowP8qz+J+t/r8sP01kaA4yRXBsj2XZGsm3J320M59oZybUxmm0jn86RT2cppHOMZ1oopLPJvhyFdI5SKk05TFMJUlTCFNUgXH0BlXOk4ogwjvxvFyd/J7+df8ydjLmkI8OJoXHWV6oNz/OPZ6IqmahCOqqSjSrknP+djqqE1QrpaqX58bhKNq7S5qpYpUKrq9JhMRfckmK4XOLkyVEuKJf5TLVMUC6RrZTJRmWy1QqxGcVUhlLyU/87zFBM+9+lVJpSOgv/NXBqMF2bWzw50M7lIJejvaWF9qkeS6fn/fl2HujjL5IpAY0Z+Xy5yqbOHNZwPuccTxQqfnwiIiIiIjKlZcugn47LL7/c7du3b9rHr93939xzcHAJR7QyZatlOotjdBbHWVPyvztLyU9xjM7iGK2VEq2VIi2VIq2VIq2Vkv+7XKSlUiITJUFnXCUdVUnHEaUwRaUhaC+HKapBCmcQW+DnSlswsW3JNo3bhsMwHIHzxdyBc5hLfuP/btyeeF6MOfxvIIjjepAdTgq6U3FM6GIqQUgUhFSDkMgC/zv5u74/CEll0ozHnLI/siB5rWmiVIpSmKYYpKmm/L5SmKYcpCin0lTCFFEqQyWdpmT+vSql0vSs62DcQoarRjmVJtPWQomQoyUYIU2UzdDW1c6JSkDBGZXIkQmNMAgoVJpbChpJFj8M+N4fvWLW78KeA30zzwU/Q7XzNy4Dt3tv74LOdRc51yiDLiIicu5azjnoC+7WXT/ETXc9wof3PE7pbKp/n6dSKkN/+1r629cu2DnNxaSjiHRD0J6JqqSjCgYNQXVMQEOA7eKmQNsH2Q6XBOpxLYg3a9rnzIixqfcnQf9UgXY1mNh2NrfGXaHBhevb6O0fn7a83aC+vni9mVuyHnqtwVtgRjoMiJ2jEsWEgZEJA7ZtaAfg0eOjYLCpM8fRoSLW5jPLzjmGKwGZ0AgiRyaZMh1PKp0PzHeWj5zjonWts76upVj3fMf2ninPNe1c92nGuZg3EUREREREVouzKkAHuO7qS3neli527+1l3xMnT5kfLKfHWUA5FZyVzepiByOFCunQzy2fShAYUexoLAIPzdjUmSUIAsrViBPjZapx3NTTbX17tv53NY4xM54eKvjvpZuoKr+gu6Xe/fzGOw/wSN8Y6RA2tKQ5OV6hNiwLoCubrje2m8nuvb2kQ6tnslszKfLlKrv39i5qALxjew83JNdvzKxPdc2luIkgIiIiIrJanHUBOjRn9fYc6ONd//odDg9N3TFbxAH9Y2VSgfmA2TUvedbdmmYw7ysFaunywPxP/1iZ3b/4oqQD/EmK1YboPHIcHS4wMFZifXuWVOCz640rEDjnT1mNYg4P5uvf3cbS8Us35nDOTdlsbiaHBvN0tTTfUGlJhxweXPxlCqfLrE+2XDcRRERERERWorMyQG+0Y3sPX3vnjwFw012PcMvXDjYsXyUyoZpkyFsyIa3pgEs2dvKSbWu57f4jnMz7JdNqkXvY0ABtx/Ye/u1bh5uDc/DNCyNHhZgjQwVa0wH5ip960ZSJD4zjoyVesNU3UFuoku+t3a2nzAUvVHyQv1Is500EEREREZGV5qwP0Btdd/WlXHf1pafsf/n793Dg+PgyjEhWGgcUKxGtmZCXbFvLPb0nGS9VCMyXwhuQCvx880oUk0sHXP5H/87AWBmYmI9eEyfbqcA4v7uV/rESo4UKpchh5pdkA+pztBey5Lu27vnAWJHhfIVSFJMKAl7z/M1n/kYtkNVwE0FEREREZKnMrYvWWe7Ot+7g47/yYl6ybR0d2ZAwWGXLicmCih0Mjpe56e5H6Rstct6aFjZ2ZpM10AEzYheDwZrWFPnSzBUZ2zd1cnFPO2OlKpf0dLBlbSsXrmulNR3i8I3isqmAd/3bQ1z3mQeoRBGtmRRmvvQ7HRq79/bO+3Xs2N7Da194PifHK5QjRy4V0t2a5rb7j7DnQN/pvTkLbNdV2+pLsznnf8/UUE5ERERE5Gx2TmXQZzLVnNlaSfx4OSIEqs6hnnNnv1q2HEc9s7u+PUepEjNcrCYpcqOrJUU2FVKolOrHTv561O711LLCu67axttue5CxUpVyNa53hbcoJjTIlyMK5YhsKqQj50u/z6Tk+57ek2zpbjllybOVMsd7Pg3lRERERETOdgrQZzBVSfxbP3M/n/vW08s0IlkKqSCgHPm54r39Y5SjmNCMcjUmCIztmzo4cGyU4UJl1n4G69rSp2SFDYiiuOlmT+zg6HCR0HxZfP9oqR6gFyoR7dkU195877znpa+GOd5zbSgnIiIiInK2U4n7PL3/dS/kife9ql4S35LWW3i2cQ158GrkCM0oVWNi/JrpZkY2FRA5iGJHOgyYPCnCgPVtaXLpFD0dOW645jJ2bO9h995eOlvSZFIh2eQ4A2LnCLD6umvFalQv+R4pVOgfK9E3Wmyalz6XMvWt3a0UKlHTPs3xFhERERFZmZRBP03TZf1uuusR3n/Xo6eUOsvKlQ4gxohjH5pXG5ZBK0cx6dAmPs8kgF7fnmX8ZB6XPKf+MJBJBez+xRdN+f2oZbRrWXkzv9SaS9ZEj5xjXVuGfDliuFBhS3cr6cCoxI7WTIqRQoWBsRKlasx1n3mAm173ghmzz7VGcflylZZ0SKESzTjHe6E6yIuIiIiIyPwp/bvArrv6Ug6+71W85KLu5R6KzNH53a1s6WqhNeMbBDr8P4x0ABiUI0dgPnseOx+q2zR9BAODbevbpg1qaxntTBjgnC+nT6a0EztHGBiZVMhNr3sBX33HS7l15xWMlSNa0iEjhQpHhws+qx/AeLl6SiZ9z4E+rr35Xq688W6uvfleAG645jJ6OnIMFypN2fzJah3kTydTPx+Tx7hSGtaJiIiIiCw3ZdAXya27fqj+d2OzuUhd5lac2lzvzpY0jx4fJR0aoRnVyJELjdj59dGrsQ+gnXMcGy5Oea7I+a7sjRqz0h3ZFMOFCh25FCfG/dJsYVLnXo4cKedoy4RNx9eWIhsYKxFgBIERO8ilgnqH9x3be6Zfou2ay7h15xWzvg+79/aSDq3eUK41k1rwhnILuYyciIiIiMjZRhn0JXDd1Zfy7ff8OI//ySt5Qtn1FaExAd4/WqS3f4yHnx6hWI3pyIasb88S46hUI8pRTLEaYwbrW9MMFypNWfbA/PkC8z/j5Yk535Oz0uUoxoCuljRrcila0iFt2RSpIGBTZ5btmzopR3FT5rq2FFmpGoM5YudwDjZ0ZJsavjUG2KezRNuhwTwt6eabAwvdUO5MxygiIiIicjZTBn0ZNGbXa/Yc6OO3/+kBTuRn7gouC6Mxx318pNT0WP9YhQ3tPsM7MFb2y6AB7dmQ/nGf/c6mAuI4JggCUkm9exw7LKCpAdtUWWmA7rYsd771RwG49uZ76Rst1h8bKVQ4MV7mVz7+TTpzKd5w5UXccM1lXPeZBxgvV8mlAjZ0ZOnI+Q7xteudacf2Wqa+cUm2hW4otxq6youIiIiILBcF6CvEju093Hf9j9e3b7rrET6853GfNZVFFZgvTQ+DiUZx/WNlUgGkQsOSNc8HCxUA8qUqa1rTjJWqVKo+I14P4lNhUwO2uQSkjc85Nlygf6xcf2y8VOWv7nqUjmzI+V0tnBgv09mSpiUdnrJ823QB9lyXaJtvQ7nTsRQ3AUREREREVisF6CtU4xrsew708b47HubR/nHNYV8Etbc0il1T6Xs1hmwI53Xl6B/1878t8HPFRwpValPNa59IOjRy6ZBvHx6qzzkfKVQYK1aoRI5yFJMJAzpyKS5a316/TmPQWpuXXhtHbWzj5YhKcvMgEwYMFyq0Z1OkA8e7/u0htu5t5SXb1nLb/UeaAuyRpBy/HMWzzvnesb2HG/BZ/8ODebYsQhf3pbgJICIiIiKyWtnkhlYr2eWXX+727du33MNYMfYc6OPGOw/w8LHR5R7KqhTgg+uZ/gXkUgGXbOzgwLERQjMi56hGjkwqoBzFOOcD5s1dOTpyaQbGipwcr7Clu4WWdMjRoQIn8xUC8wF87HzQ/ZaXXtx0A6bWOO3x/nGgeY58bXzfd/4a8uUqPR25eqCbDq0p0H3tC8/nnt6T9QB7cLxUX6KtpnaOuTSOWwy1pnmLdRNAZLUzs/ucc5cv9zhERERk6SlAPws0BjxD+RKjJZXFz0WtsVs0wz+BwOCi9W0cGy5SjuJkiTMjHQYUKhGGL4OPkg7v1WRJtudsXsNoscJTJ/P1LHgqMLKpgM6WFBeua28KkGuf4b0HT9SD/kqyvrpLxnHZ5jU45+rro08uFZ8q8L7yxrvpakljyTz52jrqxWrMD1y4VsGxyAqkAF1EROTcpRL3s8CO7T3TBlk33fUIf/OVxylUzu6gvTYPfL5i59c0n+o+VWCQTQV+DfF8mWrsMPNrkMdJOXxgUI2S0vPAqESO2EHfSJHBfIXGGQlR7BgvR+TLEf2jJfYc6Kt/brXP8Ka7HuGDdz+GS15N7fD1bRlgYr72XJutNZbP19ZRB8iGpiXORERERERWGC2zdpa77upLefgPX8ET73sVv3X1JWRTZ+dHPt/g3EgCcyAwa3osMEgFEJrRlk1x684r2Pful7H7F19EezZFFPus+fr2DLGbaBDn4onz9o+VsGTZtcljdPj57dfden99KbWa666+lLe89GJa0iFm/lzdrSl6OnNNTeG2drdSqERNx07VbK22RFu+XGVgrJS8dqOnM6clzkREREREVpizM1qTKV139aXs/sUX8ZJt61jfliYVQCY0wsBmP/gskwqMWly+oT1DWyYkrK9nbmRTIes7MlzS01E/Zsf2Hm563QvY3NXCpjU5NnbmgOSYwEiFRkc2xCXzzMtJh/fpjJQi3vwPUwfp337Pj9P7p6/iY7/8YrZvWsNwoUJPR44brrmMHdt7mgJv59wpHd0bx3zDNZfR05GjWI1JB1afLw9a4kxEREREZCXRHPRzWOPc9fZsitFihaNDRc62YvhUYFQbas0Nv6Ra5Bxrcim2rm1rKv9OB8Z5XS1UIlcPiBvVuuofPJGnXI1Jh8Z5a1oAODpcIIpdvbS9FqDP9K/sgrWtU15nNvNttjZ5vXVY/oZxInIqzUEXERE5d2kO+jlsprnrjVb7muzVSUvTOeCSnnb6Rov1wLozmc9da6BW65Q+3fuTr8Rs6W6hGsUcOlngqZP5eql7YNDTnqmvmx7Fbso57iTPr5WZzzdAn+vnV6MlzkREREREVjYF6DKr666+lOdt6eLGOw/wveOjOHd6DdlWikxojJcjNrRnKVSieka5syVNKrRZM8q79/aSDq3eeC2YlKF3wHChUl+zPnbTN7HLJMukLUWZ+VKscy4iIiIiIqdPAbrMSS1bW1t7/dG+UarxRIA5XKxOedzpdldfDLWxBGakQ8PMqETxvDPKjR3UB8ZKhGak0kapEvul1mJHOXJkUwGRc6TNeOVzN/KF7xyj0rCmW2hwXlfLlM3dampl7IcG82xdgIB6vll3ERERERFZOpqDLqetcQ40wPHhIuVk+TEz2NiZZV1blsf6xijOoTy+MZgPkqXPzvTbGUB9Tr3vzm444FkbOxguVPiZF23hlq8dZLwckQ0D1ralwawpGJ4cJA/ly5SjmNZMigPHRgjN6uXtpWpcH3M2FeAcrGtPc+G6dnZdtY333fEwj/aPkQ4CNnZmSYXBjHPdr799P+nkJkjtBsLpzFefj4W+KSAi86M56CIiIucuBeiyKCY3oDs8mCdfjmhIIBMatGVCNne1MF72WeTvHBmkVHUE+C7rjQHv6ao1asulQ2LnSAXGpjU5MmHAeDkiHRrVKObIUBGA87ty9cD5tS88n9vuP9IUJA8XKhi+JP7YcJFiNSKOfSf3OHb1YL01E7KhI0t7NsVwocJX3/HSU96bmcrMl6Op23LdFBCRCQrQRUREzl0qcZdFMbmUutb5/PH+caqxIxUaz1zfxjtf8eym5117870cHBhjtFilHMVkQqPS0BX9dARWW3s8BgedbWkqkSMdONKhEcWOQ4OF+lzx4yMlLtnYQb5c5ZavHWRDR7YeJNd+pwOjuy1L30iRKPY3G1IBlJJ0/Yb2DBuTBnT5crWphH2uZeaNpfQ1iz1fvXF+PfjXmy9XT6uJnYiIiIiIzI8CdFkScw1Ka53GN61JNWWsAQbGyvO+bmC+Y/vAWIlykr4fL0Wkw5jjIxFrcimGCtWmRm7FasxosUJ7NsV4OeKCdNh0zpZ0yHChwp1vveKUGwrZVEClGjNSrNLT6Wad1z5TOfnW7tZTMugzzVdfCMtxU0BERERERDwF6LKiTNVp/N2vek59Lvh1n3nAl8onc92nm9keBgDGW156MdddfSlwavn2Y/1j9I2VSYdWn/MOfv58/2iJMDDaMmFTp3doDpIPDeZZ355lQ0eu/vhIocyxkRLDhcqMJey15euqcUw2DIjimOtv388NyfuwHMuiLcdNARERERER8RSgy4ozXbZ9x/YebnrdC7j+9v30jRSJnS99n7zOeWDQlknxhisvqgfncGr59saOHE+ezFON/Lz0ShKhhwbFqg+G33DlRdx2/5Fpg+SpAtpUGPDCC7pnnCe+50AfH97zOLFzpMOAyMGJsQrr2qmXky/HsmhaK11EREREZPkoQJdVpRa03njnAR7pGyMdWlNTt5mamU0u3+5sSZMNjVLk69uzqQCcI3K+eV3tXM/b0jVtkDw5oD0xXuLkeIWhfJlrb7532oB6995eotjfGLCkIV6MYzhf4XAwUU6+1MuinclNAXV/FxERERE5M+riLqvWXLuh10zVFb1/tMhgvsKW7pbT7lpeG8ejfaOMFqt0t6ZZ356d8VxX3ng3g+NlqpEjCHyfeYejEsX8wIXrFq1L+2JR93eRhaMu7iIiIucuZdBl1Zpvdnmq8u1MKuTXd1zAPb0nT7uMvDaOyTcAZuqAvrW7lWoUc2K8DLGf9x45RyoIVmU5eW36QBQ7Dg6MU45iQjNuvPOAAnQRERERkTlSgC7njJnKt6+b4vnzLdk+NJgnNOjtH0uWiAtY356ZsgN67WbBurYMo8UqpWpMGBi/vuOZqzKgrb32p4dLmEGYrAn/SN8Yew70rcrXJCIiIiKy1BSgyzllrln3xpLtrpY0faPFpg7rU2nPhDzWP05oRmhGNXIcGSpy8Ya2Kcex1A3gFtPW7lYeeGoQMwjMl+wbkA7RGuoiIiIiInOkAF1kCpM7vs9Url5jSWCKJT8ArmH/JEvdAG4x7bpqG7/2yW8SmuHwS9bFODZ35LSGuoiIiIjIHAXLcVEz+xkz229msZmpEY6sOIcG87Skw6Z9LelwxmBztFT1HeUDq3doP78rx1iputjDXXY7tvdwyYZ2gsD8XPrQ2LymhVQYaA11EREREZE5WpYAHXgI+Clg7zJdX2RGW7tbKVSipn2FSjRjsLm1u5VUGLBtQzvbN3WybUP7ORWgvvMV/z97dx4f113d//91ZtEuW97kLLaTmCQ4CYQtQIB8UxfotwRoQn+lLWlZ2oY6tLQptFBCC6GkpU2+LWUpFJymbAESIC0lbQlLmhpDG0OcQCCLs9lJvMWybFmWNKNZ7j2/Pz53RiNZqy1ptLyfPMxo7ty585kZRTPnnvM5n3PobG9i3fIWzljZSiZtWkNdRERERGQK6hKgu/tD7v5wPR5bZDKuvHg9pcjJFcu4h8uJgs3juc98s2VHF5ffsI2Lrr+Ty2/YxpYdXdXbNm7o5NpLz6OzvYnefInO9iYtsyYiIiIiMgV1XQfdzLYA73L3MRc3N7NNwCaAdevWveDJJ5+cpdHJYjfVddaP9z7zhdY6F5kdWgddRERk8ZqxAN3M7gBOGuWmP3f3byT7bGGCAL3WBRdc4Nu3T2pXEZlmI9d5B8gVy3S2N3HzpgvrODKRhUUBuoiIyOI1Y13c3f2VM3VsEZl9u3tydDRnh22bqHGeiIiIiIhMXr2axInIPHM8jfNERERERGTy6rXM2i+b2R7gJcB/mtm36zEOEZm8xdAET0RERESknmasxH087v514Ov1eGwROT4bN3RyLSzYJngiIiIiIvVWlwBdROanjRs6FZCLiIiIiMwQzUEXERERERERmQMUoIuIiIiIiIjMASpxF5EZs2VHF5u37mR3T461mrMuIiIiIjIuZdBFZEZs2dHFNbc9QFffIB3NWbr6BrnmtgfYsqOr3kMTEREREZmTFKCLyIzYvHUn2bTR0pDBLFxm08bmrTvrPTQRERERkTlJAbqIzIjdPTmas+lh25qzafb05Oo0IhERERGRuU0BuojMiLXLWsiXomHb8qWINcta6jQiEREREZG5TQG6iMyIKy9eTylycsUy7uGyFDlXXry+3kMTEREREZmTFKCLyIzYuKGTay89j872JnrzJTrbm7j20vPUxV1EREREZAxaZk1EZszGDZ0KyEVEREREJkkZdBEREREREZE5QAG6iIiIiIiIyBygEncREZE5YMuOLjZv3Ul21enPrvdYREREpD4UoIuIiNTZlh1dXHPbA2TTBh6X6z0eERERqQ+VuIuIiNTZ5q07yaaNlgadNxcREVnMFKCLiIjU2e6eHM3ZdL2HISIiInWmAF1ERKTO1i5rIV+K6j0MERERqTMF6CIiInV25cXrKUVOrqjp5yIiIouZAnQREZE627ihk2svPY/O9iawlCaii4iILFLm7vUew6RdcMEFvn379noPQ0REZMaY2T3ufkG9xyEiIiKzTxl0ERERERERkTlAAbqIiIiIiIjIHKAAXURERERERGQOUIAuIiIiIiIiMgcoQBcRERERERGZAxSgi4iIiIiIiMwBCtBFRERERERE5gAF6CIiIiIiIiJzgLl7vccwaWZ2EHiy3uOYZiuB7noPYhbp+S5ser4L22J7vlCf53yau6+a5ccUERGROWBeBegLkZltd/cL6j2O2aLnu7Dp+S5si+35wuJ8ziIiIlI/KnEXERERERERmQMUoIuIiIiIiIjMAQrQ6++Geg9glun5Lmx6vgvbYnu+sDifs4iIiNSJ5qCLiIiIiIiIzAHKoIuIiIiIiIjMAQrQRUREREREROYABeizwMxeZWYPm9ljZnb1KLdvNLNeM/tJ8u+aeoxzupjZZ8ysy8zuH+N2M7OPJ6/HT83s+bM9xuk0iee70N7ftWb232b2kJk9YGZ/NMo+C+Y9nuTzXTDvsZk1mdmPzOy+5Pl+cJR9FtL7O5nnu2DeXxEREZnbMvUewEJnZmngk8AvAHuAu83sNnd/cMSu33f31876AGfG54BPAF8Y4/ZLgLOSfy8GPpVczlefY/znCwvr/S0Df+Lu95pZO3CPmX13xO/0QnqPJ/N8YeG8xwXg5e7eb2ZZ4Admdru7b6vZZyG9v5N5vrBw3l8RERGZw5RBn3kvAh5z953uXgRuAS6r85hmlLtvBQ6Ps8tlwBc82AZ0mNnJszO66TeJ57uguPt+d783+bkPeAg4dcRuC+Y9nuTzXTCS96w/uZpN/o3sJrqQ3t/JPF8RERGRWaEAfeadCuyuub6H0b/cvyQpsbzdzM6bnaHVzWRfk4VkQb6/ZnY68DzghyNuWpDv8TjPFxbQe2xmaTP7CdAFfNfdF/T7O4nnCwvo/RUREZG5SwH6zLNRto3MztwLnObuzwH+Afi3mR5UnU3mNVlIFuT7a2ZtwL8A73D3oyNvHuUu8/o9nuD5Lqj32N0jd38usAZ4kZk9a8QuC+r9ncTzXVDvr4iIiMxdCtBn3h5gbc31NcC+2h3c/WilxNLdvwlkzWzl7A1x1k34miwkC/H9Tebq/gvwJXf/11F2WVDv8UTPdyG+xwDufgTYArxqxE0L6v2tGOv5LtT3V0REROYeBegz727gLDM7w8wagDcAt9XuYGYnmZklP7+I8L4cmvWRzp7bgDcnnaAvBHrdfX+9BzVTFtr7mzyXfwYecve/H2O3BfMeT+b5LqT32MxWmVlH8nMz8Epgx4jdFtL7O+HzXUjvr4iIiMxt6uI+w9y9bGZ/AHwbSAOfcfcHzOxtye2fBl4P/J6ZlYE88AZ3n7flomZ2M7ARWGlme4APEBovVZ7vN4FXA48BOeC36zPS6TGJ57ug3l/gZcCbgJ8l83YB/gxYBwvyPZ7M811I7/HJwOeTFShSwFfd/T9G/M1aSO/vZJ7vQnp/RUREZA4zfccQERERERERqT+VuIuIiIiIiIjMAQrQRUREREREROYABegiIiIiIiIic4ACdBEREREREZE5QAG6iIjINDGzz5hZl5ndP8n9f83MHjSzB8zsyzM9PhEREZnbFKCLzANmFpnZT2r+nW5m/5vcdrqZ/UbNvs81s1cfx2NsMbMLpmGs03IckXnqc8CrJrOjmZ0FvBd4mbufB7xj5oYlIiIi84ECdJH5Ie/uz63594S7vzS57XTgN2r2fS5hjWoRmWXuvhU4XLvNzJ5hZt8ys3vM7PtmtiG56XeBT7p7T3LfrlkeroiIiMwxCtBF5ikz609+vA74P0lm/T3AtcCvJ9d/3cxak7Lbu83sx2Z2WXL/ZjO7xcx+amZfAZpHeYxLzOyrNdc3mtm/Jz9/ysy2J6W5H5xgjJjZ683sc8nPq8zsX5Ix3W1mL0u2/1xNlcCPzax9Ol4rkTq7AfhDd38B8C7gH5PtZwNnm9n/mNk2M5tU5l1EREQWrky9ByAik9JsZj9Jft7l7r9cc9vVwLvc/bUAZnYAuMDd/yC5/tfAne7+O2bWAfzIzO4ArgRy7n6+mZ0P3DvK434X2Gxmre4+APw68JXktj9398Nmlgb+y8zOd/efTvL5fAz4iLv/wMzWAd8GziEEL2939/8xszZgcJLHE5mTkt/jlwJfM7PK5sbkMgOcBWwE1gDfN7NnufuRWR6miIiIzBEK0EXmh7y7P/c47/t/gUvN7F3J9SZgHXAx8HEAd/+pmR0TXLt72cy+BfySmd0KvAb40+TmXzOzTYS/IycD5wKTDdBfCZxbE7AsSbLl/wP8vZl9CfhXd98ztacqMuekgCNj/Pe7B9jm7iVgl5k9TAjY757F8YmIiMgcohJ3kYXPgF+pmb++zt0fSm7zSdz/K8CvAS8H7nb3PjM7g5DtfoW7nw/8JyHwH6n2+LW3p4CX1IzpVHfvc/frgLcSyu231czVFZmX3P0oIfj+VQALnpPc/G/AzyfbVxJK3nfWY5wiIiIyNyhAF5n/+oD2ca5/G/hDS9LVZva8ZPtW4DeTbc8Czh/j+FuA5xMaWlXK25cAA0Cvma0GLhnjvgfM7BwzSwG1ZfnfAf6gcsXMnptcPsPdf+bu1wPbAQXoMq+Y2c3AXcAzzWyPmV1B+O/sCjO7D3gAuCzZ/dvAITN7EPhv4N3ufqge4xYREZG5wdwnk0ATkXoys353bxttm5llgW8BKwlLPH2e8MU/C/wNcBvwUcI8WAOecPfXmlkz8FlCafpPgDOBq9x9+yiP/wngt4BOd88l2z4HvJiQ8SsAt7n758xsC2FO/HYzez1wPbAbuB9oc/ffSrKFnyTMO88AW939bWb2D4SMYgQ8CPyWuxdO7NUTEREREZkfFKCLiIiIiIiIzAEqcRcRERERERGZAxSgi4iIiIiIiMwBCtBFRERERERE5gAF6CIiIiIiIiJzgAJ0ERERERERkTlAAbqIiIiIiIjIHKAAXURERERERGQOUIAuIiIiIiIiMgcoQBcRERERERGZAxSgi4iIiIiIiMwBCtBFRERERERE5gAF6CIiIiIiIiJzgAJ0mbPM7HQzczPLTPNx15lZv5mlJ9jv/5jZw9P52BM83hYze+s0H3Ojme2Z5mP+mZndOJ3HHOUx3MzOnIHjPmFmr5zu4y4EZvZpM3v/OLdPy3tiZn9hZl880eOIiMwkM3vAzDbWexz1ZGa/bGa7k+9Mz5ulxxz3O8Z0fY6b2W+Z2Q9O9DgiM0EB+jwz2h8m/ZEZ38jXzN2fcvc2d4/Gu5+7f9/dnznWcY5jHA1JcPKomQ0kx/uMmZ1+vMecScnvVZR8MFf+fcLd/9rd35rsc8xJlJn8fTSzzWb2hVG2n29mBTNbPhOPO5fUvOaV9+QJM7v6RI/r7m9z97+cjjGKiMxlk/ku5e7nufuWCY4zI4mEOeTvgD9IvjP9eOSNyXMfSD6L9prZ30+U/JhI7XcMkcVKAbrI7LkVuBT4DWAp8BzgHuAV9RzUBO5KPpgr//6gzuP5HPD/mVnriO1vBv7D3Q/P/pDqpsPd24DXA+83s1+o94BERGT6zIHA/zTggQn2eU7yWfRzwK8DvzPjoxJZ4BSgL0Bmdk5SLn0kKdG6tOa2z5nZP5rZ7ckZz/8xs5PM7KNm1mNmO2rLmCY41qvN7EEz60vOnL6r5rbLzOwnZnbUzB43s1cl25ea2T+b2f7kPn9VOdtqZmkz+zsz6zazncBrRjyv3zazh5LH22lmV9bcttLM/iMZ52Ez+76ZpczsJmAd8O/J8/3TkWe8zWy5mX3WzPYlr8G/Jdur5eFjHOc/zewPR4zxp2b2ulHek1cCvwBc5u53u3vZ3Xvd/ZPu/s81u56WvCd9ZvYdM1tZc4wLzex/k+d4n9WU3o31HEYZx1XJe7ZmtNsnw4aXKG9NLo8kr8tLgE8DL0muH0nu05i8t0+Z2QEL5dTNNcd8d/I7sc/Mxvxwd/e7gL3Ar9TcN0046fF5M3uGmd1pZoeS36MvmVnHGM/jc2b2VzXXh00HMLNTzOxfzOygme0ys6tqbnuRmW1Pfr8PmNnfj/EYD5nZa2uuZ5JxPd/Mmszsi8lYj5jZ3Wa2eqznPs5rsp3wBeq5NY/zO8lj95jZt83stGS7mdlHzKzLzHqT39dnjfF6jPme2IjpGDYi82RmH7NQFnnUzO4xs/8zxuszLa+BiMh0s5os+zh/84/5DLTw3eN9ZvZk8rf2C2a2tOa4b05uO2Rm7x/xOH9hZrcmfxePAr+VPPZdyd/I/Wb2CTNrqDmem9nvW6jO6zOzv0w+C+9KxvvV2v1HPMdRx2rhM7sfSAP3mdnjE71e7v4Y8D8M/yx6rYXvgkcsfH85v+a291j4HthnZg+b2StqXoMv1uz3pprX689HjH+iz/GrLXwH7bPw3eeXx3gdxvxsFKkHBegLjJllgX8HvgN0An8IfMnMnlmz268B7wNWAgXgLuDe5PqtwN9P8lj/DFzp7u3As4A7k/u9CPgC8G6gA7gYeCK5z+eBMnAm8Dzg/wKVL/q/C7w22X4BITNYqyu5fQnw28BHzOz5yW1/AuwBVgGrgT8D3N3fBDwF/FKSAf5/o7xsNwEtwHnJ8/zIyB3GOM7ngTdW9jGz5wCnAt8c5TFeCfzI3XePclut30ieWyfQALwrOfapwH8CfwUsT7b/i5mtmuxzsDC/+LeAn3P3yomHI2Z20QRjGs/FyWVH8rrcBbyNocx7R3L79cDZhA/uMwmv0zXJGF6VPJ9fAM4ivFbj+QIhY17xSiAL3A4Y8DfAKcA5wFrgL6b6pMwsRfjdvy8Z6yuAd5jZLya7fAz4mLsvAZ4BfHWMQ90MXF5z/ReBbne/F3gLoZJiLbCC8Lrlj2OsFxL++3ssuf46wu///0f47+H7yTgg/Pd2MeG96CBkOw6Ncsypvicj3U14r5cDXwa+ZmZNo+w3La+BiMgMG+tv/mifgb+V/Pt5YD3QBnwCwMzOBf4R+E3gZMLfv1NHPNZlhO9iHcCXgAh4J+E72ksIn0e/P+I+rwJeAFwI/ClwQ/IYawmfD5czulHH6u6FJCsOIUP+jDFfmYSZbQD+D0OfRc8HPgNcSfj7vhm4LQn+nwn8AfDC5DvkLzL0PbH2mOcCnwLeRPhcXwFMJcHweDKmpcAHgS+a2cmj7Depz0aR2TLvAnQLc3a7zOz+Se7/a8lZswfM7MszPb5Z8m9JYHXEQobyH2tuu5DwB/Y6dy+6+53AfzD8j/PX3f0edx8Evg4MuvsXkjnZXyEEyJM5Vgk418yWuHtPEnQAXAF8xt2/6+6xu+919x1JZuwS4B3uPuDuXYRA8g3J/X4N+Ki7705Klf+m9km7+3+6++MefI9w4qCSmSsRPuxOc/dSMn/cJ3ohkz/UlwBvS55DKTn2ZHwDOMvMzkquvwn4irsXR9l3BbB/Esf8rLs/4u55wheA5ybb3wh8092/mbym3wW2A6+exHOw5Gz/LwI/7+4HKze4e4e7jzdf/MLa37UkGJwSMzPCyZd3uvthd+8D/prh7/tn3f1+dx9g4oD6JuDnbKgK4M3Al5Pn/Vjye1dInuffE8rupuqFwCp3vzb53d8J/FPNmEvAmWa20t373X3bGMf5MnCpmbUk138j2VY5xgrgTHePkv8mj05hjN1mliecYPtH4N+S7VcCf+PuD7l7mfBaP9dCFr0EtAMbAEv2Ge33cqrvyTDu/kV3P5RUinwYaASeOcquJ/oaiIgcr/G+S4002b/5EALjv3f3ne7eD7wXeIOFqr3XA//u7j9IvitcA4z8rnKXu/9b8lmfT/4ubkv+nj5BCHRHfq5d7+5H3f0B4H7gO8nj9xJOXo/V4G28sU7WvWY2ADwEbGHodfxdYLO7/zD5+/55QlLoQsJJh0bCd8isuz/h7qNl6V9PmL621d0LwPuBeLIDc/evufu+5LX8CvAo8KJRdp3sZ6PIrJh3ATphDuqrJrNjEji9F3iZu58HvGPmhjWrXpcEVh1JhrL2TOopwG53r/0D9iTDz9AeqPk5P8r1ylnTiY71K8CrgSfN7HsWypshnLEd7Q/taYRM5/6aD8TNhIxv9fFGPFaVmV1iZtsslLAfSR67UgL+t4Sztt+xUP4+2aZZa4HD7t4zyf2rkg+LrwJvTDKulxOCx9EcIpxAmMjTNT/nGHovTgN+dcSXiYuSY070HDqATYSgrXcSY6i1rfZ3bYIvJWNZRcju31Mz9m8l22GC930kd3+KUFb4RjNrA15HqGbAzDrN7JakbO4o8EWGfkem4jTglBGv958RqjMgnIQ6G9hhoSz7taMdxEPJ30PALyVB+qUMBeg3Ad8GbrFQRv7/kqqVyVpJ+P14F7CR8N9WZewfqxn3YUJlwanJSbZPAJ8EDpjZDWa2ZJRjT+k9GcnM/sRCiX1vMoaljP4+nOhrICJyvMb7LjXSpP7mJ05h+N/MJ4EM4fNj2N9Wd89xbKZ2WKWdmZ1tYQrf08nn2l9z7N/TyX6vm8pYJ+v5yfF/HXgxUOkRcxrwJyM+R9cCpySfje8gnPztSj63TxljfLWv1wBTyGxbmE7wk5rHfxajfBZN4bNRZFbMuwDd3bcSvnBWWZhr8y0Lcx2/n5TZQDh798lK4JJkbBe6fcDaJGCsWEeYtzutx/Iwl/oyQoD9bwyVfO0mlICNtJtw9nRlzYfikuTkCYQM89oRjwWEOczAvxA6iq5OPky/SQg8cPc+d/8Td18P/BLwx5bMZ+LYs9Mjx7TcxpinPMJox/k84Qz0K4Cch/K20dwBvMiOf+73buCmEcFyq7tfx8TPoYcwNeCzZvay43z8sYz2mozc1k34gnBezdiX+lD53Jjv+zg+T8ic/wqwy4eqN/4mefzzPZQivpHkd2QUA4QTBxUn1fy8Ozlu7evd7u6vBnD3R939csLv/vXArXZs47qKSpn7ZcCDyRcTkoz/B939XOClhPfozWMcY1RJVuLDwCBDXy53E6ae1I692d3/N7nPx939BYTpEGcTpqKMNNF7MuZrZ2G++XsIWfhlyX+rvYzyPkzHayAiMtPG+Zs/2mfgPkJwWrGOMLXvAOFva/V7gIVeLCtGPtyI658CdgBnJZ9rf8bYn2tTNd5YJ82DrxIquq5JNu8GPjTis6jF3W9O7vNld78oeXwnvK4jDfssSk50175e430WnUaofPsDYEXyWXQ/Y7x2k/xsFJkV8y5AH8MNwB8m/2G9i6HymrOBsy003dpmSaOyBe6HhD9Yf2pmWQuNxH4JuGU6j2VhybDfNLOl7l4CjhJKliDMTf9tM3uFhQYkp5rZhqRc6DvAh81sSXLbM8ysUqr1VeAqM1tjZsuA2ix4A6Ec6iBQNrNLCHOGgGojkjOTcurKWCrjOUCYW3WMZEy3A/9oZsuS53nxaPuOdpwkII+BDzN29hx3vwP4LvB1M3uBhWZh7Wb2NhunKVqNLxKysL9ooZlek4VmKGsm8xw8LBXzm8njv3gSjzdZBwnPv/Z1OQCssaQpTVKB8U+EngGdEObU29B87q8SGuGcm3z4fmASj/svhA/tD5JkzxPtQD+hYc+pjP8B+xPCFIHlZnYSwytsfgQctdDEpjl5zZ9lZi9Mxv9GM1uVPLcjyX3GWrbvFsLv6u8xlD3HzH7ezJ5tocndUUKJ3bhL/43jOsJ/p02EJn3vNbPzksdZama/mvz8QjN7cZKlHiAE9qM95kTvyU8I3fRbLKyNfkXNbe2EL3gHgYyZXUPoG3GMaX4NRERmxDh/80f7DLwZeKeZnWGhyuuvCdPfyoS55b9kZi9NPiM/yMTBdjvh72N/koD6vel6XhOM9XhcB2xKPlP/CXhb8pljZtZqZq9Jvvs808xeniRfBgkn8Uf7238r8Fozuyh5va5leOzyE8b+HK+cQDkIodEwIYN+jCl8NorMinkfoCd/UF5KaEL0E0LJdKWUOENocLSRkMG6cZKZ0nnLw5ymSwlzkrsJJyve7O47ZuBYbwKesFBy9TaShmnu/iOSJm6EzNn3GDpD+2ZCsP0gIbN7K0Pv1z8Ryl3vIzSt+9easfQBVxEChx7CXN7baoZ7FiFL3U8yJ9eH1i/9G+B9Fkqc3sWx3kQIDHYQGtG9Y4yXZKzjfAF4NiGIHs/rCVn/rxBel/sJzfDumOB+eGgudxnhzPlBwpnpdzP03/CEz8HDvPXfJjRpeQGAha6zo3bYnoykPO9DwP/Y0Bz1OwldxZ82s+5k1/cQpiBsS35f7iCZk+zutwMfTe73WHI50eMOMBSkf6nmpg8Syu16CU31/vXYe1fdRPhde4Jw4ugrNcePCCejngvsIvz+30go1YYwzeYBC11uPwa8wUNPh9HGup/wO/nS2scgnOm/lfDF6yHCfydfBLDQ5f7T44x9pP8k/Hfxu+7+dUIm4pbktb6f8N8whED5n5J9nySUCv7dKGOe6D35CFAknIz5PMPfg28TThg9kjzGICNKNmuM+RqIiMwho/7NH+Mz8DOEz5ethM+PQUKTXTzMEf9Dwonb/UAf4TO7MM5jv4vwnaeP8Pf7K+PsO1VjjvV4uPvPCH/H3+1hhZHfJZSO9xA+S34r2bWREMx3E6b2dRK+34w83gPA2wknt/cnx9lTs8t4n+MPEpIndxE+q55N6DI/mkl9NorMFvOJ+2jNOWZ2OqFpxLMszBF52N2Pmd+bfMHd5u6fS67/F3C1u989m+OVhc3M3gxsSkq1RERERCaUJJmOEMrXd9V5OCIyR8z7DLqHjr+7ako4zcJyVxDmRf98sn0loeR9Zz3GKQtTUv77+4RpFiIiIiJjMrNfSqYHtRKytD9jlCXGRGTxmncBupndTChXeaaZ7TGzKwhza68ws/sIpbWXJbt/GzhkZg8C/00oudG6hjItkjnUBwmlUwtlCT8RERGZOZcRmrPtI0zPe4PPx3JWEZkx87LEXURERERERGShmXcZdBEREREREZGFKFPvAUzFypUr/fTTT6/3MERERGbMPffc0+3uq+o9jnrRZ72IiCwGY33ez6sA/fTTT2f79u31HoaIiMiMMbMn6z2GetJnvYiILAZjfd6rxF1ERERERERkDlCALiIiIiIiIjIHKEAXERERERERmQMUoIuIiIiIiIjMAQrQRUREREREROYABegiIiIiIiIic4ACdBEREREREZE5QAG6iIiIiIiIyByQqfcARERETsSWHV1s3rqT3T051i5r4cqL17NxQ2e9hyUiIiIyZcqgi4jIvLVlRxfX3PYAXX2DdDRn6eob5JrbHmDLjq56D01ERERkyhSgi4jIvLV5606yaaOlIYNZuMymjc1bd9Z7aCIiIiJTphJ3ERGZt3b35Ohozg7b1pxNs6cnV6cRiUit/n74sz+DfB5aW+Gaa2D58nqPSkRk7lKALiIyD2nedbB2WQtdfYO0NAx9nOVLEWuWtdRxVCJS8e//Dv/wD7B6NRw4AG1t8Fd/Ve9RiYjMXSpxFxGZZzTvesiVF6+nFDm5Yhn3cFmKnCsvXl/voYkIsHUrtLfDnj3wutfBpz4FORW4iIiMSQG6iMg8o3nXQzZu6OTaS8+js72J3nyJzvYmrr30vEVZTSAyF33ve3DRRZDJwB//MRw+DDfdVO9RiYjMXSpxFxGZZzTveriNGzoVkIvMQV1d8NBD8Ja3hOsXXQQveAF85CPwu78LKaWJRESOoT+NIiLzzNplLeRL0bBtmnctInPN978fLi++OFyahSz6ww/DnXfWb1wiInOZAnQRkXlG865FZD743vegpSVkzSte97pQ7n7HHXUblojInKYSdxGROW60ju3XXnoem7fuZE9PjjWLuIu7iMxd3/sevOQl0NAwtK2lBV74wtA8TkREjqUAXURkDqt0bM+mbVjH9msvPY+bN11Y7+GJiIzq8GH42c/ggx889raLL4YPfzh0c2/RzBwRkWFU4i4iMoepY7uIzEf33APu8LKXHXvbxRdDuQzbts3+uERE5joF6CIic9junhzN2fSwbYu5Y7uIzA9PPhkuzzzz2Nte9rLQME5l7iIix1KALiIyh6lju4jMR089FZZRO+WUY29buhSe+1wF6CIio1GALiIyh6lju4jMR089BaeeGjq2j+bii0OJe7E4u+MSEZnrFKCLiMxhGzd0cu2l59HZ3kRvvkRnexPXXnqeOraLyJz21FOwdu3Yt198MeTzsH377I1JRGQ+qHsXdzNLA9uBve7+2nqPR0Rkrtm4oVMBuYjMK089FZZTG0vltp/9DF760tkZk4jIfDAXMuh/BDxU70GIiIiIyImLY9i9G9atG3ufU06BbBaeeGLWhiUiMi/UNUA3szXAa4Ab6zkOEREREZkeXV1hbvl4AXo6HW5XgC4iMly9M+gfBf4UiOs8DhERERGZBk89FS7HC9ABTj8ddu2a8eGIiMwrdQvQzey1QJe73zPBfpvMbLuZbT948OAsjU5EREREjsdUAnRl0EVEhqtnBv1lwKVm9gRwC/ByM/viyJ3c/QZ3v8DdL1i1atVsj1FEREREpmD37nA5mQD9wIHQzV1ERIK6Beju/l53X+PupwNvAO509zfWazwiIiIicuKeegra2qCjY/z9zjgjXD755IwPSURk3qj3HHQRERGpAzP7jJl1mdn9Ndv+1sx2mNlPzezrZtZRc9t7zewxM3vYzH6xZvsLzOxnyW0fNzNLtjea2VeS7T80s9Nn8/lJ/Tz1VMieh9+EsZ1+erhUmbuIyJA5EaC7+xatgS4iIjKrPge8asS27wLPcvfzgUeA9wKY2bmEarfzkvv8o5mlk/t8CtgEnJX8qxzzCqDH3c8EPgJcP2PPROaUSoA+EQXoIiLHmhMBuoiIiMwud98KHB6x7TvuXk6ubgPWJD9fBtzi7gV33wU8BrzIzE4Glrj7Xe7uwBeA19Xc5/PJz7cCr6hk12Vhe+opWLt24v1OPjmsha5O7iIiQxSgi4iIyGh+B7g9+flUYHfNbXuSbacmP4/cPuw+SdDfC6yYwfHKHJDPh3XQJ5NBT6XgtNOUQRcRqaUAXURERIYxsz8HysCXKptG2c3H2T7efUZ7PC2pukDsSU7XTCZAh9AoTgG6iMgQBegiIiJSZWZvAV4L/GZStg4hM15btLwG2JdsXzPK9mH3MbMMsJQRJfUVWlJ14aisgT6ZEnfQWugiIiMpQBcREREAzOxVwHuAS909V3PTbcAbks7sZxCawf3I3fcDfWZ2YTK//M3AN2ru85bk59cTllMdNYMuC8f+/eHylFMmt//pp4eS+Fxuwl1FRBaFTL0HICIiIrPPzG4GNgIrzWwP8AFC1/ZG4LtJP7dt7v42d3/AzL4KPEgofX+7u0fJoX6P0BG+mTBnvTJv/Z+Bm8zsMULm/A2z8bykvg4cCJerV09u/0on9yefhHPOmZEhiYjMKwrQRUREFiF3v3yUzf88zv4fAj40yvbtwLNG2T4I/OqJjFHmn64uaGiApUsnt3/tUmsK0EVEVOIuIiIiItPkwIGQPZ/sgnqVueq7d4+/n4jIYqEAXURERESmRSVAn6xKT0A17xcRCRSgi4iIiMi0mGqA3tQEbW3Q3T1zYxIRmU8UoIuIiIjItJhqgA4hi64MuohIoABdRERERE5YHIcmcQrQRUSOnwJ0ERERETlhPT1QLitAFxE5EQrQRUREROSETXUN9AoF6CIiQxSgi4iIiMgJO9EA3X36xyQiMt8oQBcRERGRE1YJ0Ds7p3a/lSuhUID+/ukfk4jIfKMAXURERERO2Ilk0EFl7iIioABdRERERKbBgQOQTsPy5VO7XyVA11roIiIK0EVERERkGhw4EMrbU1P8dqkMuojIEAXoIiIiInLCDhyYenk7KEAXEamlAF1ERERETpgCdBGRE6cAXURERERO2PEG6G1t0NioAF1EBBSgi4iIiMgJcj/+AN0sLLWmAF1ERAG6iIiIiJyg3l4oFo8vQIdQ5q4AXUREAbqIiIiInKDjXQO9YtUqLbMmIgIK0EVERETkBE1HgK4MuoiIAnQREREROUFdXeGys/P47q8AXUQkUIAuIiIiIiekUp5eWTJtqlatgr4+KBSmb0wiIvORAnQREREROSGV7PfKlcd3f62FLiISKEAXERERkRPS3Q1LlkBDw/HdvxLYK0AXkcVOAbqIiIiInJCDB4+/vB2UQRcRqcjUewAiIhPZsqOLzVt3srsnx9plLVx58Xo2bjjOTkQiIjLturuPv7wdhgJ0LbUmIoudMugiMqdt2dHFNbc9QFffIB3NWbr6BrnmtgfYsqOr3kMTEZHEiWbQK8G9AnQRWewUoIvInLZ5606yaaOlIYNZuMymjc1bd9Z7aCIikjjRDPqyZeHy8OHpGY+IyHylAF1E5rTdPTmas+lh25qzafb05Oo0IhERqeV+4hn0dBo6OuDQoWkblojIvKQAXUTmtLXLWsiXomHb8qWINcta6jQiERGpNTAQ1i8/kQw6wIoVyqCLiChAF5E57cqL11OKnFyxjHu4LEXOlRevr/fQqrbs6OLyG7Zx0fV3cvkN2zQ/XkQWlcq88RPJoAMsX64AXUREAbqIzGkbN3Ry7aXn0dneRG++RGd7E9deet6c6eKuJnYisthVlkY70Qz68uUqcRcRqdsya2bWBGwFGpNx3OruH6jXeERk7tq4oXPOBOQj1TaxA2hpyJArltm8deecHbOIyHSqZNCno8T9scdOfDwiIvNZPTPoBeDl7v4c4LnAq8zswjqOR0RkytTETkQWu0oGfTpK3JVBF5HFrm4Bugf9ydVs8s/rNR4RkeOhJnYisthNVwZ9+XI4cgSiaMJdRUQWrLrOQTeztJn9BOgCvuvuPxxln01mtt3Mth+snKIVEZkj5kMTOxGRmXTwIGQysHTpiR1nxYpweeTICQ9JRGTeqmuA7u6Ruz8XWAO8yMyeNco+N7j7Be5+waoTrZ0SkXlpLndJn+tN7ETGYmafMbMuM7u/ZttyM/uumT2aXC6rue29ZvaYmT1sZr9Ys/0FZvaz5LaPm5kl2xvN7CvJ9h+a2emz+gRl1nR3h+x5eOeP3/Ll4VJl7iKymNWtSVwtdz9iZluAVwH3T7C7iCwilS7p2bQN65J+LcyZIHguN7ETGcfngE8AX6jZdjXwX+5+nZldnVx/j5mdC7wBOA84BbjDzM529wj4FLAJ2AZ8k/BZfjtwBdDj7mea2RuA64Ffn5VnJtNqzZrT2bv3yXH2+DqwHrPnTOp4p556Gnv2PHHM9koGXUutichiVs8u7quAUhKcNwOvJHx4i4hUqUu6yMxw962jZLUvAzYmP38e2AK8J9l+i7sXgF1m9hih8u0JYIm73wVgZl8AXkcI0C8D/iI51q3AJ8zM3F39ZuaZvXuf5AMfGPtt+8xnIJ2Gt7xlcm/tBz84eqq9kkFXgC4ii1k9S9xPBv7bzH4K3E2Yg/4fdRyPiMxB6pIuMqtWu/t+gOSychbsVGB3zX57km2nJj+P3D7sPu5eBnqBFaM9qPrNzG+5HLS2nvhxVOIuIlLHDLq7/xR4Xr0eX0Tmh7XLWujqG6xm0EFd0kXqYLSUp4+zfbz7HLvR/QbgBoALLrhAGfZ5JpeD5uYTP45K3EVE6twkTkRkIuqSLjKrDpjZyQDJZaUj4x5gbc1+a4B9yfY1o2wfdh8zywBLAYVeC0wcQz4/PRn0pUtDozll0EVkMVOALiJzmrqki8yq24C3JD+/BfhGzfY3JJ3ZzwDOAn6UlMH3mdmFSff2N4+4T+VYrwfu1PzzhSefD5ct01DUlE7DsmXKoIvI4jYnuriLiIxHXdJFpp+Z3UxoCLfSzPYAHwCuA75qZlcATwG/CuDuD5jZV4EHgTLw9qSDO8DvETrCNxOaw92ebP9n4KakodxhQhd4WWBySTuQ6QjQIcxDV4AuIouZAnSRBWbLji42b93J7p4ca5e1cOXF6xXcisgx3P3yMW56xRj7fwj40CjbtwPPGmX7IEmALwvXwEC4nM4AXSXuIrKYqcRdZAGprBne1Tc4bM3wLTu6Jr6ziIjIFE13Bn3FCmXQRWRxUwZdZAGZaM1wZddFRGQ6TeccdAgZ9B07pudYIiLzkTLoIgvIeGuGK7suIiLTrRKgT8cya6AMuoiIAnSRBWTtshbypWjYtsqa4bXZdbNwmU0bm7furNNoRURkvsvnIZOBbHZ6jrd8OfT2Qrk8PccTEZlvFKCLLCDjrRk+XnZdRETkeOTz05c9hxCgA/T0TN8xRUTmEwXoIgvIeGuGj5ddFxEROR7THaCvWBEuVeYuIouVmsSJLDBjrRl+5cXruea2B8gVyzRn0+RLUTW7LiIicjxmKoOupdZEZLFSBl1kkRgvuy4iInI8ZipAVwZdRBYrZdBFFpGxsusiIiLHQyXuIiLTSxl0ERERETkuKnEXEZleCtBFREREZMpKpbAc2nQG6EuXQiqlDLqILF4K0EVERERkyvL5cDmdAXoqBcuWKUAXkcVLAbqIiIiITNlMBOgQytxV4i4ii5UCdBERERGZspkK0FesUAZdRBYvBegiIiIiMmW5XLhUBl1EZPooQBcRERGRKVMGXURk+ilAFxEREZEpqwToLS3Te9zlyxWgi8jipQBdRERERKYsn4d0GjKZ6T3u8uVw9GhYxk1EZLGZ5j+pIvPHlh1dbN66k909OdYua+HKi9ezcUNnvYclIiIyL+TzobzdbHqPu2JFuOzpgU59LIvIIqMAXRalLTu6uOa2B8imjY7mLF19g1xz2wNcC9MSpCv4FxGRha4SoE+35cvD5aFDCtBFZPFRgC6L0uatO8mmjZaG8J9AS0OGXLHM5q072bih84QC7MkG/wriRURkPsvnp3/+OQxl0DUPXUQWIwXoMq9MV1C7uydHR3OWo/kS3f0FilFMQzpFb654wtn1iYL/yvOYyQy+iIjITMvnh7Ld06lyTAXoIrIYqUmczBuVoLarb3BYULtlR9eUj7V2WQvd/QX29eYpR07ajGIU01eIuO72hyhFEU/3DvLwgT6e7h2kFEVs3rpzUsfe3ZOjOZsetq05m2ZPT656vTaINwuX2bRN+jFERETqLZ+HpqbpP25tibuIyGKjAF3mjekMaq+8eD09udAe1lLggGEsb83yePcA3X1FyrGTThnl2OnuK/JoV9+kjr12WQv5UjRsW74UsWbZUB3gZIJ4ERGRuWym5qCrxF1EFjMF6DJvTGdQu3FDJ+1NGbIpI4qdTMo4paOJFa2NlCMHg5QZhpEyA4NiOZ7Usa+8eD2lyOnuH+Txrj4e3N/Lnp48L1k/VAc4mSBeRERkriqVoFyemTnoS5aE5duUQReRxUgBuswb0x3UntXZzskdzWw4aQnrV7XR3pQlX4pIp8J6MXHsuDtx7AA0pCe3jszGDZ28/vmncnigRDFymjJplrVkufXevdVy/EoQnyuWcQ+Xpci58uL1x/Vc5qItO7q4/IZtXHT9nVx+w7bjmoogIiJzUz4fLmcig24Gy5Ypgy4ii5MCdJk3XrJ+OXt68jy0/yg7D/ZzsG/whILasYLkM1e1sqK1gUzaiNzJpI0VrQ2ctXrJpI778Tse4WN3PkahHBO709qQZlV707By/I0bOrn20vPobG+iN1+is72Jay89b8E0iJvOfgEiIjL3zGSADqHMXQG6iCxG6uIu88KWHV3ceu9elrdm6c2VGCxHlHPO2zeuO+6gduOGTq4lzG3f05NjTdIVHuCa2x7gpKUZmrNp8qVo0icCKsF5lGTdY4eu/iIAnUuahpXjb9zQuWAC8pEm08leRETmr5kO0JcvV4m7iCxOCtBlXqgEfEubm1jZFlrG5opl7tp5mKumcJzRlmm7edOFx+w3WuA+mcDyxh/sImXgBh46z+EO3QNF2puzi2aOeWUZu1pqgicisnDMRgZ9796ZObaIyFymAF3mhekI+Kay9vjxZrcHihGZFFgqRSmKQ3t4QiZ9oc0xH8/aZS109Q1WM+igJngiIgvJbGTQf/azmTm2iMhcpjnoMi9MR4O42Vh7vLUhTeyQThnZdApL+sqlUzbv55hPpenbYmiCJyKymKnEXURkZihAl7qabNA3HQHfbKw9/taLziB2KMcxZmEd9XTK+KOXnznvg/OpNH1b6E3wREQWu3w+LIWWzU6877HSmNm4/z760ffR3w9mDRPuu2bN6dP87ERE6kcl7lI3laCvFEX05krs781z71M9vH3jM7jqlWcP23eshm5TCfhmo+y6Mu4bf7CLgWJEa0Oat150xjHPZ745nqZvC7kJ3nQarS+CXjcRmetyuZA9t8mtQDpCxAc+4OPucffd8M1vwp/8SZG2tvGP9sEPHtcgRETmpLoF6Ga2FvgCcBIQAze4+8fqNR6ZfZu37qQURRzqL2EG2XSKKHY+ueVxzl/TMe688EpQ875v3D/poObKi9dzzW0PkCuWp9ydfSqueuXZ8z4gH0lN32bGVPoiiIjMJYODM1feDkPHzueZMEAXEVlI6lniXgb+xN3PAS4E3m5m59ZxPDLLdvfk6M2F4DxlhmGkzYhiH3de+PGusT1dZddTmYu9UExHDwA51mz0RRA5Hmb2TjN7wMzuN7ObzazJzJab2XfN7NHkclnN/u81s8fM7GEz+8Wa7S8ws58lt33c7PjyrTL35POzF6CLiCwmdcugu/t+YH/yc5+ZPQScCjxYrzHJ7Fq7rIX9vXmy6aHzRO7QmElVM7Ojlf9Optx6rLLhEy27XqwZz9mqPlhsVJkgc5GZnQpcBZzr7nkz+yrwBuBc4L/c/Tozuxq4GnhPcnL9DcB5wCnAHWZ2trtHwKeATcA24JvAq4DbZ/1JybTL56GjY+aOXwnQc/pzKCKLzJxoEmdmpwPPA344ym2bzGy7mW0/ePDgrI9NZs6VF68nkwpl7e5OHDsxTntThjXLWsbMlD9y4Oi4zd6ON8M+GRNlPBdqdl1N32aGKhNkDssAzWaWAVqAfcBlwOeT2z8PvC75+TLgFncvuPsu4DHgRWZ2MrDE3e9ydydMa3sdsiDMdAa9pWXocUREFpO6N4kzszbgX4B3uPvRkbe7+w3ADQAXXHDB+B1FZN7pbG9kd0+eMk5D2ljR2kBDJj1uprwUOflSNGqzty07urjqlh8zUCzTlEmzqr2R9qbshA3NJmu8jOdCz66r6dv0U2WCzEXuvtfM/g54CsgD33H375jZ6qT6DXffb2aVPwinEjLkFXuSbaXk55Hbj2FmmwiZdtatWzedT0dmSKVJ3ExRibuILFZ1DdDNLEsIzr/k7v9az7HIzKstO29rSHNooMiS5iynLW/mwNECpTimoznL1Zecw8YNnbzvG/ePGgw3ZFLVJddqg5qXrF+eBDsRmZRRjp19RwY5pQPaGjPDyoaPt3P2aJ3gu/sL5IoRV37xHsxgdXsT1mCT6nQui9t0rE4gAmBmz3L3+6fpWMsIWfEzgCPA18zsjePdZZRtPs72YzfqZPy8UipBuTyzAXpDA6RSCtBFZPGpZxd3A/4ZeMjd/75e45DZMTK7/NjBfsqR09qYYUlzA0uaG8gVyyxrbawGJ2Mti3ZWZ3s1w14b1FQy7o2ZFOXISaWMGOdgX4F0yqplwyeS6R6Z8ezuL3Cwv0hnexh/yox9veHbxJLm7IKZT6ylwGaOKhNkmnzazBqAzwFfdvcjJ3CsVwK73P0ggJn9K/BS4ICZnZxkz08GKnN49gBra+6/hlASvyf5eeR2mecGB8PlTAboZuH4CtBFZLGZ8hx0M0uZ2ZJpeOyXAW8CXm5mP0n+vXoajitz0Mi521HspAwO9hWq+4wMZq+8eH01U+7u1fL2SnB486YL+f57Xs7Nmy5k44ZOdvfkaM6mWdnWSOTOYCmiUI4ZKEYczZeqZcMn0jl75FzsXDGis72BlW1NNGbSmBkpjO7+8LwWwnzimZzTLyLTw90vAn6TEChvN7Mvm9kvHOfhngIuNLOW5GT6K4CHgNuAtyT7vAX4RvLzbcAbzKzRzM4AzgJ+lJTD95nZhclx3lxzH5nHKkHzTAboleMrQBeRxWZSGXQz+zLwNiAC7gGWmtnfu/vfHu8Du/sPGL38TRagkXO3G9IpSlFMMYqr20YGs1Mt/61k3M3A3cNvl0PKhtdUnmjn7NqM50XX31k91qr2RvYdGQScYjT8hMJ8Npmu+SJSf+7+qJm9D9gOfBx4XhIY/9lUppG5+w/N7FbgXsKSqD8mlJ+3AV81sysIQfyvJvs/kHR6fzDZ/+1JB3eA3yNk9ZsJ3dvVwX0BqHRWV4AuIjL9Jlvifq67HzWz3yQsk/IeQqB+3AG6LC4jy9VXtjWy90ieTMpw9zGbY02m/LdSfv1oVx99g2XiOCadMjKEEvdTljaTSTLkGzd0jlk6fzyZ7tpjtTdlOaUDnu4dxIHO9qYFUQqupcBE5j4zOx/4beA1wHeBX3L3e83sFOAuYEp9Xtz9A8AHRmwuELLpo+3/IeBDo2zfDjxrKo8tc18laG6Z4QKx5mbo7Z3ZxxARmWsmW+KeTRq6vQ74hruXGKPRi8hoRparZ9JGR0uWM1a2HveyXVt2dPGqj3yPK75wNz9+qofWhjTLW7OUYijH4TFOWdp8zFzw8UrnT/R5pVNG55ImNr/xBdXS+/lOS4GJzAufIGS8n+Pub3f3ewHcfR/wvrqOTBYclbiLiMycyWbQNwNPAPcBW83sNOCYJdFExjJaufr7X3PucQewtfOi02Y4sL+3wCkdTTRlUmAhS9/dX2Bfb550yjh9ecuYYzneTPdi6MKtpcBE5oVXA/lKabmZpYAmd8+5+031HZosNArQRURmzqQCdHf/OGE+W8WTZvbzMzMkWaims1t1ZV50FDtpM8yMUhzx1OFctQnd7sM5MmnDgHLkHBoosmVHV3Uc0zWWhd6FezGchBBZAO4gdF/vT663AN8hdF8XmVb5fFgCLZudeN8T0dw8tKRbpq4LA4uIzJ5x/9yZ2R9PcH8tjyZ1UZkX3ZAOS6pFcUw56TfXkIKY8K8cO83ZNKvaG0mnbMqNzbS8WLDQT0KILABN7l4JznH3fjPTPBSZEfl8CJ5thlv9Vua45/PQ3j6zjyUiMldMdD5Sfw6lamSw+pL1y7lr5+G6BK+V5mwr2xrZ15unHIeWCJb8fyoFaYNsOsX6VW1A6Ow+lcZmJ7JeuojILBsws+dX5p6b2QsAFQfLjMjnZ75BHAyV0CtAF5HFZNwA3d0/OFsDkbltZLC6q7ufHz1xmM72Bla0Nk4peJ2OrHRlXnQ2bZyytIknD4fvoY2ZFCctbeJgX4HBUkQpitjx9FEa0inamzKcsbJt0mPR8mIiMo+8A/iame1Lrp8M/Hr9hiMLWSWDPtNqA3QRkcVisuugNwFXAOcBTZXt7v47MzQumWNGBqt9g2VSBkfzZVa2NU06eB0vK115nMkE7iPnRS9pytDamGZlW/j1zBcjBoqh83gpckpRRL4U8RsvWjepsWzc0KnlxURk3nD3u81sA/BMQjHRjmTFFZFpl89DR8fMP44CdBFZjCbbcuMmYAfwi8C1wG8CD83UoGTuGRmsFqOYlIXLiskEr2Nlpa+7/SFypfiYYPn1e46MWUZfOy+6EmxXOo335IrVx7Tk/wy4/f6nueqVZ1fHUooiDvWXKUYxDekUS5ozM7JeuojILHghcDrhs/15Zoa7f6G+Q5KFKJ+Hk0+e+cepBOg5nRcXkUVksgH6me7+q2Z2mbt/3sy+DHx7Jgcms2uisvPaYLVvsEQUOyWHlEHfYIn2puykgtexstKPdvWzZlnzsMC9u3+QT255nDXLmiecAz4yo16KnbRBQyZd3SeKY3Z2D1SvP9rVR2+uRCplpFNGOXa6+4qUoj4glNG/69b72HskH7rFp4y2xgzvf825x/MSTxs1rhORkczsJuAZwE+AKNnsgAJ0mXazVeJe2yRORGSxmGyAXimTO2JmzwKeJpyllwVgMs3QKnO+u/sH6e4bnp3e25NnZXtENp2ecG3ssbLSEAL1Wr25cCJgsnPAazPqz3zf7bj7uGMplmMwSCVtaM0gNg/ba54fHhrM4cYMN6ydkBrXicgYLgDO9Yn+8ImcoHI5LH02GwF6NhuWc1OALiKLSWqS+91gZsuA9wO3AQ8C/2/GRiWzqrbs3CxcZtNhSbKKjRs6ufbS8xgoRDghmF7V1kA2naIUO/t7C3T3FXjXrfdx+Q3b2LKja9THuvLi9ZQiJ1cs4x4uS5GzfmVrNVCvKEQxjZmhX9G+wRL7j+T50ROHx30MgDNWtBDFzmApzD0fLEVEsXPGiqEMfzYdwu04dtydOOkE35Bs37x1J0uas5y1up1zTl7KWavbWdKcHfa6zLbJvFcisijdD5xU70HIwlcJlmcjQDcLj6MAXUQWk0ll0N39xuTH7wHjp0hl3plsM7SNGzpZ0pxl3fIWzIy+wRK9+TJpg8ihFMf05mKeONQ/LKu7ZUcX193+ELsOheN1tjeSTaXozZdYk5RoA8PmkOdLEZlU6LwOITjfd2QQx2nKpI7JHI8s+z7n5HYePtA/bPyxw6ufPTRp7uzVS9jV3U/f4NAc9PambLXT+2Rfl9ksOVfjOhEZw0rgQTP7EVCobHT3S+s3JFmIZjNAh1DmrgBdRBaTyXZxv2a07e5+7WjbZX6ZqBlabQB6NF+iHMWsag9LmZmFwNeATCpF7M7RfJmTlmaqWd133XofR3IlUkl9+P7eQZa1ZPnb1z9nWCBbO4d8zbIWLnvOKdx6715yxTJdR0NwDtDSkObp3kEK5Zirbvkxb73oDG69d++wsu97n+qhoyVDsezDGsDdtfMwVyWPVynbP2lppnpSoBR59YRBe2OGx7r6idxpSKdY1d5IOmXD5tnPdsm5GteJyBj+ot4DkMVhtgN0ZdBFZLGZ7Bz0gZqfm4DXoi7uC0YlUK3NXlcC1ZEBaBTHdCVz0AvliJQZDmRTQ/O4i1Fczepu3rqT/kKZtBmpyj7u9A0eO5e8dg55xflrOti8dSdPHMrRlEnR0pDmSL5ECiOdgoFimX/478fAHcxoSKdY2dZIOY4plo31q4bWPXf3YZnmkY3l1tRkv7fs6OJgf4Fy7KQMSlHMnp48y1qyw5rEzfZa6bW9AHpzJQpRTCaV4rLnnDLtjyUi84e7f8/MTgPOcvc7zKwFSE90P5GpqkeAfuTI7DyWiMhcMNkS9w/XXjezvyPMRZcFYLxA9fIbtg0LQCvrjA8UIozQ+RygHDtmjhk0pFPVrO7unlzogG5D7dXMoBzFkyrLrgTtl9+wja6+QZ7uHSRFCPZjDycGBssxBjRmjHLk7OvNkwYKNc3eYPRM82gnBQCuu/0h+gtlYvdQIWBG2qC/UOZ937iftVvDazTbJecbN3Ty+j1H+OSWx4lipymTpr0pw6337uX8NR1qFCeySJnZ7wKbgOWEbu6nAp8GXlHPccnCU1nybDYD9P37Z+exRETmgslm0EdqQXPRF5SxAtXRAtAVrY2UokEaMyl6ciUMJ/KQOU8bLG9trGbgN2/dSXd/AY9DYA4h2Z1JpaZUll3JHBfKMelUKKtPkubhmIQg2gyIwQ3SZqNWBUxky44uHj3YTzrJyHsyvz4CosiHlbK3NYRjz2bJ+V07Dw9bkg6Y0ay9iMwLbwdeBPwQwN0fNTP9QZBpV8mgt8zSzKrmZq2DLiKLy2TnoP8MqCzdkgZWEaYMywI0cs55FMfVzDmEALRYjlnV3khrY4aDfYVqB/aUGaevaBvWKK0yB92TbHvssKwlOyxYnqjRWiXLf9UtP2agWKYpE+aE7z2SJ5OCKIbYQwbfkxMGf/Tzz+CunYePqQqYyOatO8mmUsOCfo/CuFsb0tXu6bliGTOjN1+c1bXS1ShOREZRcPeiVZeNtAxDn9si0yaXg3Q6LIE2G5qbh5Z2m63HFBGpp8lm0F9b83MZOODu5RkYj9TZyDnn5WhoznlDOsWBvgKlyDFCmfqS5gbam8In5tF8kaePFtidzD3/6Z4j3LXzMO5ONm3JfG7jrFWtvOdVG6rB8mQarVUC+GzayKRCw7e2xgxpM8oOq9qyDBSjJItvPGNVK1e98uxqQ7ip2N2TY/WSRvb3FoipBP3BqvbG6n7N2TRPHx2c9bXSZ6pR3Gx2oxeRafc9M/szoNnMfgH4feDf6zwmWYByuZA9t5n+sEtUSunzeQXoIrI4jBugm9ny5Me+ETctMTPc/fDMDEvqZWTTs1XtIXN+JFeiGMVkUynWdDSxv3eQJw/nSVme5myaloY0PbkSmVQIsp841M8Pdx0ibYAZ6ZSxtDnL343o3D7aY45stFYbwJ+8tJnu/gKHB0qUIueMla0c7C/Q3pylc0lTtZT9Pa/acNyvQSUAPqUjdKovRjEpg0zKqicjYHglwUlLhybjzXS5+XhN/Y7XbHejF5FpdzVwBfAz4Ergm8CN495D5Djk87NX3g7DA/QlS2bvcUVE6mWiDPo9JNN7gXVAT/JzB/AUcMZMDk5m32jl0yvbGunJlTh9RSstDRn6Bksk1ephfnYU090fkTI4aWkzZkbPQKk6T7wxY3gcgvz3ff2nrF3RNixLO1HJ9mgnDVobMzSkU3S0NNDVN8jBvgINmRRndbYfk/mtZIYf7eqjWI7Jpo2zVy8ZM0NcCYCzaeOMla3kSxG9+RIGxwTF2bTRnB3eKHmmy83Ha+p3vGa7G72ITC93j4F/Sv6JzJhKBn22VB5LS62JyGIxboDu7mcAmNmngdvc/ZvJ9UuAV8788GS2jVU+DVQD0YN9BdKpUMpdjsPq5M7wDHNtB/XKPO5yOWZPb4HGhsyUGq2NFsCXo5gnDuU4fUULJy9trgbRR3LFYV3WAa657QFKUURvrgQG+RLs6u4fM0M8WgBcmVM+MijevHVnXdYlH6up3/HSvHaR+c3MdjHKnHN3V0NXmVa5HJx00uw9Xm0GXURkMZjsHPQXuvvbKlfc/XYz+8sZGpPU0UvWL68u4dWYSdHelKEhk2Z9kkluaciEed4pC3OuDcpR+E5YiJy+wdKwMvDaOWqVrPvILK2ZUYriMUu2R540OJovsbsnT+zwdO8gK9vCvPAjuRL9g2XO7GyrBv+tDWmyaeNQf5lUykiZEcdhHfaTlmbGzBCPFQCPtm26y83rYabmtYvIrLmg5ucm4FcJS66JTKtcbvaWWAMF6CKy+Ew2QO82s/cBXyScoX8jcGjGRiXH7UQafW3Z0cWt9+5leWuW3lyJwXJEOee8feM6zl/TUQ1EK2uPx6P0B37iUI7OtgayaaMYOamkq7p7+MVpTA/vKtOcTdObL/GXlz1rWHb6JeuXs3nrTt73jftpa0hzNF8CQiO6g/2l6v1zxYgnD+cwIGUQJWuWV4L/nd0DnNXZVj2pAOGkQTGKpyVDfLzl5lt2dHH9t3aws3sAgDNWtHD1JefUrZx8Jua1i8jscfeRn8kfNbMfANfUYzyyMMUxDA7WZw66lloTkcVisgH65cAHgK8n17cm22QOqJ1j3TdYZllLlpVtjVNu9FWZh7y0uam6rFp3/yA3/mAXS5qztFWWGGvMkC8VxzxOV3+Rtcua6c0VKUZOOYrJpFJk08bytoZh+1aytLUZ69qGZWmDJw7nKJRijg6Wh5XOw1A9pxOC89pf6EpJfr4U0ZBOUY5DR/Zy7MTu7Hi6j5aGNFt2dJ1QYDzVcvMtO7p496330ZMrkZwz4LGDA7zr1vtGbaI3G8Y60QBw+Q3b1NldZI4zs+fXXE0RMurtdRqOLFCDg6G3zGwG6NksZDIK0EVk8ZhUgJ50a/+jGR6LHIfaYDZXKBO7c2igSGMmzZLm7JQafY2ch9w3WKK7r4gD65a3JFnVmDdfeBof/a9H8TFW2DWgrTEsg7aze4B0KsXpK1p49bNP5tZ7906Ypa2cKChHzr7eQVJYyMgnwXk2ZdW578c8dk1Nfb4UccaKFnKlmCXNGbr7ipQ9JvKQbTegtTF93N3Kj7daYfPWnfQNlkknJfcAFjv9hfo2ZRt5omG0zu7vvvU+VrQ20F+MFLCLzC0frvm5DDwB/Fp9hiILVSVIns0Sd7NwQkABuogsFhMts/ZRd3+Hmf07ozefuXTGRiaTUtt9uxR7mBseQ3d/gSXN2VHLuMcKLEfOQz7YVwCDtBm7ugeqZeKf+t7jNGRSDJbi0YaEAw89HVbma0yy5rlSzPlrOjh/TceE5eCVEwW7egdIYaRSFhrRVdZfH6W23pLHHSzHPN7Vx9KWLNl0elhzt1LUR0+uhLnTnE2zqr2R9qapncSofQ2Pd1my3T05ynFMJp0aGr9BFPuUSu5net3ykZ3do9jpyZXoK5Q5c1WblmITmUPc/efrPQZZ+CrzwGczgw7Q2goDA7P7mCIi9TJRBv2m5PLvZnogcnxqs961ZdzFKATPIxt9bdnRxbtuvY/+Qpkodrr7C9XS6pHzkAfLoXt7FDt4inTSYK0YOavasmMG6BVGaBx3sK/IqvYGrrv9IZa1No4aUNYGm0fzJcpRHE4IJBlmr8l6R6E3XfUxgGrTOgeKkXN4oMTbN66rHr9yedH1d9LRnB2WaT+euegnsizZ2mUtdPcVcB9qoucensNkm7LNxrrlIysqDvYVwjz/2IfN89dSbCL1Z2Z/PN7t7v73szUWWbgqWezZDtCVQReRxWSiZdbuSS6/V9lmZsuAte7+0xkem0xCbdZ7VXsj+44MUvaYOIaf7e0FoFAsV+dZX3f7QxzJlUibkbah9cmvu/0hvvXOnxs2D7m1IUO+WCaVSpGqNFgDDKevENGYNgrRGHXuyc7mIaA71Fegq6/A6bEfUy7dPVAcNnc+imOe7i3gQKmmcGNpU4aBYkTWnJSFxw7Lu4VA18w4ZWlztbT/rp2HuWqc16vieLqVn8iyZFdevL46B90tPL/YoaMxO+mmbLOxbvnI16oYxRjhRFCFlmITmTMuAF4I3JZc/yVCv5jddRuRLDj1CtBbW6G7e3YfU0SkXiY1B93MtgCXJvv/BDhoZt9z93HP2MvMq816h3nfaQ7nQma70tn86b5iNUu+61COlDEUcBt47Ow6lDumZPpXX7Ccj935GEay1rmHf6vaGjiUK7GqrYEDRwujzgWvsnCfYgxNmRRR7OzqHmCwHBPFTm++RCZlw+bO5wrRqMfsK5Rpa0hzSkczA8WI1oY0A8WI3T15DKchbdWM9MjAsfLcHtjXS1+hDA7N2VS1FH6q3cpPJNDfuKGTv339c4Z1cT9z5dS6uJ/ICYLJlsZXTiTs7clTjmOiOPw+nby0sbrPiS7FNtNl+iKLyErg+e7eB2BmfwF8zd3fWtdRyYKiDLqIyMybbBf3pe5+1MzeCnzW3T9gZsqgzwEju2+XYyebImS9k2g1rmlAFiWBVimKsGR+uVnYZ2TJ9K337uWUpU0c7CsQxU5DOsWq9kbSKWNFWyMdLSFATyXZa3cftvTayCZyS5oy7DsyGE4KJDcWo3CfTDpk858+OnhMp/aK2GFpSwPLWhvpL4ZP6sFSRFM2RRyHevF9RwY5pSOUi7c2pLn8hm08cuAo/YUQ0OeLESkgBgZLMdGIUviKiQLHynrx5TimMT31QH+qnd9HOt4TBFMtjXcIlRBmZNNQipxCOcI9c8JLsc1Gmb7IIrIOqF1eowicXp+hyEKVy0E6HTqrz6aWFiiVwr/ZfmwRkdk22QA9Y2YnEzrC/vkMjkcmMFbgWDvHOlcsUzPFutqA7NEDR0MgneSn3SF2JwU0ZdPDSqbLkdPVN0g5ikmnUqxsa2BFa2M1KHv/azawcUMnl9+wrRooHujN09V/7PJr6ZRVA30zSJlVg3ezMAb3cKJgcIzgvGJf7yANmRQdzVkeO9hPOXJasymOlmKc0ERub0+OpS0NGFCKncFSTOyhwVk6ZTRk0sSxk0kbJy1tOqYUfqLAsbJe/LKWLH3J0m8j57zPtONdt3wqpfGbt+5kaXOWk5cOtevt7h9koBDRmy9Nes336RiLiEzoJuBHZvZ1wrm1Xwa+UN8hyUKTy4VgufY7xmxobQ2XAwPQ0TG7jy0iMttSE+8CwLXAt4HH3f1uM1sPPDpzw5LRVALHrr7BYYHjlh1d1X3WLmshk0oNy15XGpDlijGjfaam00ZzQ4rmbJqj+RKPHOjjycM5Bksx5RjiOKa7v8jTRwfpbG/i2kvPqwZQV168nlLk5IplBooRlenJlvxrSBtnrWrlLy97FqU4Do3cfGiJtHCSIJxAiMZat61GJaAzC8uulWOntxANPVegFMPh/iJ9hTLlyKvN5pxwMgCoNtIbrSy8NnCsNEPLpo3NW3cOu70pWWfdkuZ1t9//9DHv1+U3bOOi6+/k8hu2DXufTtTGDZ1ce+l5dLY30ZsvHfO+jGV3T666PnzFWKXxo+27orWRpc1Zvv+el3PzpgtPKJCeylhEZHzu/iHgt4Ee4Ajw2+7+13UdlCw4+fzsl7fD0GOqzF1EFoPJroP+NeBrNdd3Ar8yU4OS0U2Ucdyyo4sjuSLFKK52PU+njNihOZVioBiRSRkNaaMUO+6hwRoOPQMlDg2URn3cSty8srWBmzddOOy22hL7Jw7laEynaGsJzdyKUUw2naK7v8DGDZ2ctaqNJw7nKI3SWC5KMtoTKUXOzoP9tDSkh5+EGDlmQtn+vt58NWOfMqol+O6h2dloZeG7e3KkDXYe7KcYxTSkQwVBJXCs3L6/N1QEpFOhu/0jXf3VZnyzUb59PGXyUymNn66GetMxFtB8dZFJaAGOuvtnzWyVmZ3h7rvqPShZOHK52V0DvaI2gy4istBNKoNuZmeb2X+Z2f3J9fPN7H0zOzQZabyMYyUgLEYxa5c1k00bUVLCfuaqVpYmDcWKkVOOPcw9B8pxWFd8vMLysod9Hj8YPhlHZoYhZNJbG9IMlmIO9hcplEPWuhjF9BUituzo4upLzqGzvYmx4vDV7Y1sWN1GJmWj7tOQDtvLkdPdXyQ1ooy/IlttgGekki51MV7dpxzHxDjtTRlKkfOS9cuHPx939h4ZpByF16kcheutDeG1X7ushQNHh8r1K/9LG1x1y4+56Po7ueqWH9M/WOLp3kEePtDH072DlKKomoWvl9qKB/dwOVZp/FT2nemxTKZ6RGQxM7MPAO8B3ptsygJfrN+IZCGqlLjPNmXQRWQxmWyJ+z8RPvRLAMkSa2+YqUHJ6NYuayFfioZtOzRQ4NBAkd/+/N08dTjH3p48ZsYzT1rCM1a18sLTV3D1Jeew/+hgtbw9TgLuapn5JB+/FDsfv+ORYwKld916H+++9T5aG9Oh2zshI16OHcNY3pqtZvmvvfQ8SkkaO2UhmG5O5r8f7C9y9SXn0NGSJZtJJWXkKTKp0Dm8ozkbmpRFcXiMyjx2hjekKyfZeHdwwsmHFa0NZFIpljRlaM6mWdqU4YyVbbz++ady6717hz2fA32F0MSuUqefvHCVtdOvvHj9sHL9OCnPL0dOrhjR0Zylv1DmcK4UyutTRjl2uvuKPNrVN4V3fPpNpTT+eMvoZ2IsE007EBF+mbDaygCAu+8D2o/3YGbWYWa3mtkOM3vIzF5iZsvN7Ltm9mhyuaxm//ea2WNm9rCZ/WLN9heY2c+S2z5uNtuzl2U61avEXRl0EVlMJtskrsXdfzTic7U8A+ORcYxsDHZooMCBowXwoSB7sByzpyfPmmXQ1phhT08uBDepFA6kYq8GyFNlwI0/2MWq9sZhzeQODxSJHVob0iFLnYwndmfNsubqOCAEZZlUyEq7Q4RjNePZuKGTv3v9c6pd6dcsa+HRrj4a0in29g0eczKhspRcNKLcPY6dlW0N9ObDSupnrGzjulFKoi+/Ydsx0wZC6b+RSVm1xP2kJY30F8rVMZ61qo3HD/YPWwfeDBozKfoGy9VS+lIUTlKkU0ZsTnGCJnjHY6ql31MpjT/RbvPTdfwTWVZOZJEourubWVh8waz1BI/3MeBb7v56M2sglM//GfBf7n6dmV0NXA28x8zOJZy0Pw84BbjDzM529wj4FLAJ2AZ8E3gVcPsJjk3qII5DgF6PEvfGRkillEEXkcVhsgF6t5k9g8qqS2avB/af6IOb2WeA1wJd7v6sEz3efDGZgGqsfV6/5wg3/mAXA8Ukk+6QzaQoRXE1KCzHzhOHchhh+bKuvgJLmtL05iN80vnyYzlwdLBM32CZhrSxtDlLT7409LhJ0J1OGZm0EcVOe1OWXLFcnVe8ZUfXMU3iQhM3OKszfJ8cGbRdfsM2fry7hygOXdora6tXxjRySnsmWee9N1+ic8n4Wd/RAr/GTIrBcsTZq9qq23LFMp3tTdXrr372yXzszsfIpMMJgkI5ee4G+3rzw45XjGIySZf6hknMs5+KxbJU2UzPhxdZAL5qZpuBDjP7XeB3CNVvU2ZmS4CLgd8CcPciUDSzy4CNyW6fB7YQyuovA25x9wKwy8weA15kZk8AS9z9ruS4XwBehwL0eWlwMHz21iODbhYeVxl0EVkMJlvi/nZgM7DBzPYC7wDeNg2P/znC2fRFYzJzacfa5+N3PMKt9+5lVXsj55zUDkn5dhyH+d4jORBFoQy7J1emoznDcSbPjzluIXK6+ou4D3UxTyVzx6PYiWInm7Jj5hVv3rqTtsZjzwuZwdWXnDPq41XmKleH7sMqz4EQJK9qa+C05S00ZodK7ScqyR5t2kA2HRrrPbT/KDsP9nOwb/CYudF37TxMZ3sDDekUcaUhn0F/MSKFDZsfD6GaYEVrA2etXjLmWI7HyNLvyvJ4V37xnmnvHF9PMz0fXmQ+S8rGvwLcCvwL8EzgGnf/h+M85HrgIPBZM/uxmd2YZORXu/t+gOSy8sf1VGB3zf33JNtOTX4euX2057DJzLab2faDBw8e57BlJuWTc8/1CNAhlLkrgy4ii8Fku7jvBF6ZfECngDzw68CTJ/Lg7r7VzE4/kWPMN5NZ+3msfT69dWd1vnNDOkU2lSKKY8oOTZkUpXhEoJkyUqnQDC6sA14cNld7LKmaDHWl83nlbplUyIxXrpdjJ5MKEXrsTjptxOVwayly9vTkOWPF0Kf5IweOVrvJV45jQHM2NWYgvXFDJ2d3trHj6b5Qpp80ZysnGfVKJ/Uj+RItDRnWr2qrZrxrj1mpSni0q49iOSabNla1NXI0H7rXN2fTdPcX6CtELGvOMliKGCxHlHN+zBrnu3tyrGhtZGVbyKr3DZbY25MP0wfMSZkRu5NNh2C9HIV12EsHjnL5DdumrQN5bQXA0XyJfb356vu9kLLptasFVKY+qIu7SJCUtv+bu78A+O40HDIDPB/4Q3f/oZl9jFDOPpbRSoN8nO3HbnS/AbgB4IILLpiGU8ky3SrBcb0C9JYWBegisjiMG6AnZW5vJ5zx/gZwR3L9XcB9wJdmeoBmtokwf41169bN9MPNuEe7+sgVypTiEGSvam8cNkcbRi+7LkcxuWIUOpknTcei2ElRWUd8+NzmbMows2rpe8agNMFXnuaMsaK1gYMDJeLYSaWMKI6rwXp47BQQ1h+vWNHaQHNDmoN9BQbLMY3ZFG2NGZY0Z2nOpsmXomqgWFliLZtOkU1DKQrH6ivEnP8X3+atF53BVa88+5ixvedVG/ijr/yY3nyY3127nvnK1gaO5Ms4Tnd/gUzahmVXt+zo4rrbH+LRg/2kLZwYSKWMfClk+/sLZY7kS6QsvLad7Q2sbGuib7CUPKeIG3+wi/PXdFQDwpEl1+1NWVa2R3T1FSnHTlMmzYrGDP2FMoOliBhY1pJlZVvjtAbOtePo7i+ErvUGjenUqCd/ZstMLIk20/PhRea5bWb2Qne/exqOtQfY4+4/TK7fSgjQD5jZye6+38xOBrpq9l9bc/81wL5k+5pRtss8VO8AvbUV9u6tz2OLiMymiUrcbyKUyv0M+F3gO8CvAq9z98tmeGxAOKvu7he4+wWrVq2ajYecMVt2dNE3GILzSpC978gghwYKtDVmqkt9Hc2XODRQGHbfA0cLhER1+F8lmMykjZaGNGZDc5ybMqEhXCkaCq7Lk8hH5MvOnt7QwdySUvVlLQ2sXdaMEZZoy5eiYcE5wKGBIrlCmSjZblTGFQLUvT15njqc47c/fzcDhTJx7MTu1eC84uhgmb+/41Heecu91der8ppc/60dxLHTkLZhKZmVrQ2sXtrMKR1NZFPGYDke1g28Ml3gicM50hYC98jDGN2hN1/GzMimjDXLmimUQ1O4vsES+44MVisEcsVo2FSE0Uqus+k0f/TyM1ne0kAUO4cGCtWTAavbG1nV3jTtHchrx1GMYpwwF35VeyNQn0ZqWhJNpC5+nhCkP25mP006p//0eA7k7k8Du83smcmmVwAPArcBb0m2vYVw4p5k+xvMrNHMzgDOAn6UlMH3mdmFSRn+m2vuI/NMvQN0ZdBFZLGYqMR9vbs/G8DMbgS6gXXuXt+1ouapEGTGlOJQ8lwpDe/uL1KKnGIU09GcpRzFdPUVAVjR2ki+FFGKY1a0ZjmSL0NMEkDHlGNob0px9uolvGT9cr6w7Ul6c0Wm0iw8m5wsqITKxWgoO32ov0gqdWyn9IpQDh/mozdlU6zpaGbvkUG6+4rEcQjeK0G4O9X11sMa7MMPaElp/b/dt58zVob59pXmZ4919VOOnVM7QvvY3T05Yoeu/iKHciWaMimWNGc5Y2UbN2+6sHrMzVt3UixHFErxsLrKYjT0AqVTRin2auB84GiBdMqq65zHMTRmrBpUVzK5o5VcAzhPgiXLshmUy05jZvi5sOkKnIePI5S3n7S0ifamUIFRj0Zqk5nGISLTw8zWuftTwCXTfOg/BL6UdHDfCfw24aT+V83sCuApwgl73P0BM/sqIYgvA29POrgD/B6h30wzoTmcGsTNU5XguB5d3CEE6IUClMuQmWyLYxGReWiiP3Glyg/uHpnZLgXnExutvBfgka5QYp1NOWUP64o3ZlKUyjH9hTK9+RIN6RQr2xpZ1dbAQCGiVM5TjJwURl8hrLGdK0YMlmPiGLIpOHlpM119g3xh25OUoph0KkUpHjtCT1ZCqxpr2TVP5p6bw5plLdWguKIhncKAUhxjQNqMfb2D1fLzg/0FKsuJV+eOM3Y2v9qZ3eEjdzxKYybFSUtD1jlyJ2Xw9NHk+DXHiGJP5ovH/MaLlg875iMHjnJ0cPwVActRTGMmDcDq9kb2HMlTisN8+ziGGGdlW9MxQfVoJdeX37CNpc1ZTl469A3m0QN9HOgrsKS5obptOgPnyjgqmet0ynAP1Q71aKSmJdFEZtW/Ac939yfN7F/c/Vem46Du/hPgglFuesUY+38I+NAo27cDi2aVloUsl4N0GhoaJt53JlTWQs/lYMn09lsVEZlTJgrQn2NmR5OfDWhOrhuhL80J/Yk0s5sJS7asNLM9wAfc/Z9P5Jj1NtayVy3ZFNm04TGk0ykyhHnUcRwnndiddNKFe19vnlOWNjFQKNFXCAFoOmUUSzE9UYlTO5rYfTgfythjeHD/UVa0NoR1uh1WL2k6JpiuNdXuO5HDU4dzx9yvnHSPj5MgvByF5+B2bHYcGDaewQlS/J7s8+ShHKvaQrf0UhRTKMfHnGCo7L+6vYG7dh7mqprtlTnv4z3nKB4qCc+kU5y1qo19vYPkihGNGWNlWxNm8FhXPw7VJm/AMSdiRgtOVy9pZM+Rwer69TMVOM+VRmpaEk1kVtXO+tGyBjJj8vmQxR5l0ZhZUSmtV4AuIgvduAG6u6dn8sHd/fKZPH49jFXeu+tQjpOWNLKvdxBiiD2Up9eWlVvNKuVPHh5aSztlQORJR3Zn9+E8teFt7HCwv1j9ljZaMH2iRjueO5R8KAAuRjFmkEmlSJuPWhJfGXdLNkVTNs3hXOnYnUY8bld/MTTDG2MsDWnDCdMBRmZpGzIp8sWI8aQM2hoz1aW73v+acwGqJ1rKUcyenkHcnbTBj544xPYnD9OcSbFqSdOwEzFtDSEAb2nIcDRforu/QKEc05hJkU3WZp/JwHkuNFK78uL1XHPbAzN+QkJEgOF/EtX9XGZMLle/8nYYyqBrLXQRWeg0i2caVLqE7zqUo5As31UJKEPJeqgHy6RTnLK0mad7Qwn1SGN9s4qTudsZnIZsmlwpGnXn2f5mNloW231ofncyxX70QN2hr1AeNRs+mceqVYxC+fuhgQKnr2gbdttZne08caifA0cLwzL4KYNsKoXjNGXTowbOlWz0vU/1VOfgWypF1mCwFFOOIpbHXm38liuWq53zD/YNcmgg9BEwYGlzhlwp5i8ve9aoAfRMdD2vl7mSyRdZJJ5TU9nWPKLq7YQr3UQqcrn6NYiD4Rl0EZGFTAH6Cdqyo4t33XofR3KlkOmmZimxVCj13n04TyoFjx8Mp33NIG1DJd8TBaiV5mkxUCgPdWaf62KHJY0pitGxJe2D5Tg0mEuuTxSoT/SUY4f9vQWKpZiLrr+zGuRWsrlLmzP05IbmoocTB05HS5a/e/1zRg0eK9noi66/k8P9BcxC5/dScgLCgf1H8rSfFEram5NA/y8vexZX3fJjYg/Lra1qb6S9KTtmo7SxpkXM5/XL50ImX2QxmOlKN5GKfB466/hnXRl0EVksFKCfoM1bd9JfKCdzsYeHkaUYMl6ZYw6ZFKGs3SFi/AxzrcphY4f0RAvjzTFHCzGdbQ3E+XJY9q3mttqM9nSdcziUK0GuxN6ePNufPMzqtgaODJYYKIa56yQnOyKHNUsb+avXPXvMQLKS1T7YF8rUa0+qVBQi52i+xJLmbHWe9cYNnSxpzrJueUvo5J4Yq1Gaup6LiMhcV+8S9+bmkLBQgC4iC50C9BO0uycX1v92H7VsvVzTxTybTpNNw2A5GhZ0T0Vpomh+Dqk8767+4qw/dlgH3tnbW6AhbWRSYf34UzqaqtnszvamcYPzSlZ7aVOGrv7imCdSuvsLZNI2bJ71ZBqlVU4A/OiJwzRlQvf+Jc1D2Xh1PRcRkbnBqk3i6jYCC1l0BegistApQJ+E8eYHr13WEhqBlcYOnCu3FMpRyOIeZ3A+38yFpxea14Xl7Bw42FegvSk7YQBcyWpHsY/ZyC7MYzcGyzGd7U3Dfi8mapRWewKgMW0Uo5h9vaExYG02fqoW0lx2ERGZKzpwr2+ADgrQRWRxmGcF07OvEkh19Q0Omx+8ZUcXEAKxbMomFYzGSWl17bzrxaJez7Uyz71QDku05YoRfYOlCQPg3T05mrNpDvYViGIfdfwN6RRrlrfwotOXc/OmC4cFwhs3dHLtpefR2d5Eb75EZ3sT1156XnWf2rL2ziVNWPII3f2Fajf5qXY9n+h3VURE5PisBOofoLe1QX9/fccgIjLTlEGfwGjzgw/2DXLVLT9mSXOWtob0hHPIR5NJGStaGzjQV5jmEc9N9cqmV6YSVB7fgScP5WjKGA3p1LCGcrUBdqVEvZjMm7dRutiV4njcQHq8Rmm166W3N2U5pQO6jg6Omo2frJG/q+XI6eob5Mov3sPz1y1TNl1ERI7T3AnQu7vrOwYRkZmmAH0Cjxw4ymApphjFNKRTtDSkOZIvEbuzbnkLjxzoqy7zNVbJeiX7WntzW2OGrkUSnNeTc2yHeAfyZacnV2RlW+OoXdMrJeppM0p4NdCvbRTX2pCpZsWnWlo+co56e1OWdMrobG/i5k0XTvl5btnRxb1P9RC7D/s9NSB2XxCd4UVEpF5WAPUP0FtbQwbdPTlxLiKyAKnEfQwfv+MRzn3/tzg0UGKgGBFFTjlyuvuLRLGTMWNX9wDFJH0+3nzybDpFUzZdXYYN4Ei+NCfmaC8GlSC9IZ2iueZ96BssV9cwz6aNzVt3Vu9TKVE/Y2Vrtdt+2iCdMtIpY0VbAx9/w/MAuOSjW7niC9v58e4e0sakSsuvvHg9pcjJFcu4+3GXtcNQabslz7Pyexq+wBiNmfSoz3GiY15+wzYuuv5OLr9hm8rkRUQWtZBBr2cXdwgBehRBQfkNEVnAlEFnqLHWo119FMsxg6WoGnhXxEChsv51Mpc8Ko/Str1GJbAzA8dJjbIUm8yOxkyquuRZnHTWL0ZD799oTeMqJepbdnRx/bd2sLM7dKY5c2ULV19yDkCY8310kLSBx7Cvd5BTljZXg+GxstUbN3RyLaEs/YF9vQwUI+LYueqWH/PWi87gqleePennViltP2lpE/uODIal5IByHH7nVrU3jvkcK2orANobMxzsL7C0Obtg1mUXEZETMTcy6G1t4bK/H5qa6jsWEZGZsugD9Er2sRRF9OZKRO6T6q7u7hPOPa+sW+7uIeueShFP8vgyXDoVOqo3po1SPPXXMHIPQXQSnKdSIaNeMV7TuLHmkl9+w7bQ6d2ddLKMG3Fo9HbGytYJl0nbuKGTn+45wo+eOEzKIJMx8qWIj935GMCkg/TKfHYz45SO0Km+SAjSK8vKjfccazvKdzRneayrn3LstDZksAbTuuwiIoveyvC52VDfUVQC9IEBWLmyvmMREZkpiz5Av/5bO+g6OkghyYZPNu6bbGM4j8HT0NHSQHM2ze6e/PENdJEznGzaMDN8ilUIKSBlRimKaUynWNaSpXewTHtTBnc/Zgm0iYxcvzzF0Hw4s5CZn+wyaTf+YFcIzlPhZEHKoBzHfHrrTu7aeXhSc9pr57O3N2Vpb8pysG+QnlyJdMomfI4jm8tFHnoqdPcXtC67iIgAK2lpqf+879bWcKlO7iKykC3qOehbdnTx8IE+BsuhU/dMJLZjoJTMCVZwfvzKMcSRU459yu+TA5vf+AJedPoKVrQ18syTlvBHLz+TM1a2jboE2nhqlzKrrF8eOZSjOKmOCNn0yQb8A8VoWG+CMGAnV4wmvVzaaPPZGzJp3r7xGWMu81arsqRcRaWyoHYKwPGuy348NP9dRGSuWVH38nYYXuIuIrJQLeoM+vu/cb/KzeeRGFjZkuVgf3FK93NGL1O/iqFs+Pu+cT9rtx6bqR7Znf1Irjhs/fIw59tJp1KhQZs765e3cvUl50wq4G9tSJMvDQ/SS3HIpNcu7TdeiXntfPY9PTnW1GTcr5rE6zOyo/yq9kb29OTJpCfOvk+3keX2mv8uIjIXrJwTAXpzc8jiDwzUeyQiIjNn0QXolYDr/r1H6CtE9R5O1SjLbMsIDlMOzgFaarLDtSYKBmtvTxv8+KkeBssxjWnj5I7mYeuXFyLnRacvn/Ja42+96Aw+dudjlON42FJ9K1qzw/abqMR8vDXXJ1JZUi5XLNOcTZNOGctasqxobaA3XxoW8M+0keX2Mz3/farL44mILE5zI0BPpYaWWhMRWagWTYC+ZUcX193+EI909c/JrPkcHNKCEblz/l98m2zaOHv1kmoQNlEwWLk9ip39vYUwxxwoRM6+I4Oc0nHi65dXGsHd+INdDBQjWhvSrG5L09gw/D/NmSwxHy0D//7XnFuXQLXS8K7WTM1/V7ZeRGSyVtR9ibWK1lZl0EVkYVsUAXrli/jTvfk5GZzLzCqUYwrlmHQKdnX3V4OwSjB4NF+iu79AMYppSKfozYUsfeX2Xd0DmIVGc5lUKEF3PCyvNsn55uNlaq965dnDOrZXfl8rGe3ZKDE/kQz8dBpZbg8zd3JitrP1IiLzURzDXJmDDmEeugJ0EVnIFkWTuMoX8ZFrm8viYUAUw5FcqbpG+dplLXT3F9jXm6ccOWkLTd/6ChFbdnSxdlkL+VJEMYqrnWtTliKTgih2cqWYg30FXv/8U8cN6Goby02m6dvGDZ1ce+l5k2rwttCM1vBupk5OjGyOB+pWLyIy0pEjAOk5E6CrxF1EFroFmUEfma185MBRygrOF7XKu18ox9Ug7C8vexZv/cLdRElG3CycsVq5pIHNW3dW52anzYhjrzaBcw/rsjeljFXtjdx6717OX9MxZgB9PJnauZLRnm3jNbybbrOZrRcRma8OHQqXcyVAb2sLAXpleVMRkYVmwQXoo80rPZIvUbNilMxD09lErzYIGzblwcFSRkM6xZ6eXDVYvP5bO3ikq59sGjIxxGYYRueSpkkF27M5r3ohmK2TEyOb481mt3oRkfmiuztczqU56FEEhQI0NdV7NCIi02/BBegjs5X9g2UF5/NcJmU0Z1PT0nXfge7+Apc95xQ23bR9WICeMkibse9InuaGDBddfydrl7XwnldtAMLv1o+eOExj2mhvynKwr8DeI3myKaM3XxrzMZWpnZtmM1svIjJfVQL0uZRBhzAPXQG6iCxECypA//gdj7Bt5yGcUPaUNqOsrnDzXjkOa3FPVxa9b7DMp7Y8dkxPgsghSs7mxMUyy1pqOntfeh43b7qQy2/YxhOH+jnUXyJ2J3anFDn5cpGP3/HIsGZvFcrUzl2LdSqBiMhkzbUS99bWcNnfDytW1HcsIiIzYcE0ifv4HY/wsTsfqwZw7ig4X0DK8fQE55W1xvPl8Y8WxfDk4Tx7e/KUoojNW3cCIdg+PFAiimPKsVcz8AZ8csvjxzR+q/RDGCiUONhX4Omjg4uq6ZuIiMxvBw+Gy7kSoFcy6GoUJyIL1bzOoNc2g9vfO0isgFwmMNVfkUI5pruvSCnqA0LGta0xTU8uZNorZfHptFGK4mFz0Wv7IZy8tHlY5lzBuYiIzAchQB+koWFu1JPXlriLiCxE8zZA//gdj/DJLY9TjmMyZkQKzmWmGBTLcfWEUH8hwh2yKcikwzJdsTuN6RSPdvXxqo98j12HchSjmGzKOKWjGTMbtaHceOuji4iI1FtXF0AXZuvqPRQgNKszUwZdRBaueRmgb9nRxSe3PE7sDg4FBecyw9xj3n3rffQNlilHMQ6UYoCIVCqFOzQ1pOnNlziSK5GyMM2iGDl7evKsWQbtTdlh3dtHW3Hgmtse4FqYcF11BfUiIjIbQgb9IDA3AvRUKmTR+/rqPRIRkZkxrwL0nQcHuOj6OzmaL1GK4imXK4tMVWMmRUdLlqP5Mj25EumUkc2kIHKi2EOQHsc0pI2+QhncSVuKVMpIxRGxQxQ7B/sKtDdlh3VvP5710Y83qJ9JM3HCQCchRETmhkoGfS5pb1eALiIL17xqEleOYzqaswwUygrOZVosa8nQmEmxpClDS0O6+h9EQ9o4bXkzpy5rpiGTphTFpAxSyRroabPqMbJpI5UyypETeSi9A6r7OFAoR+SK5WHd23f35GjOpoeNZ6L10WuD+krZfDZt1SZ2tbbs6OLyG7Zx0fV3cvkN245pYDcdKicMuvoGh50wOJHHmoljiojI8ZmLAfqSJXD0aL1HISIyM+ZVgF4ox9y/7yiRgnM5ASlC1/W0hW7tm9/4Aj7+hufxnDUdnLKsmQ2r23jGqjZih872Jl7//FMpu1OMnEI5IoqdchyaxBmw4aQlnNXZTmMm/Ofkye9nJp0ik0oayaVSx3RvX7ushXxp+NruE62PPtmgfraC3KmcMKjnMUVEZOrca0vc5472dgXoIrJwzasSd5HpkLIQPEfuFMoxP91zhFvv3VstGw/d1mP+8rJnAXDNbQ+QMSh56AJfTNZKh5Bpr1i9pJHdPXkid7xa4mGsaM3yt69/zjEl2pNZH31kqXdbQ9ivUhYPowf1x1M+fzx29+ToaM4O2zZRFUA9jikiIlM3MAD5PMzFDHqhAMUiNDTUezQiItNLAbosOmUHophUyqrrly9rybK0OSwhUxvMAhTLoREccXzMsZbWBJKZdIpnrm7H3dl1KASTZ61q5ZJnncTmrTt53zfuP2Y+dWtDmp3dA8TuZFJGS0N6WKZ45Hzzo/lSdT34sYJ6mL0gd+2yFrr6Bic8YVDvY4qIyNQdrCbO51aA3t4eLvv6YMWK+o5FRGS6KUCXRanskI6dU5Y3s7snR99gmcZMie7+AsUoJmXGk4cGqCTL0ykLJfE10ytSwOFcieaGNJl0ilLkvP81G4YtoXb9t3bwsTsfI5s2Vrc3VkvNX1+TtT9pSSN7jwxSipy2xkx1n5Zs6pgsOEBDOkVHSwN7enKsGaOB2mwFuZOpApgLxxQRkanrqsblc6vEfcmScHn0qAJ0EVl45lWAfmpvF2/4ye082LmeHatOYzDbVO8hyRxkwJLGNL2FaNz9UimjvSlLYzpFvhixrzdPCiOKnKE8dRAlJetGaPpmhOZwxch56nCe5mya5oYUm7fu5Kd7jvDNn+3n0YP9xHEoqffY2Nc7yClLm8mmjRt/sItV7Y20NGTYebCfdMrAobu/yPpVbeSKZXYdynHSkkZ2HuynGMU0pFOsbGugWI65/R0Xj/vcKkFud/8gvbkShSgmk0px2XNOOf4XdhQbN3RyLaGkfrwTBvU+pohMnZmlge3AXnd/rZktB74CnA48Afyau/ck+74XuAKIgKvc/dvJ9hcAnwOagW8Cf+Tu6iQzTwwF6HM3gy4istDMqwB9MNPAc/c9zG/85FuceWg3e5Z08uDq9TzYuZ4HO8/gwdXr6W5dVu9hSp050FccPzgHKEXO0XyJpS1Z8kcLQFgp4NhCdhiaUV573cikwlJrxShmVWMDu7r7+dETh0lb6OIe4cQe9k9hdPcXOGNlKwPFiGVRzM6D/QzUjLUYQd9gibbGDHHs7D0yGLL3KaOcXD+rs23C57ZxQyev33OET255nCh2mjJp2psy3HrvXs5f0zGtwe7GDZ3THjzPxDFF5oVo4r9ds+iPgIeAJF/J1cB/uft1ZnZ1cv09ZnYu8AbgPOAU4A4zO9vdI+BTwCZgGyFAfxVw++w+DTlec7XEvTaDLiKy0MyrAP1Qawd/+up3AJCNSjzj0B7O7drJuQd28ns/vIdzD+xkMNsYgvXOJHBfvZ4nlp2M27xqWC8nKPahbPd49h7Jk04Z7iFgn4iP+LnslY7wRnd/EQgZ81LkNGZSpMxxD4F/QzpFMYrJlyIa0yn2Hhkc9TH2HRlkRVuWbLJ/9UGTy7GSTyMbyvUMFFizrHlYmftMNIoTWZSiKHTQ6u8fupyOnwuFej8zAMxsDfAa4EPAHyebLwM2Jj9/HtgCvCfZfou7F4BdZvYY8CIzewJY4u53Jcf8AvA6FKDPG3O1xL2hARobFaCLyMI0rwL0WqV0lh2dZ7Cj8wz+9VmvCBvdOfXowWrQfulD3+Pq732OZfmjPLzyNB5cvZ4HkqD94ZWnUcg21vdJyIxyIJ2CaLSUeKIcO+X4+KotK93cG9KG2dD1VJJmd4dMKgTZ7jBYDmup9+ZLLG/N8vTRQrV0vnbMjnN4oERbY5pVjQ109xerJe4ntTUOy7hXbNnRxbtuvY/+Qpkodrr7CxRKMeuWNw/bT93QZdGpBNLHGziPdXuhAK2t4V9bW/g33s+nnjrxPm1t0NwMqTlxQvmjwJ8C7TXbVrv7fgB3329mlTN9pxIy5BV7km2l5OeR249hZpsImXbWrVs3DcOX6dDVFX41Bwby9R7KMZYsUYm7iCxM8ypAtwl3MPYu7WTv0k6+e9aF1c1LBvs5p2sX53bt5IK9D/HmH/8n6w/v5amlJ/Hg6uHZ9sMtS2f0OcjsiuLJZdJPROyOYTSkw5fqEEwbMbXLrVX2DQF6IZPi1I4mnjocvvTUjrEhnaI5m+Ks1Uvo6htk/aqhkvZcsUxn+7G9F667/SGO5EqkzUib4Unifd+RPEuah9agUTd0mbOiCHK5yQfIk/15cBBaWiYXRLe1hUB6on1aW+dSID3tzOy1QJe732NmGydzl1G2+Tjbj93ofgNwA8AFF1ygOepzxMGD0NkJu3bVeyTHam9XgC4iC1NdA3QzexXwMSAN3Oju183E4xxtauOH657ND9c9u7qtoVzizEO7q9n2lz9+N+d27SKXbeSB1c8YNq/9qY6TVCI/D6UsfDucROX6xMcCmhvSlCPHgVKSLU9WbIOUc1JbI4VyzMH+IivbG2hIp9jdMxSAp1NGJm1EsdNfjGhrimlJjplKWXWptZOWNtHZ3jSlbua7DuVIWWh8B2AGGYdSzLD79+ZLNKRTXHT9nccs+SYyKXF8YhnpsX6uDaQnk5U++eTJBd0LOJCeQS8DLjWzVwNNwBIz+yJwwMxOTrLnJzM0MXkPsLbm/muAfcn2NaNsl3miqwtWrZqbAfqSJfD44/UehYjI9KtbgJ50h/0k8AuED/G7zew2d39wNh6/mMmGBnOr10MlbndnzdEuzj2wk/MO7OT/e+C/+fP//meWDvazY9UZ1Wz7A6ufwaMr11HINIz7GFJf0xWcG3DK0kb29hZCszcL/2IAh0zKeMaq0PjtjJVt/MaLlnPXzsM82tVHdbq4QcoMI1muLSljX9aS5WB/gagUdvO0cTRf4v2vOfeEu5mnzMimoLO9iT09OVob0hghw19ZV/2a2x7gWlCQvhDF8egZ6RMp6x4YgHw+BNKTLe2uBNLzp7R70XP39wLvBUgy6O9y9zea2d8CbwGuSy6/kdzlNuDLZvb3hCZxZwE/cvfIzPrM7ELgh8CbgX+YzeciJ6arC9asmXi/emhvD3+W4nGmsYmIzEf1/Db0IuAxd9/p7kXgFkKjmTGlc4do3PfjcCWOWLr9szTuvy9cj4os3f5ZGp6+HwArD4brXSHet+JAuH7w4XC90MfS7Z8l2/0oAKnBXpbe8zkOlPv5ztkv4WMvuIR3nbaGjW/4ABe97TN89IW/RFffbl6880f87Tc/yn0f/XW++4nf4KP/+pe89Uf/yv956E7W3fVp0n37Acj07mXp9s+S7j8Qrh95Klwf6A7Xe55g6fbPksodBiB76PFwfbA3XO9+lKXbP4sVQv1Ww8GHw/XiQLje9WC4Xg6Nxhqevp+l2z8LUWhU1rj/vnA9DvOVG/f9OFxPNO7dzpJ7P1+93rT7Ryz58U1D15+6i/affLl6vfnJ/6H9vluGrj/xfdp/9rWh6zu30Hb/v1Svtzx+J20PfH3o+mPfpe2h24auP/JtWnf8R/V668O30/rwUN+g1h3/Qcsj365eb3voNloe++7Q9Qe+Tsvjdw5dv/9faN65pXq9/Wdfo2HX94eu33cLzU/+z9D1n3yZpqfuql5f8uObaNr9o6Hr936exr3bq9f77/w0DcnvXhxFtN39WZr238fJSxt5/po21j30Jd7/vDI3b7qQ37nwFKKtm2noeojGbAorDrDk7s/C0w+FOeeDfSy957M0HnqUo4NlPNcbfjcPPU4qZfjAYf7xr97DPffcw8YNnfzN/z2Jcx6/hatf3MzGDZ089thjbNq0iQceeACAhx9+mOX3fg7r3R/K7Y/spnHbjXD0AGd2tnH1i5s55/FbaC0dYUlzlvThJzj87U/QUDhCNm185MvfZNOmTTz99NMA/O///i+bNm2iuzv8rm7dupVNmzZx5MgRAO688042bdpEf38/W3Z08dr3fJIXXvJr/Po/bmXLji6++c1wvHK5DMC///u/s2nTpupr+fWvf53f//3fr17/2te+xlVXXVW9fvPNN/POd76zev2mm27i3e9+d/X65z73Od773vdWr9944428//3vr17/9Kc/zQc/+MHq9U984hN86EMfql7/6Ec/yvXXX1+9/uEPf5gPf/jD1evXX389H/3oR6vXP/ShD/GJT3yiev2DH/wgn/70p6vX3//+93PjjTdWr7/3ve/lc5/7XPX6u9/9bm66aei/rXe+853cfPPN4Uoc867f+z1uu/HGkKJ64AGu/5Vf4fvXXgv/8R/wla/w+Z/7OX525ZVw3XXEf/7nfPfZz+apSy6BN76R6NJLeXDNGo6cey6cfz7xGWdwtKWFqLkZMhl89WqOrl9P/ud+Dt70JkrvfCcPXnEFhz72Mfjud8n98Id856ab2LN3L5x8Mj3r1vGZvXt5/GUvg3e/m/3vehfXnnUWD3384/Czn/H4//4vV15xBQ/88Idw4AAPf/ObbHrxi3n4s5+F73yHB/7yL9nU3Mxjf/In8Ld/y32//MtseuQRnnjVq+BNb+KedevY9LWvsWfdOjj/fH548CCb3v9+nu7rg1RqSr97AN/5znfYtGkTg4Ph7+BC+92bg64DfsHMHiWcXL8OwN0fAL4KPAh8C3h70sEd4PeAG4HHgMdRg7h5pVLiPhctWRJ6vSR/DkREFox6lrifCuyuub4HePHInWobxzS3tk88D30G9Da3s23NOdzf9xgDZ76Ccsc6mnv3cP49X+WM1jU88+hB/u+O73NO1y767/kfHjjpTHYsXcXO0mHuqM3GTwAANw1JREFUPfkgT7auqsOoZbJSduzPRqU7e5g3DqFsvHaueMqMbDrNFS87nVt/MvyY+3vzZFYaq9ub2D0wNEmuFMVkcVJmFGKIYx9qKgd47KRT4f4Qmr/91VfupnvXYb5/wzZOP6uXN5937Bz0tctbKDVl6Ces2W7AkuYM73nVBiiEk0b7j+ZZ3rmcYs39mrNpDvYXWD3lVw2+/8hB/uaOJ/B8iUzKOJhk5C9bvkDa6sYxDA7SPDBAexTBQw9BLse6J56gs7sb/vVfIZfjgnvuYfUjj8CBAzAwwGvvuIOVd90Fd9wBAwO89Sc/oeO22+AjH4Fcjmv27aPlxhvhd34HCgU+lE7Dl74Ey5ZBaytv6O6m5eGH4Yc/hNZWznz6adra2qCjA29u5khrK0ef8Qx48YuJslm+Uy7z0l/4BV708peTN+Pa//f/+KXLL+fnLrmE3qNH+dM//VPe+MY3cvHFF9Pb3c1H/+zP+K3f+i1e+tKXcvTpp7n1mmu44oorWPPiFzOwZw/bnniC5/z8z8MLXkDhiSfY8+1vU1y7Flavxvv6cKvHX2GpF3ffQujWjrsfAl4xxn4fInR8H7l9O/CsmRuhzBT3oRL3uUhroYvIQmVjLdk04w9s9qvAL7r7W5PrbwJe5O5/ONZ9lqx9pj/vDz/Nk4fnZhdq85g1vaFE/tyu8O+8AztpK+R4KJnPXlkC7tGVp1HMZOs9ZAE2rG7j6kvOYfPWnXT1DdLSkGHnwf7Q3d1DSXhjJjWs49FgOdTUNWdTtDSkOXv1kmHl5xddfycdzVnMjAO9eQ72F6v3X72kkZ5c6OTe1VckTs4AVG7PpI2lTRn+7lefy7tuvY+egWJ1LXWApc0ZPvbrzzumLL2yzNpY5fCX37Ct+vwqKk3nbt50IVM13vGuvHj9sCXfpn2uu3uYs5zLhbLrXO7Yn0/0tsFBaGoK5dyVf62to/98vLc1NamsW45hZve4+wX1Hke9XHDBBb59+/aJd5QZ1dsLHR3w4Q/Dn/yJ8YEPTN/3xQ9+8MSPt38/3HAD/NqvwVe/amMuQSoiMleN9Xlfzwz6WE1lxuQegpfGtFGYjsnF08wtxe6Ok9jdcRLffuZLq9uX5XqrXeRf8tTPuOLub3D6kf3sWnYKD3aewa7lp/JUx0nsXnoST3WcxKGWpSFdKzMuZXD1JedUg8d333ofuw/nKNb8fhlhObZM2kJDNzPSqZBgLcfO0cEyu7r7h83nXruspRq8rl7aTEtjhqd7B3HgjJVtlLv6WNHayIGjhaEHSYLwcuQcGihxxRe2H7MMG0Bvvsz139pxTMC7cUPnuEHwVJrOTcbunhwdzUMnmVJRmeVRge7Hu/jHnbtojYu8wEv47jzfuPsHrHheJ89e0RjmMOfzIRCu/DzWttGuV7Y1NEw9KF6xYvLBtOZEi8giVlkDfa6WuFcy6FoLXUQWmnoG6HcDZ5nZGcBe4A3Ab4x3h1M6mulsb6I3V6Q4UJrRpbOmU0/LUv739Ofyv6c/t7qtsVzkrO6nOPfATk47sp9fePSHrDvyNOuOPE02LrN76Wqe6jgpCdxX09W2nIOtyzjYuozu1g5yDc1jP6BMWiZlbN66E4Drv7WD7iTTXcsJgbi7EztYOgTn6VRY57xUjujqK4DDFV/YzrKWLKvaGunNh85vzdk06ZTRuaSJay89j40bOqvZ55QZkfuwhYcqP44WnFc81jXBpLtSKWSA8/lwOTjIxkKej5w2yH/+aBe9PUc5pREuObODc/93F9xZs2/t5TiB9L92HSFTHKSxVKTx/2/vzuPkKO87j39+VX1Mz6V7hJCEkUAgDAaEMYbYxjLgGHAC6ywOJmt77RyQXXsxm/WuSZbggLNZ/Fq/7A1Zr1fEdogdB5zgxCa28UFsWZBwi0uyxgiEjA7QSGg09/RR9ewfVd3Tc2lmpJmp6Z7v+/XqV3VXV3f/qmumq3/9PM/vKQxizlFIZxhIZSlmshQzDeTTWQrpLAOpNIXHGmH9iVHiW76UE+HFi4ffHnl/9aVcmdv3p3awRURk0soJ+lzt4t7UFP2GqgRdROpNYgm6c65kZh8Hfkg0zdpX40Iz42ppSFW64t754At84cGdNZOkj5RPZdh2wqlsO+HUUfe1DvayuusAq+OEfd3re7joledZ1tfJsr5O2no7Cc0qCfvBpoUcbF7E4dwCuhqa6W5oojtbXjZVbvdmcmqZHyEMHTsPdHPr/dvp6B486t9TuVHdM8PMkfI9SkFIqfpBoaOrr0ADAY35AosYpHR4kDc2pfjghhN4a/EAPPkK/7Xldb72ZDundvWSKuZJFwtkS9ElVyqQDQo0lApkitEyWyrSUMqTDYpkS9E6/vm2sZPqwcGou0k5oW1oqCzfnMvx5qrbdDaM2oZcDhYsOHqi3NjIrv19fPZnrxA0NOA1NtITWmVatxNaG7CqvzXnHF0DRR761CUzchxFRGR6HTwYLedqC7pZdKrq6ko6EhGR6ZXoPOjOue8D3z+Wx9542WmcvWoht3z7efZ1DVJPQ4+6G5rZ3tDM9uWnjL2BczQVBioJ+7K+Iyzr62RJ3xHecORVWgd7ac330TrYV7XsJVfM05NtoifbSH+6gYF0A/2Z7ND1dAMD6eh2PpWh6KcoeCmKfoqin45u+ymK3tDtoufjzMMZhOYRmhHi4cwIzeLl0O3QhsZyey7EXDR233MOw0XL8gUXbTNi2/JtPwzxXRAtw4BUGOC7MFrGt71ht6u2i7f1w4AGc7Smob8vj1f1POXHpIMimaBIOiiRjZeZqnWZ0uj7QzOKqQwFP0UplSbMZGlqztGXyXCgABk8PprO0p/K0O38qJXZT1NIZ+n30hQzWQYzOQ43tDCYypJPpcnHy8FUllI6zTduvGR0Yt3QEF3SM1/f4MIz4ROrT64a956rjD0fOTZ9oBiwalHjjMckIiLTY653cYeotmY8yYOISN1INEE/XhvXt/HwzUMFZTe3d/DJ+57lUG/hKI+qA2b0ZRvpyzaye/HKST/MDwNa4oS9sThIY3GQXDEfLwdpLAzSWMyTKw6SCYrkinnSQZF0WIqT0mhZXle+XU6scdHSqyTcYSXhLq8vXy8n764qaQ/jRN8xlPA7osSecsLP0DLwfALPI/B8ShYvPb+yDM2Lb3vD1gfmU/JTDMb3hZ5PfmET+3qLlMyjZOVtPULzKPhpiqk0RS9FKZ1hIP7RIu+no/v8VGXpMlmCdIqC8yoV39ef0MJAMaB7IBqWsSCXphSEHOjOUwxDVrRkacll2HWor1KELppv3RgoBsOOYbm6/GnLm+GtoyY9GKVcOG6mirWNN+59Ose6i4jI7DsQzRLL0qXJxnE0CxdCe3vSUYiITK+aTtBH2ri+jc9dcw53PLCD9gOaGHOkwPM5kmvlSK416VDmFDNYu7SJX77eH1VuH2sborFuzg1Nu+bFU7D5NpRQp32P0DnCMMT3jKzvYWY0ZlLs6xwAg+Zsile78piBb8ah/iKe7/Oxjadw39Z9FIOAQz0FQnOVYnQufr1MyqM5m+LmK86YcL82t3dw6/3bSfvGwlyajngatHIhu5mycX0bt8NRK8qPjHNGK76LiMiU7d8fJefZbNKRjG/hwqhMCjQlHImIyPSpqwQdhrfo3fngC3xx80vk4ymxRMbiHHTHc3mDY+SfixEl8WHV+nJy3ZTxWNSUpRAXihssBpVx7GHgCF3IroO9LG3OUgpDzIxXjwxQjOdNMwNCR9o3Htl1mNuvOpNNW3ZRDHoolEIyvrGspQHnHH2FYMJkt9qmLbtI+1bpat6YSdFfKLFpy64ZT4AnqihfltSPCCIicnT79sHKyXfSS8SiReVra5IMQ0RkWtVdgl7txstO48bLTqu00D25+3WKytVlDAd7C5Uu6Z7BkqY0rbkMXQNFDEZVd0+ZgYGZcftVZ3LLt59nrMZ3D0cpcOzvGqh0T6+eItC5eBq3IGRvZ/+kE9vJGDkNGkQV5fd29k/L80+HJH9EEBGR8e3fDyeemHQURzeUoGsIlYjUj7pO0MtGJj13PvgCX374ZfoKASnP1MIuAJXidSnPONxfJJvyWdqU4WBvvrJNdH/UnT3EkfGjUeMHevKV+6vz9FII2TQQEo2jj6sZVtfS9z3jQE+eDaujbxrT1eW7ei72srlWrK0WfkQQEZmP9u2DDRuSjuLo1IIuIvVoXiToI5Vb1ss2t3fw2R+0s+O1ngSjkrmgPOf5glyKg70FsmmfFQty9OUDikFIyjNCIOVH480LgeOGv3mKYrlVfESG7ojmM097RmM2+nfrGSiSDxxmkPaiVL1cRG06u3zfcPFabr1/O4d6B+nqL5IPQlKex9XnzJ0mkVr4EUFEZL4pFqMicXO9BT2Xg0wGCgW1oItI/fCSDmAu2Li+jQduupjdd7yXuz/yFi5au4SWrN6a+Sp0cKS/RLEU0phJYWacsCCa19s84/TlLbQ0pOjJj+6BMXK6PwPWn9DKioU51rW1sK6thVWLGzl5SSONaR9HNEd4NuVxy3e2ceO9T1MMgsrrNmZSpH1j05ZdU96PjevbuOa8lRzuK1IIHA0pn0WNae7buo/N7R3H9yZNkxsuXksxcPQXSjgXLVXxXUQkWQcOROezuT4G3SwqFKcWdBGpJ/OyBf1oqrvDl7sa7+3spynjc6ivUP9TuEmlOjsGuw72UghCMr5HU9qjOx9Uelrk0h69+WBUt/ZqaY9RSecn73uW3nyJQinEubhrfRDiG/QXAgYKAdmUT0tDOn6dY+/y/ciuw6xalBvWQj2XxnhPteK7iIjMvH37ouVcb0GHqJt7R4cSdBGpH0rQj2Ksgl13PvgC/+cnL1IYZzouqX1mBs7hHJQCh29RK3lf6Eh7cPoJLbS/1kNvPiBd6eo+uo6BZ9DamKGtpaGSdG5u78CAIAiHFZULHezvGsS3aLj6wZ58JUEfKAY0Z1Ncd9ejUx6XXgtjvKezMJ6IiBy//fujZS0k6OUWdFeeGUVEpMYpQZ+iscavb9qyi+37u+geLCUYmUwX54bmHy+XXg/ibNrzonnNsymPUiGgFDjMRv9Ys355MzdfccaoxHPTll205tL0FwK8wFUS+9A50ubFY9gdg6UA5xwDxYDugSIOKAThlMela4y3iIhMVbkFfa53cYdyobgmOjpg+fKkoxEROX5K0I/TyNa/ze0d3PLt59l7ZDDBqGSyfKNS+M3DwKJp0coN4sUgxIt/kk97VKqwL23O0ne4Px5DPvR8HlEL+wM3XTzm65VbtAtBiG9G3Fhf+eU/cI4lTRn6CwFdA0VWLWok7RnF0NGYSdE9UORQb558KeTGe5/mzg9sOGqSXi4U118okUv7DBSDo47xnq4K8kczG68hIiLHbv9+SKVg2bKkI5lYuZL7yy8rQReR+qBKaNNs4/o2Hr75Uv7gsnU0pv2kw5EJBA5WLMxx4oIcKd8oBY6QKBnPxN3XvbjFHDMyfvQvM143uijhHn/4w+pFjQwUAzK+h3OQ8rxo/LpFyb/vGZmUz50f2MBDn7qEe66/kN5CQC7t0z1QZH/XQNTt3oO+Qolb798+rODb5vYOrrvrUd7+2Z9w3V2PAnD7VWfS1tJA10CRtpYGbr/qzDET4nIF+Y6ewWEt9dNZUG42XkNERI7Pvn2wYgV4NfAtMeriDrumXktVRGROUgv6DBnZFb7a5V/YTPuBvlmOSMbje0Yu7ZPyjb2dAyxqTJNN+ezvGiAMQgI3VASuKWM453itK+oh4QHYUCt6yjP6CsGw569uMW7JpugaKNLSkOL1vqjgoB93oy8EjpRzNGWG/7BT7qZ+qDePh+F5RuigIeVVKryXx7ePOUXbVWdyz/UXTvg+bNqyi7Rvle7wjZnUtBeUm43XEBGR47N/f22MP4ehBP3llxMNQ0Rk2ihBT8AP/vPGStL29Cuvo6Hrs69ced2I5iLf2dELRGPNsykvHide4lBvoZKcL25M01cIeK1rAAdkPDDPq3SBd85RCt2w8d0jk+aBYlT1fWEuTSkIKcTzp+dLIcua0yxtznKoN88Nf/MULQ0p1rW1cNHaxdy3dR/5UhiNjY+7xC9ryQ4r+Ha8ye9sFJSrhaJ1IiLz3b59cMYZSUcxOZkMwKu8+OKKpEMREZkWStATMl7l6s3tHdzyj8+xtyufQFTzh6tavvx6NI3eYDEgHzp+eXiAZc0B/YWoKzrxOPWWhjT9xYCefEA25eF7Hn35kBBXGT/uezZsfPdYSTPAoqYsP/jP7wTgurserRRy6x4ocrC3QBA68r0FugcO8+QvD9OQirrClwJHQ9pjWUs2iqdQqvwgMF7yu7OjZ1IV4GejoJyK1omIzH3798OllyYdxVS0096uBF1E6kMNjC6aXzaub+PhP7wsGsOe0Rj22VAKQg73F8kHjvLQ8oO9BfoLAY5ourWmTNzlPXSUgpDBYkBnf4liEFIohQwWQ0qh48qzogo15XHgW1/ppDRiCraRLcZ7OvvJxfUKXuseJAiH4igEjmLgKASOZc0ZMKM1l6I5mxo1v3p5fHu11/vy9AyWJjXm+4aL11IMHP2FEs65Uc8/HWbjNURE5Nj19UFXV21UcB+ygx07hhdtFRGpVWpBn6Oqx7BXT+XWVwgIwqilNuVF83PL8Yl7mVemUisr3zpxYQMHe6Lx3yGO0EHKDI+ooBxA1jcWN2d4+MXXeejF11mQS7Mwl+ZQT569nQOkugYJgYzv0dKQYs3S5srrVLcqF0Ycz3JX/EIpZFlLAwB9+YCUV6Q5myLtOW75zjZWb2msdIWvrth+uK/Iosb0pLq9b1zfxu1Erf57O/tZNQMV1mfjNURE5NjV0hzoQ3bQ1QWvvRYVtxMRqWVK0GvAeN3hIUre/+M3nqK/qER9JjiiInKFIMQgrqBu0bjzOHvOpjxSnrG0uYGdB3rAYMWCHAALcmkO9OQJA0cmFT3Pwd4Cv3XB4sprVE+FNpGlzVm6Bop85uqzRhWEu2/rPq45byWP7DpcSX6P9BdY2pwd9hxHG/N9tL+16TIbryEiIsemluZAH9IOwI4dStBFpPapi3uN27i+jf/7797MRWuXsHpRjlULG2hM+/ieoQ7yRzfOTGnD7nfO0dbSgGdRUu55RsqPHhm6oW36CwHtr3UzWAopxk3yPYNFDvZGtQSiVvBojnXf4PvPv1p5nY3r2ypTocWzuOF7Vmk9h2jKNxgar109tt0sWqZ945Fdh7nn+gsrU7Sdtrx1WLf37oEiLx7spaMnz3V3ParpzUREZJhabUEHaG9POAwRkWmgFvQ6cLQWyTsffIEv/ewlBtTCPmWege953HP9hUPzd3cPEsZjxC3ephBEReJ8zygGjjB0dHQP0tlfpLrXfPlqIXC0H+jlzgdfqAxjKB/Dze0dfPK+Z+nNl3BQea0TFjQMG699y3e2TaoaenXrfCkI2Xckmh5u5cKGoWnY4tcXERGpzRb0fbS0RC3oIiK1Ti3ode7Gy05jx2euYPcd7+UPLltHNuXpoMfKCXPaG/6PYEDKAzNj7dImYKiVe83SJgIH5sHS5nRl/HrKDBcOtXx39OQBd9RW+s8/uJPLv/CzYa3YG9e38blrzmHD6kWcuKCB05c3c/oJLYQO2loauP2qM9m4vm3MgnBjVUOvbp1/rTtPyjNWLcrRmstUWt03bdl1LG+fiIjUod27YcECaGlJOpKpWb9eCbqI1Ae1oM8jN152GmevWsimLbvYeaCbIwNFPDMCN7pAWr3zDUJgQUOK1Yub6B4osvfIAEHocEDKi4q5fery9ZXHVLdyl4ucpbwivucROkfKN1rjqdLycfX1lG+VLu9j2XWob1Qr9mTGaFe3jJcLwo1XDb38fG//7E9YmEtjNvSzgeYgFxGRai+9BKecAjbROLA5Zv16+MlPko5CROT4KUGfZ6qTv+pEszmbomewyIHuPMU6S9azKY8gCCnFu2XAactb6OgZrBRza82lWQUc6s0zWArZcNKicauLl9dt2rKLjp48YRjieR6DpZD+QoBZuXu8YRhpn3GT9GLgKq3YU+lmfizV0DUHuYiITOSll2DDhqSjmLozzoCvfx26u6G1NeloRESOnRL0eWyy1bTvfPAFvrj5JQqlkFpM3cvJebnregj0FQKWNWcZKAaVhLU1lyblG20tDdxz/YXjPl95PHraN1obfA72hhAOjfF3LuoyHyXljtQErRDH2oo91WroU2l1FxGR+adUirq4X3NN0pFM3RlnRMv2drjggmRjERE5HhqOLBO68bLT2PTBN3Ph2iUsbc7QmKmt+vClql8ViiGkPY+0b5hF3c/7C6W4EntpUglrdQX1/kJIKh53DkOV4R3R2HaLX3+8JD3j26y1YlePR+8aKA4b0y4iIrJnT5Skn3JK0pFMXXWCLiJSy9SCLpMyVmttuYv89v1ddA+OnsN7QUOKk5Y08cJr3eSPMg47Cbm0X5lPfCrdxAH2dPZXKqgXgihBT/lGvhhSPTdayvfxvKhQ3JqlTRzszdM9UIyqvhNturg5c9QfBcrv8Z7OflZPMr6j0RzkIiIynpdeipa1mKCvXQvptArFiUjtU4Iux6w62bvzwRf48sMv01cIyPoeKQ+WtmRxzrG4OcOrXfkJn88s6h5uQMb3MIPB0vFND+cRdWkvP3/aMwLnRrVaO+BIf4E7HtjBLd/ZNiwZHpkkt2RTla7xGd8jXworxeXKybkZhM7hHJywIEtvvsTnrjmnUqCvEDgyKY+TlzSPm3RXd6VfmEvP2rRo0/2jgIiI1IZd8aQetZigp9Nw+unw3HNJRyIicnyUoMu0uPGy0ypzesPwAnQnL2nmurecxPeff5WXDvZRCqPq5qcsbeLKN63gkV2HKy3YnX15jgwU6RksUQhCvLhr+LHWrQuBlGcEoSPteTgcfty1/aK1iysJsG+ws6MXGD5H+DV7j3Df1n3DkuSugWKlK3tjxqOvEE13lvaiLvQAuOh1l7Vk8b1oXPtUW6+ru9JHr5Wiv1CackG5qUjqRwEREUneSy9BJlNrc6APOe88+PGPk45CROT4KEGXGTFWMlqdwA9bX3W9nCCesCBFLu1zqDfPwd4CvjmC42hMX9qcpicfUCw5GlIeffkiX374ZZqyPp757OkcIIxb7w9051m3vIX+QokvP/wyy1qyw5JkiFriFzVl2ds5EM2jHk+1lk0ZxVJIJuWxZmnTcRViq+5KXzbT06Il8aOAiIjMDS+9BGvWgF9bpWYqNmyAr30NXnsNTjgh6WhERI6NisTJnDKykNmapc184pJTOa2tJZ62bGo8g1OXNdGQTrFmSROLmzKsWNjAigU5+gsBHd159lYl546oW33PYJFc2qevEJBLD/+mUl5/z/UXsqwly+kntHLa8hbWx8vVi3M4mFQhts3tHVx316O8/bM/4bq7HmVze0flvtWLGhkoBsO2n+mCcns6+8fcX82VLjJ/mNlqM/upme0ws+1m9ol4/WIz+7GZ7YyXi6oe84dm9qKZ/cLM3lO1/s1m9nx8351mtTa79vxSngO9Vp13XrR8+ulk4xAROR5qQZc5Z7zW983tHXz2B+280NGLcw7c0PjykXwPwPjEJadWWu6vu+tRCkFYaR3OpqLu6eYcXjz+HaLx4wd78vie0ZTxh03FBsOT5LHmFk/5HuedtOioU7XB0PR1pTAk63sEYTisO3kS06JprnQRAUrAf3HObTWzFuApM/sx8BHgn51zd5jZzcDNwKfM7I3AB4AzgROBB83sNOdcAHwJuB54FPg+cDnwwKzvkUzIuShBf/vbk47k2J17brTcuhWuuCLRUEREjpla0KVmbFzfxgM3XcxXPnw+py9vwTyjIe2xrDlNQyr6U/YsujRlUsOScxjdOry0OQtErea+WWWOd99gsBQlw7/79jVHnYrthovXHtNUbZvbO/ji5pcInSPtewQOXu8tUgwCNm3ZVdnf2Z4W7Vj3R0Tqh3PuVefc1vh6D7ADWAlcDfx1vNlfA/8mvn41cK9zLu+cexl4EbjAzFYArc65R5xzDvha1WNkjjl0CHp6arsFvbUVTj1VLegiUtvUgi41p9zCXl2I7tS21gmrjY9sHW7NpUl7EDjAohZ1nCNw0JTxK8nw2asWjjsV28b1bdwOlfubsynSnosqwW8ZvwL6pi27CEIXz6FuUdV3HF39RfZ6Q93JZ3tatJH7M9mp50DV30XqkZmdDGwAHgOWO+dehSiJN7PyP/hKohbysr3xumJ8feR6mYNqeYq1ahs2wJNPJh2FiMixU4IuNWuqyetYXcYXNGYwomS9uht5dUv1RK9T/YPBZCug7+nsJ5vyKAWO8ohMM8gHYeLdyY/lR4HyvheDgK7+Iq92DbD1lU4+tvGUcYsDisjcZmbNwLeAm5xz3UcZPj7WHe4o68d6reuJusJz0kknTT1YOW71kqCfdx78/d9DZycsWjTx9iIic426uMu8MVaX8c9dcw7/65pzxuxGfrQCbmOproBuFi3TvlW6rFdbvaiRloYUIY4wdDjn4hZ1rya7k2/asotiEPB6b5HAQdqPqtp/cfNLE75vIjL3mFmaKDn/hnPuH+LVB+Ju68TL8j/3XmB11cNXAfvj9avGWD+Kc+4u59z5zrnzly1bNn07IpNWTtDXrEk2juNVLhT3zDOJhiEicszUgi7zynitwyPXHct84Hs6+/ENdh3spRCEZHyPpc2ZMSugl1vzlzRl6BkskS+F+J7xsY2n1GS38D2d/XT1FzEDL25l8w1KodMUbSI1Jq60/hVgh3Pu81V33Q/8e+COePmdqvV/a2afJyoStw543DkXmFmPmV1I1EX+w8BfzNJuyBQ9/3w0fjuXSzqS47NhQ7R8+ml417uSjUVE5FioBV1kDFNpDS9rzvjsOzJIKXD4ZpQCx74jgzRlRk8oW27NX7O0mcVNGd5y8mI2ffDNNdsdfPWiRvJBSHUPWOeicf2aok2k5rwN+BBwiZk9E1+uJErM321mO4F3x7dxzm0H/g74OfAD4GNxBXeA/wB8mahw3Euogvuc9fTTQ1XQa9myZbBqFTzxRNKRiIgcm0Ra0M3s/cCfAGcAFzjnVM5D5pQ9nf0szKWHrZtoPvDK+ExjaOSlq1o/wmwXgJtJN1y8lq2vdBKEDj+esi7E0dKQTnxMvYhMjXPuYcYePw5w6TiP+R/A/xhj/ZPAWdMXncyE7u6oi/tHP5p0JNPj7W+Hhx6KzkXjl04QEZmbkmpB3wb8BrAlodcXOarVixoZKAbD1k00H3hPvsTKhQ2kPKtUaF+5sIHefGmmw03cxvVtfGzjKXhmlEJHyjeWNGXIpPyaHFMvIjKfPPdctCx3D691F18M+/bByy8nHYmIyNQlkqA753Y4536RxGuLTMaxzAe+elEjKd9j7bJm1p/QytplzaR8b960IN942Wls+uCbecvJi1nclGHN0uYZn7ddRESOX3ne8Hro4g5Rgg7ws58lG4eIyLFQkTiRMRzLfOBjTeM2UVJfayaa67yeuu2LiMwXzzwTjd1esSLpSKbHG98IS5fCli31021fROaPGUvQzexB4IQx7vrvzrnvjLF+vOfR3KiSiKkmm8eS1NeSY6lsLyIic98zz0Td2+tlvLYZvOMdUYIuIlJrZixBd85dNk3PcxdwF8D555/vpuM5RWZKPbcgV1e2B2jMpOgvlDSNmohIDSsWYds2uOmmpCOZXu98J/zjP8LevVFVdxGRWqFp1kRkUvZ09pNLD58ybqLK9iIiMrft2AGFQv2MPy8rj0NXK7qI1JpEEnQze5+Z7QUuAr5nZj9MIg4RmbxjqWwvIiJz2zPPRMt6qeBedvbZsGABbN6cdCQiIlOTVBX3f3TOrXLOZZ1zy51z70kiDhGZvGOpbC8iInPb449DYyOsW5d0JNPL9+FXfxXuvx+CYOLtRUTmCnVxF5FJ2bi+jduvOpO2lga6Boq0tTRoGjURkRr3ox/Bxo1RQltvrrkGDhyAhx9OOhIRkcnTNGsiMmn1XARPRKRerFp1Mvv2/XISW54MvMzOnTdi9hczHNXsu/JKyOXgvvuionEiIrVACbqIiIhIHdm375d8+tMTT3zz5JPwve/Bxz52J0uX3jnudrfdVpvzrzU3wxVXwLe+BX/+5+Cp36iI1AAl6CIyYza3d7Bpyy72dPazus7mhRcRqXUvvRQVUluyJOlIjpePjTuJ+7XAvfj+O4DJ9XVfufIN7N27e5piExGZGiXoIjIjNrd3cOv920n7xsJcmo6eQW69fzu3g5J0EZGEBQHs2gVvehOMm9vWjGDcHgP5PHzuc7Bhw0NceeXknq1WewyISH1QZx8RmRGbtuwi7RuNmRRm0TLtG5u27Eo6NBGReW/v3mj+81NOSTqSmZXNwhlnwLPPwuBg0tGIiExMCbqIzIg9nf3k0sPLAufSPns7+xOKSEREynbujFrO16xJOpKZd9FF0Y8RTz6ZdCQiIhNTgi4iM2L1okYGisMnnx0oBqxa1JhQRCIiAhCG8PzzsHYtNDQkHc3MW7Ei2tfHHoNSKeloRESOTgm6iMyIGy5eSzFw9BdKOBcti4HjhovXJh2aiMi8tns3dHfDuecmHcnsedvboLc36uouIjKXKUEXkRmxcX0bt191Jm0tDXQNFGlraeD2q85UgTgRkYQ980zUcr5+fdKRzJ41a+DEE2HLlqi7u4jIXKUq7iIyYzaub1NCLiIyhwwOwo4dUet5ah59CzSD97wH/uqv4KGH4NJLk45IRGRsakEXERERmSe2bYvGYc+n7u1lJ50EZ58N//qv8PrrSUcjIjI2JegiIiIi84BzUSXztraou/d89O53QzoN//RP0VzwIiJzjRJ0ERGROWBzewfX3fUo6WUnvynpWKQ+vfIKHDgAF1wQdfmej5qb4cor4Ze/hB/9KOloRERGm0ejj0REROamze0d3Hr/dtK+gQs1EZTMiMceg1wu6uY9n519Nrz6Kjz6KCxfDuedl3REIiJDlKCLiIgkbNOWXaR9ozGj07LMjCNHoL0dfuVXoi7e89273w0HD0Zd3YtFeOtbk45IRCSiLu4iIiIJ29PZTy7tJx2G1LHHH4+Wb3lLsnHMFZ4H114bTTX3gx9E3d3DMOmoRESUoIuIiCRu9aJGBoqqWCUzo7cXnngCzjoLFixIOpq5I52G978/+tHikUfgb/8W+vuTjkpE5jsl6CIiIgm74eK1FANHf0HDz2X6PfRQVLF848akI5l7PC8qGvfrvw67d8Nf/iXAOQlHJSLzmRJ0ERGRhG1c38btV51JW0sDmKeB6DJturrgqaeiec8XL046mrnrvPPgIx8pT732r3z96wkHJCLzlhJ0ERGROWDj+jbuuf5Cigd3P590LFIfnIMHH4yuv/OdycZSC1atguuvB3iSD38YrrsOOjuTjkpE5hsl6CIiIiJ16KmnYNs2uPhijT2frOZmgEv4zGfgvvtg3Tr40z+Fw4eTjkxE5gt1oxMRERGpM/v2RdXJTz0V3vGOpKOpNQG33ALvfS/88R9Hl1tvjSq+n3lmNFRg0aLhlxNPjLrJ53JJxy4itU4JuoiIiEhduZyvfz1qDX7f+8As6XhqjY8Ne9POxrmr2bHjLezYsQ5YFF8yIx5XAB4Bvg58E+it3LNy5RvYu3f3jEYtIvVBCbqIiIhIHSiV4LOfBfguixZF83w3NiYdVS0K+PSn3VG3cA6KRRgcjC6dnbBnT4Zf/OKdHDr0TjKZL7NhA1x4ISxcCLfdpl9JRGRylKCLiIiI1Lht2+C3fzua7xz+jo9+9DoyIxt4ZdqYQSYTXVpboa0NTj8dLr0U9u6NjsPjj8Njj8HatQAfYft2OO20aP51EZHxKEEXERERqVE7d8Kf/Rl87WvR2OhvfhOuvfa3yGSuSzq0eckMVq+OLpdeClu3wnPPAfwVZ50VJfRnngnnnANnnx0tzzkHliwZeg7noK8vapXv6Yl6QbS2RpeUvrmL1D39m4uIiMiMMbPLgT8HfODLzrk7Eg6pJpVKsHt3lJDv3AkvvACbN8P27ZDNwk03wR/9UZToXXttwsEKEFXOf9e7YONGuP32N/H1rz/Pc8/Bs8/CAw/A3XcPbbtsWZR8F4tw5Eh0vMfS2Bg9b2vr+MuGhuhvYvFiWL586NLWptZ7kVqgBF1ERERmhJn5wBeBdwN7gSfM7H7n3M9n4/WDILqU632ZjX99OoRhlGCVStGl+vrI2+PdVyxCVxccOhQl5C+8ECXkL788PGlraYHzz4ff+z245hpYuXJ69kGmX/T3tYMPfWjkH1obcDZwDgcPrgMMCIAjQGd86QYagVZgAf39rfT3L+DVVxcCLZX1w5dHczh+zoGqSw+5XJGrr/51BgdhYGBobP3AAPh+VHCwfMnloh8AMpmhZSYTra/ernzJZof/refzUc+Anh7o7Y1eJ50e/5LJjL3e96PnKseaz0d76HlDF+ei/8swjO5raICmprEv09U7IQyHPnuqP4M8L4q5fPE02fWscW709WNZl07PznFTgi4iIiIz5QLgRefcLgAzuxe4GpiVBP2OO+CWWya//VgJ+1jJ+9G+zE2vPmDnmJeeng5++lP46U+j1nOZ6yYuPDcVt91mYz6fc1FCWCpBf3/UVb63N1pG1xdTKCwe9sNQPg/797/Avfe+QJSwD1YtBwGP6MeA5njZQFTBPhsvy9drWznph8knbyPvL/8QMFnlRL2c9I18rcl8roz3A+NcW192PAnyZNbNzGdxZOtW2LBh5p6/zNxM7sU0M7ODwC+TjmOaLQUOJR3ELNL+1jftb32bb/sLyezzG5xzy2b5NWeEmV0DXO6c+9349oeAtzrnPj5iu+uB6+ObpwO/mNVAZ958/N+ZiN6T0fSejKb3ZDS9J6PV6nsy5vm+plrQ6+ULSzUze9I5d37SccwW7W990/7Wt/m2vzA/93majdWWMqplwDl3F3DXzIeTDP0djab3ZDS9J6PpPRlN78lo9faeaPSDiIiIzJS9wOqq26uA/QnFIiIiMucpQRcREZGZ8gSwzszWmFkG+ABwf8IxiYiIzFk11cW9TtVtl75xaH/rm/a3vs23/YX5uc/TxjlXMrOPAz8kmmbtq8657QmHlQT9HY2m92Q0vSej6T0ZTe/JaHX1ntRUkTgRERERERGReqUu7iIiIiIiIiJzgBJ0ERERERERkTlACfosMLPLzewXZvaimd08xv0bzazLzJ6JL7cmEed0MbOvmlmHmW0b534zszvj9+M5MztvtmOcTpPY33o7vqvN7KdmtsPMtpvZJ8bYpm6O8ST3t26OsZk1mNnjZvZsvL+3jbFNPR3fyexv3RxfSYaZvT/++wrNrG6mAjoWE30nmo8m+h4x30zmvDvfTOZcNV+ZmW9mT5vZd5OOZbqoSNwMMzMf+CLwbqLpZp4ws/udcz8fselDzrlfm/UAZ8bdwP8BvjbO/VcA6+LLW4EvxctadTdH31+or+NbAv6Lc26rmbUAT5nZj0f8TdfTMZ7M/kL9HOM8cIlzrtfM0sDDZvaAc+7Rqm3q6fhOZn+hfo6vJGMb8BvApqQDSdIUvhPNN3cz8feI+WSy5935ZLLnqvnoE8AOoDXpQKaLWtBn3gXAi865Xc65AnAvcHXCMc0o59wW4PBRNrka+JqLPAosNLMVsxPd9JvE/tYV59yrzrmt8fUeog/FlSM2q5tjPMn9rRvxMeuNb6bjy8hqovV0fCezvyLHxTm3wzn3i6TjmAPm3XeiyZhv3yMmMt/Ou5Ohc9XYzGwV8F7gy0nHMp2UoM+8lcCeqtt7GftD5qK428oDZnbm7ISWmMm+J/WkLo+vmZ0MbAAeG3FXXR7jo+wv1NExjruLPQN0AD92ztX18Z3E/kIdHV+RBNXVZ4fMvAnOu/PKJM9V883/Bv4bECYcx7RSgj7zbIx1I3/x2gq8wTl3DvAXwLdnOqiETeY9qSd1eXzNrBn4FnCTc6575N1jPKSmj/EE+1tXx9g5FzjnzgVWAReY2VkjNqmr4zuJ/a2r4yszw8weNLNtY1zmfQtxlbr67JCZNcF5d96ZxLlqXjGzXwM6nHNPJR3LdFOCPvP2Aqurbq8C9ldv4JzrLndbcc59H0ib2dLZC3HWTfie1JN6PL7x+KdvAd9wzv3DGJvU1TGeaH/r8RgDOOeOAJuBy0fcVVfHt2y8/a3X4yvTyzl3mXPurDEu30k6tjmkLj87ZPpN4nvGvHWUc/N88zbgKjPbTTRc5hIz+5tkQ5oeStBn3hPAOjNbY2YZ4APA/dUbmNkJZmbx9QuIjsvrsx7p7Lkf+HBcCfpCoMs592rSQc2Ueju+8b58BdjhnPv8OJvVzTGezP7W0zE2s2VmtjC+ngMuA9pHbFZPx3fC/a2n4yuSsAm/E4lM8nvGvDLJc/O84pz7Q+fcKufcyUSfJT9xzn0w4bCmhaq4zzDnXMnMPg78EPCBrzrntpvZ78f3/z/gGuA/mFkJGAA+4Jyr2S5fZnYPsBFYamZ7gU8TFbMo7+/3gSuBF4F+4KPJRDo9JrG/dXV8iX6x/BDwfDwWCuCPgJOgLo/xZPa3no7xCuCv42rLHvB3zrnvjvjMqqfjO5n9rafjKwkws/cRDY9YBnzPzJ5xzr0n4bBm3XjfiRIOK3FjfY9wzn0l2agSNeZ5N+7BNF+Nea5KOCaZIabvGCIiIiIiIiLJUxd3ERERERERkTlACbqIiIiIiIjIHKAEXURERERERGQOUIIuIiIiIiIiMgcoQRcREZkmZvZVM+sws22T3P43zeznZrbdzP52puMTERGR4zPT53ol6CI1wMwCM3um6nKymf1rfN/JZvZbVduea2ZXHsNrbDaz86ch1ml5HpEadTdw+WQ2NLN1wB8Cb3POnQncNHNhiUitq/ousM3M/qk8L/YUn+N8M7tznPt2m9nSY4ztT8zsk8fyWJEadDczeK5Xgi5SGwacc+dWXXY7534lvu9k4Leqtj2XaI5qEZllzrktwOHqdWZ2ipn9wMyeMrOHzGx9fNfvAV90znXGj+2Y5XBFpLaUvwucRfQ587GpPoFz7knn3I3TH5rI/DHT53ol6CI1ysx646t3AO+If1X/FHA7cG18+1oza4q74jxhZk+b2dXx43Nmdq+ZPWdm3wRyY7zGFWb2d1W3N5rZP8XXv2RmT8bddW6bIEbM7Bozuzu+vszMvhXH9ISZvS1e/86qXgJPm1nLdLxXIgm7C/hPzrk3A58E/m+8/jTgNDP7FzN71Mwm9Wu8iAjwCLASxk8MzOz9cWv7s2a2JV630cy+G19fYmY/is+3mwCL159c3XXXzD5pZn8SX/+9+Lz9bHwebxwZmJndGHfnfc7M7p3h90Fkrpi2c31qBoMUkemTM7Nn4usvO+feV3XfzcAnnXO/BmBmB4DznXMfj2//GfAT59xvx93hHjezB4EbgH7n3NlmdjawdYzX/TGwycyanHN9wLXAN+P7/rtz7rCZ+cA/m9nZzrnnJrk/fw58wTn3sJmdBPwQOIPoA+1jzrl/MbNmYHCSzycyJ8V/x78C/L2ZlVdn42UKWAdsBFYBD5nZWc65I7McpojUkPi8eynwlXjVXcDvO+d2mtlbiRKDS4Bbgfc45/aN0x3+08DDzrnbzey9wPWTePl/cM79ZRzHnwK/A/zFiG1uBtY45/LH0g1fpNZM97leCbpIbRhwzp17jI/9VeCqqrFhDcBJwMXAnQDOuefMbFRy7ZwrmdkPgF83s/uA9wL/Lb77N83seqLPkRXAG4HJJuiXAW+s+hBrjVvL/wX4vJl9g+hLwN6p7arInOMBR8b5/90LPOqcKwIvm9kviE7iT8xifCJSO8o/1p8MPAX8eILE4F+Au+OecP8wxvNdDPwGgHPue2bWOYkYzooT84VAM9EP7CM9B3zDzL4NfHsSzylS66b1XK8u7iL1z4B/WzV+/STn3I74PjeJx38T+E2iX+OfcM71mNkaotbuS51zZwPfI0r8R6p+/ur7PeCiqphWOud6nHN3AL9L1N3+0arxOyI1yTnXTXRCfj+ARc6J7/428K54/VKibnC7kohTRGpC+cf6NwAZojHolcSg6nIGgHPu94FbgNXAM2a2ZIznHOt7QInhOUL1+ftu4OPOuTcBtzH2uf+9wBeBNwNPmZkaBKWuTfe5Xgm6SO3rAVqOcvuHwH+y+Kd1M9sQr98C/Lt43VnA2eM8/2bgPKIiF+Xu7a1AH9BlZsuBK8Z57AEzO8PMPKC6W/6PgI+Xb5jZufHyFOfc8865zwJPAkrQpaaY2T1EY0NPN7O9ZvY7RP9nv2NmzwLbgavjzX8IvG5mPwd+CvxX59zrScQtIrXDOdcF3Ej0Q/kA4yQG8Tn1MefcrcAhokS9WvX3gCuARfH6A0BbPEY9C/xa1WNagFfNLF1+bLX4fL/aOfdToh53C4la2kXqxkyf6/WLlkjtew4oxR8IdwN/Ddwcd4P7n8BngP8NPBcn6buJTrZfAv4q7tr+DPD4WE/unAvigjIfAf59vO5ZM3ua6ANoF1E3urHcDHwX2ANsY+gkfSPwxfi1U0RfEn4fuMnM3gUEwM+BB6b4Xogkyjl33Th3jSoK45xzwB/EFxGRSXPOPR2f9z9AlBh8ycxuAdLAvcCzwP+yaIonA/45XvfOqqe5DbjHzLYCPwNeiZ+7aGa3A48BLwPtVY/543j9L4HnGd4gAOADf2NmC+LX/YLqaki9melzvUWPEREREREREZEkqYu7iIiIiIiIyBygBF1ERERERERkDlCCLiIiIiIiIjIHKEEXERERERERmQOUoIuIiIiIiIjMAUrQRUREREREROYAJegiIiIiIiIic8D/B6CjiUFwxbS5AAAAAElFTkSuQmCC", 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" ] @@ -1773,6 +1752,221 @@ "\n", "The r-squared value indicates that our simple regression model only explains 43.0% variation in home price and this is not extremely high" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multilinear Regression\n", + "### Identification of categorical variables for prediction\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "categorical_features = house_data_clean.select_dtypes(\"object\").columns\n", + "fig, axes = plt.subplots(ncols=len(categorical_features), figsize=(10,5))\n", + "\n", + "for index, feature in enumerate(categorical_features):\n", + " house_data_clean.groupby(feature).mean().plot.bar(\n", + " y=\"price\", ax=axes[index])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " price waterfront view sqft_basement\n", + "0 221900.0 NO NONE 0.0\n", + "1 538000.0 NO NONE 400.0\n", + "2 180000.0 NO NONE 0.0\n", + "3 604000.0 NO NONE 910.0\n", + "4 510000.0 NO NONE 0.0\n", + "... ... ... ... ...\n", + "21592 360000.0 NO NONE 0.0\n", + "21593 400000.0 NO NONE 0.0\n", + "21594 402101.0 NO NONE 0.0\n", + "21595 400000.0 NO NONE 0.0\n", + "21596 325000.0 NO NONE 0.0\n", + "\n", + "[21534 rows x 4 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_column= pd.DataFrame(house_data_clean[['price','waterfront','view','sqft_basement']])\n", + "cat_column\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id -0.017392\n", + "bedrooms 0.308063\n", + "bathrooms 0.525053\n", + "sqft_living 0.701587\n", + "floors 0.257052\n", + " ... \n", + "sqft_basement_960.0 0.015356\n", + "sqft_basement_970.0 0.013139\n", + "sqft_basement_980.0 0.020798\n", + "sqft_basement_990.0 0.027098\n", + "sqft_basement_? -0.003530\n", + "Name: price, Length: 327, dtype: float64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert categorical variables into numerical format using one-hot encoding\n", + "house_data_encoded = pd.get_dummies(house_data_clean)\n", + "\n", + "# Calculate correlation coefficients between 'price' and other predictor variables\n", + "correlation =house_data_encoded.corr()['price'].drop('price')\n", + "correlation\n" + ] } ], "metadata": { From 10d879d048961d5e1750e574972d2cd8d195c30b Mon Sep 17 00:00:00 2001 From: sophline Date: Wed, 1 May 2024 19:20:00 +0300 Subject: [PATCH 51/53] correlation between target and numerical values --- student.ipynb | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/student.ipynb b/student.ipynb index 32313546..02826b97 100644 --- a/student.ipynb +++ b/student.ipynb @@ -1934,7 +1934,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1954,19 +1954,29 @@ "Name: price, Length: 327, dtype: float64" ] }, - "execution_count": 38, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Convert categorical variables into numerical format using one-hot encoding\n", + "# correlation of the predictor variables in relation to price\n", "house_data_encoded = pd.get_dummies(house_data_clean)\n", "\n", "# Calculate correlation coefficients between 'price' and other predictor variables\n", "correlation =house_data_encoded.corr()['price'].drop('price')\n", "correlation\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The correlation above shows bathrooms,bedrooms and sqft_living have a positive correlation\n", + "* sqft_living: The coefficient for the \"sqft_living\" feature is 0.701587. This indicates a strong positive relationship between the square footage of living space in a house and its price. As the living space area increases, the price of the house tends to increase proportionally.\n", + "\n", + "* bathrooms: The coefficient for the \"bathrooms\" feature is 0.525053. Similarly, there is a positive relationship between the number of bathrooms in a house and its price. Houses with more bathrooms tend to have higher prices" + ] } ], "metadata": { From a9fd260ff860e2e209fb9c2aafd7d4b198f98693 Mon Sep 17 00:00:00 2001 From: sophline Date: Wed, 1 May 2024 22:07:53 +0300 Subject: [PATCH 52/53] Create multilinear model --- student.ipynb | 1064 ++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 974 insertions(+), 90 deletions(-) diff --git a/student.ipynb b/student.ipynb index 02826b97..5b4097f5 100644 --- a/student.ipynb +++ b/student.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ "[21597 rows x 21 columns]" ] }, - "execution_count": 3, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -563,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -625,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -669,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -713,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -731,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -762,7 +762,7 @@ " dtype: float64}" ] }, - "execution_count": 9, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -826,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -843,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -863,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -893,7 +893,7 @@ " dtype: float64}" ] }, - "execution_count": 14, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -1015,7 +1015,7 @@ "20038 2009 98027 47.5644 -122.093 1880 3078 " ] }, - "execution_count": 15, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -1080,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -1092,7 +1092,7 @@ " 'Standard Deviation': 366059.58123129635}" ] }, - "execution_count": 17, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -1138,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -1184,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 129, "metadata": {}, "outputs": [], "source": [ @@ -1207,7 +1207,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -1256,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -1303,7 +1303,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -1343,7 +1343,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ @@ -1440,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -1465,7 +1465,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -1476,9 +1476,30 @@ " warnings.warn(msg, FutureWarning)\n" ] }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Plotting a histogram with kernel density estimate (KDE) of the \"price\" variable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprice_dist\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32mc:\\Users\\admin\\anaconda3\\envs\\learn-env\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[1;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax, x)\u001b[0m\n\u001b[0;32m 2617\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2618\u001b[0m \u001b[0mkde_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2619\u001b[1;33m \u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvertical\u001b[0m\u001b[1;33m,\u001b[0m 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"\u001b[1;32mc:\\Users\\admin\\anaconda3\\envs\\learn-env\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, **kwargs)\u001b[0m\n\u001b[0;32m 1728\u001b[0m \u001b[0mplot_kws\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1730\u001b[1;33m p.plot_univariate_density(\n\u001b[0m\u001b[0;32m 1731\u001b[0m \u001b[0mmultiple\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1732\u001b[0m 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316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 317\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlog_scale\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\Users\\admin\\anaconda3\\envs\\learn-env\\lib\\site-packages\\seaborn\\_statistics.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, x1, x2, weights)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;34m\"\"\"Fit and evaluate on univariate or bivariate data.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx2\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_eval_univariate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m_stats.pyx\u001b[0m in \u001b[0;36mscipy.stats._stats.gaussian_kernel_estimate\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mc:\\Users\\admin\\anaconda3\\envs\\learn-env\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 84\u001b[0m \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -1546,7 +1567,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -1557,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -1574,7 +1595,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -1584,7 +1605,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, "metadata": {}, "outputs": [], "source": [ @@ -1608,20 +1629,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Model R-Squared': 0.4922247077876325,\n", + "{'Model R-Squared': 0.4922247077876327,\n", " 'Model P_value': 0.0,\n", " 'Coeffecients': const -42152.946806\n", " sqft_living 279.932115\n", " dtype: float64}" ] }, - "execution_count": 47, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -1664,7 +1685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ @@ -1763,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -1792,7 +1813,701 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sqft_livingbedroomsgrade
0118037 Average
1257037 Average
277026 Low Average
3196047 Average
4168038 Good
............
21592153038 Good
21593231048 Good
21594102027 Average
21595160038 Good
21596102027 Average
\n", + "

21534 rows × 3 columns

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" + ], + "text/plain": [ + " sqft_living bedrooms grade\n", + "0 1180 3 7 Average\n", + "1 2570 3 7 Average\n", + "2 770 2 6 Low Average\n", + "3 1960 4 7 Average\n", + "4 1680 3 8 Good\n", + "... ... ... ...\n", + "21592 1530 3 8 Good\n", + "21593 2310 4 8 Good\n", + "21594 1020 2 7 Average\n", + "21595 1600 3 8 Good\n", + "21596 1020 2 7 Average\n", + "\n", + "[21534 rows x 3 columns]" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Create dataframe\n", + "cat_column= pd.DataFrame(house_data_clean[['sqft_living','bedrooms','grade']])\n", + "cat_column\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting Categorical to Numeric" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sqft_living bedrooms grade_11Excellent grade_12Luxury \\\n", + "0 1180 3 0 0 \n", + "1 2570 3 0 0 \n", + "2 770 2 0 0 \n", + "3 1960 4 0 0 \n", + "4 1680 3 0 0 \n", + "... ... ... ... ... \n", + "21592 1530 3 0 0 \n", + "21593 2310 4 0 0 \n", + "21594 1020 2 0 0 \n", + "21595 1600 3 0 0 \n", + "21596 1020 2 0 0 \n", + "\n", + " grade_13Mansion grade_3Poor grade_4Low grade_5Fair \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "21592 0 0 0 0 \n", + "21593 0 0 0 0 \n", + "21594 0 0 0 0 \n", + "21595 0 0 0 0 \n", + "21596 0 0 0 0 \n", + "\n", + " grade_6LowAverage grade_7Average grade_8Good grade_9Better \n", + "0 0 1 0 0 \n", + "1 0 1 0 0 \n", + "2 1 0 0 0 \n", + "3 0 1 0 0 \n", + "4 0 0 1 0 \n", + "... ... ... ... ... \n", + "21592 0 0 1 0 \n", + "21593 0 0 1 0 \n", + "21594 0 1 0 0 \n", + "21595 0 0 1 0 \n", + "21596 0 1 0 0 \n", + "\n", + "[21534 rows x 12 columns]" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# modifying columns\n", + "data_encoded.columns =data_encoded.columns.str.replace(' ', '')\n", + "data_encoded" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "def replace_bool_with_numeric(df, column_name):\n", + " \"\"\"\n", + " Replace boolean values with numeric values (1 for True, 0 for False) in the specified column of the DataFrame.\n", + "\n", + " Parameters:\n", + " df (DataFrame): The DataFrame containing the column to be modified.\n", + " column_name (str): The name of the column containing boolean values to be replaced.\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " df[column_name] = df[column_name].replace({True: 1, False: 0})\n", + "\n", + "# Apply the function to each grade column\n", + "grade_columns = ['grade_11Excellent', 'grade_12Luxury', 'grade_13Mansion', \n", + " 'grade_3Poor', 'grade_4Low', 'grade_5Fair', \n", + " 'grade_6LowAverage', 'grade_7Average', 'grade_8Good', 'grade_9Better']\n", + "for column in grade_columns:\n", + " replace_bool_with_numeric(data_encoded, column)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -1817,46 +2532,112 @@ " \n", " \n", " price\n", + " sqft_living\n", " waterfront\n", " view\n", " sqft_basement\n", + " grade_11Excellent\n", + " grade_12Luxury\n", + " grade_13Mansion\n", + " grade_3Poor\n", + " grade_4Low\n", + " grade_5Fair\n", + " grade_6LowAverage\n", + " grade_7Average\n", + " grade_8Good\n", + " grade_9Better\n", " \n", " \n", " \n", " \n", " 0\n", " 221900.0\n", + " 1180\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", " \n", " \n", " 1\n", " 538000.0\n", + " 2570\n", " NO\n", " NONE\n", " 400.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", " \n", " \n", " 2\n", " 180000.0\n", + " 770\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", " 3\n", " 604000.0\n", + " 1960\n", " NO\n", " NONE\n", " 910.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", " \n", " \n", " 4\n", " 510000.0\n", + " 1680\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", " \n", " \n", " ...\n", @@ -1864,118 +2645,221 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 21592\n", " 360000.0\n", + " 1530\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", " \n", " \n", " 21593\n", " 400000.0\n", + " 2310\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", " \n", " \n", " 21594\n", " 402101.0\n", + " 1020\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", " \n", " \n", " 21595\n", " 400000.0\n", + " 1600\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", " \n", " \n", " 21596\n", " 325000.0\n", + " 1020\n", " NO\n", " NONE\n", " 0.0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", " \n", " \n", "\n", - "

21534 rows × 4 columns

\n", + "

21534 rows × 15 columns

\n", "" ], "text/plain": [ - " price waterfront view sqft_basement\n", - "0 221900.0 NO NONE 0.0\n", - "1 538000.0 NO NONE 400.0\n", - "2 180000.0 NO NONE 0.0\n", - "3 604000.0 NO NONE 910.0\n", - "4 510000.0 NO NONE 0.0\n", - "... ... ... ... ...\n", - "21592 360000.0 NO NONE 0.0\n", - "21593 400000.0 NO NONE 0.0\n", - "21594 402101.0 NO NONE 0.0\n", - "21595 400000.0 NO NONE 0.0\n", - "21596 325000.0 NO NONE 0.0\n", + " price sqft_living waterfront view sqft_basement \\\n", + "0 221900.0 1180 NO NONE 0.0 \n", + "1 538000.0 2570 NO NONE 400.0 \n", + "2 180000.0 770 NO NONE 0.0 \n", + "3 604000.0 1960 NO NONE 910.0 \n", + "4 510000.0 1680 NO NONE 0.0 \n", + "... ... ... ... ... ... \n", + "21592 360000.0 1530 NO NONE 0.0 \n", + "21593 400000.0 2310 NO NONE 0.0 \n", + "21594 402101.0 1020 NO NONE 0.0 \n", + "21595 400000.0 1600 NO NONE 0.0 \n", + "21596 325000.0 1020 NO NONE 0.0 \n", + "\n", + " grade_11Excellent grade_12Luxury grade_13Mansion grade_3Poor \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "21592 0 0 0 0 \n", + "21593 0 0 0 0 \n", + "21594 0 0 0 0 \n", + "21595 0 0 0 0 \n", + "21596 0 0 0 0 \n", + "\n", + " grade_4Low grade_5Fair grade_6LowAverage grade_7Average \\\n", + "0 0 0 0 1 \n", + "1 0 0 0 1 \n", + "2 0 0 1 0 \n", + "3 0 0 0 1 \n", + "4 0 0 0 0 \n", + "... ... ... ... ... \n", + "21592 0 0 0 0 \n", + "21593 0 0 0 0 \n", + "21594 0 0 0 1 \n", + "21595 0 0 0 0 \n", + "21596 0 0 0 1 \n", + "\n", + " grade_8Good grade_9Better \n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 1 0 \n", + "... ... ... \n", + "21592 1 0 \n", + "21593 1 0 \n", + "21594 0 0 \n", + "21595 1 0 \n", + "21596 0 0 \n", "\n", - "[21534 rows x 4 columns]" + "[21534 rows x 15 columns]" ] }, - "execution_count": 37, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "cat_column= pd.DataFrame(house_data_clean[['price','waterfront','view','sqft_basement']])\n", - "cat_column\n" + "data_encoded" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 155, + "metadata": {}, + "outputs": [], + "source": [ + "multi_model=regression(data_encoded,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "id -0.017392\n", - "bedrooms 0.308063\n", - "bathrooms 0.525053\n", - "sqft_living 0.701587\n", - "floors 0.257052\n", - " ... \n", - "sqft_basement_960.0 0.015356\n", - "sqft_basement_970.0 0.013139\n", - "sqft_basement_980.0 0.020798\n", - "sqft_basement_990.0 0.027098\n", - "sqft_basement_? -0.003530\n", - "Name: price, Length: 327, dtype: float64" + "{'Model R-Squared': 0.5831491676288707,\n", + " 'Model P_value': 0.0,\n", + " 'Coeffecients': const 5.469724e+05\n", + " sqft_living 1.750225e+02\n", + " bedrooms -2.320617e+04\n", + " grade_11Excellent 2.775082e+05\n", + " grade_12Luxury 7.638318e+05\n", + " grade_13Mansion 1.966551e+06\n", + " grade_3Poor -3.527780e+05\n", + " grade_4Low -4.171575e+05\n", + " grade_5Fair -4.151264e+05\n", + " grade_6LowAverage -3.913080e+05\n", + " grade_7Average -3.644529e+05\n", + " grade_8Good -3.055288e+05\n", + " grade_9Better -1.872877e+05\n", + " dtype: float64}" ] }, - "execution_count": 39, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# correlation of the predictor variables in relation to price\n", - "house_data_encoded = pd.get_dummies(house_data_clean)\n", - "\n", - "# Calculate correlation coefficients between 'price' and other predictor variables\n", - "correlation =house_data_encoded.corr()['price'].drop('price')\n", - "correlation\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The correlation above shows bathrooms,bedrooms and sqft_living have a positive correlation\n", - "* sqft_living: The coefficient for the \"sqft_living\" feature is 0.701587. This indicates a strong positive relationship between the square footage of living space in a house and its price. As the living space area increases, the price of the house tends to increase proportionally.\n", - "\n", - "* bathrooms: The coefficient for the \"bathrooms\" feature is 0.525053. Similarly, there is a positive relationship between the number of bathrooms in a house and its price. Houses with more bathrooms tend to have higher prices" + "#calling the regression analysis function to get summary\n", + "regression_analysis(multi_model)" ] } ], From 6777827875afeb894ca4738d734e4ddc053d0c0d Mon Sep 17 00:00:00 2001 From: sophline Date: Wed, 1 May 2024 22:37:02 +0300 Subject: [PATCH 53/53] model evaluation --- student.ipynb | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/student.ipynb b/student.ipynb index 5b4097f5..46686173 100644 --- a/student.ipynb +++ b/student.ipynb @@ -2861,6 +2861,23 @@ "#calling the regression analysis function to get summary\n", "regression_analysis(multi_model)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Interpretation\n", + "\n", + "Model R-Squared: The R-squared value (0.583) indicates the proportion of variance in the target variable (house prices) that is explained by the independent variables (features) in the model. In this case, approximately 58.3% of the variance in house prices is explained by the features included in the model. A higher R-squared value suggests that the model fits the data well.\n", + "\n", + "Model P-value: The p-value (0.0) associated with the overall model indicates the statistical significance of the model. A p-value less than the significance level (e.g., 0.05) suggests that the model is statistically significant, meaning that at least one of the coefficients is significantly different from zero. In this case, the very low p-value indicates that the model is statistically significant.\n", + "\n", + "\n", + "Coefficients: The coefficients represent the estimated effects of the independent variables (features) on the target variable (house prices), holding other variables constant. Each coefficient indicates the change in the target variable for a one-unit change in the corresponding independent variable, while keeping other variables constant.\n", + "\n", + "For example, the coefficient for 'sqft_living' is approximately 175.02. This suggests that, on average, for every additional square foot of living space in a house, the price increases by $175, holding other factors constant.\n", + "Similarly, coefficients for the 'grade' categories represent the change in house price compared to a reference category (e.g., grade_11Excellent, grade_12Luxury, etc.). Positive coefficients indicate an increase in price compared to the reference category, while negative coefficients indicate a decrease." + ] } ], "metadata": {