Update student.ipynb

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": [
         {