From 02f5aa4204c822d69b4dbdae7f7b5c30b52b9031 Mon Sep 17 00:00:00 2001
From: sandrakiptumm <sandrakiptumm@gmail.com>
Date: Sat, 27 Apr 2024 21:33:42 +0300
Subject: [PATCH 01/25] Importing modules

---
 student.ipynb | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/student.ipynb b/student.ipynb
index d3bb34af..1fd5648e 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -22,6 +22,19 @@
    "source": [
     "# Your code here - remember to use markdown cells for comments as well!"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sqlite3\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
   }
  ],
  "metadata": {

From 99392888b6ff63405f09a378a4624fdc68e37097 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sat, 27 Apr 2024 22:23:48 +0300
Subject: [PATCH 02/25] Update student.ipynb

---
 student.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 1fd5648e..981801dd 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -7,10 +7,10 @@
     "## Final Project Submission\n",
     "\n",
     "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
+    "* Student name: Calvine Dasilver\n",
+    "* Student pace: full time\n",
     "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
+    "* Instructor name: Nikita\n",
     "* Blog post URL:\n"
    ]
   },

From dc953c2a66c113466d164708d18731017bb1c16d Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sat, 27 Apr 2024 23:25:55 +0300
Subject: [PATCH 03/25] Business-understanding

business overview and understanding
---
 student.ipynb | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/student.ipynb b/student.ipynb
index 981801dd..4a8a8f58 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -14,6 +14,33 @@
     "* Blog post URL:\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   # King County House Sales Analysis & Regression Modeling\n",
+    "\n",
+    "   ## Project Overview\n",
+    "\n",
+    "   ### <li>Business Understanding\n",
+    "\n",
+    "The real estate market plays a crucial role in the economic health and stability of a region. Understanding the factors that influence house prices is essential for both buyers and sellers to navigate the market effectively. This project focuses on a specific northwestern county in the United States, aiming to shed light on the key determinants of property valuation in this area.\n",
+    "   ##### Problem Statements:\n",
+    "What are the most significant factors influencing house prices in this northwestern county?How can we quantify the relationship between these factors and property value?Can we develop a reliable model to predict house prices based on relevant characteristics?\n",
+    "   ##### Challenges:\n",
+    "* Real estate data can be complex and multifaceted, encompassing various property features and local market trends.\n",
+    "* Accurately identifying and quantifying the relative impact of each factor on house prices can be challenging.\n",
+    "* External factors like economic conditions and interest rates might also influence prices, requiring careful consideration.\n",
+    "\n",
+    "##### Proposed Solutions:\n",
+    "We propose utilizing multiple linear regression, a powerful machine learning technique. This method allows us to analyze a large dataset of house sales and identify the statistical relationships between various property features (e.g., square footage, number of bedrooms, location) and the corresponding sale prices.\n",
+    "   ##### Objectives:\n",
+    "1. Develop a robust multiple linear regression model that accurately predicts house prices in the chosen northwestern county.\n",
+    "2. Identify the most significant factors influencing property value within this specific market.\n",
+    "3. Provide valuable insights into the housing market dynamics of the region, benefiting potential buyers, real estate agents, and other stakeholders.\n",
+    "   "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 94c04b3aa09b8277ab40916e09f15a0bf9a8cc12 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sat, 27 Apr 2024 23:45:03 +0300
Subject: [PATCH 04/25] Update student.ipynb

---
 student.ipynb | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/student.ipynb b/student.ipynb
index 4a8a8f58..ccbd36f3 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -38,7 +38,14 @@
     "1. Develop a robust multiple linear regression model that accurately predicts house prices in the chosen northwestern county.\n",
     "2. Identify the most significant factors influencing property value within this specific market.\n",
     "3. Provide valuable insights into the housing market dynamics of the region, benefiting potential buyers, real estate agents, and other stakeholders.\n",
-    "   "
+    "   \n",
+    " \n",
+    "**Research questions that would help to achieve the objectives**:\n",
+    "\n",
+    "1. How does the number of bedrooms, bathrooms, grade and square footage of a house correlate with its sale price in King County?\n",
+    "2. How much can a homeowner expect the value of their home to increase after a specific renovation project?\n",
+    "3. Which renovation projects have the most significant impact on a home's market value in the northwestern county?\n",
+    "4. Are there specific combinations of renovation projects that provide an interdependent effect on a home's market value?"
    ]
   },
   {

From a0708a0e09bedc96b91113f89f4007d9916561eb Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sun, 28 Apr 2024 00:13:40 +0300
Subject: [PATCH 05/25] Data-understanding

data source and data description
---
 student.ipynb | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/student.ipynb b/student.ipynb
index ccbd36f3..0e889c5f 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -48,6 +48,59 @@
     "4. Are there specific combinations of renovation projects that provide an interdependent effect on a home's market value?"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   ### <li> Data Understanding\n",
+    "\n",
+    "Our analysis leverages the King County House Sales dataset - a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n",
+    "\n",
+    "**The King County House Sales dataset contains the following columns;**\n",
+    "\n",
+    "Price - Sale price (prediction target)\n",
+    "\n",
+    "bedrooms - Number of bedrooms,\n",
+    "\n",
+    "bathrooms - Number of bathrooms,\n",
+    "\n",
+    "sqft_living - Square footage of living space in the home,\n",
+    "\n",
+    "sqft_lot - Square footage of the lot,\n",
+    "\n",
+    "floors - Number of floors (levels) in house,\n",
+    "\n",
+    "view - Quality of view from house,\n",
+    "\n",
+    "condition - How good the overall condition of the house is. Related to maintenance of house,\n",
+    "\n",
+    "grade - Overall grade of the house. Related to the construction and design of the house,\n",
+    "\n",
+    "sqft_above - Square footage of house apart from basement,\n",
+    "\n",
+    "sqft_basement - Square footage of the basement,\n",
+    "\n",
+    "yr_built - Year when house was built,\n",
+    "\n",
+    "yr_renovated - Year when house was renovated,\n",
+    "\n",
+    "zipcode - ZIP Code used by the United States Postal Service,\n",
+    "\n",
+    "sqft_living15 - The square footage of interior housing living space for the nearest 15 neighbors,\n",
+    "\n",
+    "sqft_lot15 - The square footage of the land lots of the nearest 15 neighbors, and\n",
+    "\n",
+    "sell_yr - Date house was sold.\n",
+    "\n",
+    "\n",
+    "We need to be aware of certain constraints within the data, as these might influence our analysis and interpretation of the results. From the sources;\n",
+    "\n",
+    "1. The data may contain anomalies or inconsistencies that require careful examination during analysis. For instance, a record lists a house with 33 bedrooms, which appears to be an outlier\n",
+    "\n",
+    "2. It's important to consider the time frame of the data (May 2014 - May 2015) as it may not fully capture the current market dynamics in King County.\n",
+    "3. It's important to acknowledge the scope of the data. While it provides details on house features, it may not capture external factors such as interest rates or the overall economic climate, which can also play a role in determining property values."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 27b7de987bc0143cc0410d7f149c4e786dfe8868 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sun, 28 Apr 2024 00:18:00 +0300
Subject: [PATCH 06/25] Update student.ipynb

---
 student.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 0e889c5f..67a5b8be 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "   ## Project Overview\n",
     "\n",
-    "   ### <li>Business Understanding\n",
+    "   ### <li>**Business Understanding**\n",
     "\n",
     "The real estate market plays a crucial role in the economic health and stability of a region. Understanding the factors that influence house prices is essential for both buyers and sellers to navigate the market effectively. This project focuses on a specific northwestern county in the United States, aiming to shed light on the key determinants of property valuation in this area.\n",
     "   ##### Problem Statements:\n",
@@ -52,7 +52,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "   ### <li> Data Understanding\n",
+    "   ### <li> **Data Understanding**\n",
     "\n",
     "Our analysis leverages the King County House Sales dataset - a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n",
     "\n",

From 5dca9f8514bf6244ef977d8f4a11f58ca51b22ae Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sun, 28 Apr 2024 00:28:27 +0300
Subject: [PATCH 07/25] Update student.ipynb

---
 student.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/student.ipynb b/student.ipynb
index 67a5b8be..d54668c9 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "   # King County House Sales Analysis & Regression Modeling\n",
+    "   #  Demystifying House Sales Analysis & Regression Modeling in a Northwestern County\n",
     "\n",
     "   ## Project Overview\n",
     "\n",

From 39a544a20a1e5a8cfc9aee46a8e07974752b8d90 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sun, 28 Apr 2024 00:43:50 +0300
Subject: [PATCH 08/25] Update student.ipynb

---
 student.ipynb | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index d54668c9..98841fb8 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -18,10 +18,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "   #  Demystifying House Sales Analysis & Regression Modeling in a Northwestern County\n",
-    "\n",
-    "   ## Project Overview\n",
+    " #  Demystifying House Sales Analysis & Regression Modeling in a Northwestern County\n",
     "\n",
+    "   ## Project Overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "image_path = '/content/Real estate.jpg'\n",
+    "display(Image(filename=image_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "   ### <li>**Business Understanding**\n",
     "\n",
     "The real estate market plays a crucial role in the economic health and stability of a region. Understanding the factors that influence house prices is essential for both buyers and sellers to navigate the market effectively. This project focuses on a specific northwestern county in the United States, aiming to shed light on the key determinants of property valuation in this area.\n",

From 516f422bb15391106448be3ce0d1955d731c0687 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sun, 28 Apr 2024 01:13:08 +0300
Subject: [PATCH 09/25] Update student.ipynb

---
 student.ipynb | 15 ++-------------
 1 file changed, 2 insertions(+), 13 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 98841fb8..c2d72a52 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -18,22 +18,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " #  Demystifying House Sales Analysis & Regression Modeling in a Northwestern County\n",
+    " #  Demystifying House Sales Analysis with Regression Modeling in a Northwestern County\n",
     "\n",
     "   ## Project Overview"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "image_path = '/content/Real estate.jpg'\n",
-    "display(Image(filename=image_path))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -156,7 +145,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

From 602cb3201712b528de133d596b0fdbc136ecb368 Mon Sep 17 00:00:00 2001
From: jackoti <jack.otieno@student.moringaschool.com>
Date: Sat, 27 Apr 2024 21:46:28 -0700
Subject: [PATCH 10/25] import module and read data

---
 student.ipynb | 240 +++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 239 insertions(+), 1 deletion(-)

diff --git a/student.ipynb b/student.ipynb
index d3bb34af..1f3f5c1e 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -22,6 +22,244 @@
    "source": [
     "# Your code here - remember to use markdown cells for comments as well!"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DATA PREPARATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "import pandas as pd\n",
+    "import sqlite3\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"data/kc_house_data.csv\")\n",
+    "data.head()"
+   ]
   }
  ],
  "metadata": {
@@ -40,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

From a5f1a608f1c8331cdcb566cab26b015cb6e94baa Mon Sep 17 00:00:00 2001
From: sandrakiptumm <sandrakiptumm@gmail.com>
Date: Sun, 28 Apr 2024 14:44:45 +0300
Subject: [PATCH 11/25] deleting importations in main

---
 student.ipynb | 19 +++----------------
 1 file changed, 3 insertions(+), 16 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 1fd5648e..a1e5b9cd 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -22,26 +22,13 @@
    "source": [
     "# Your code here - remember to use markdown cells for comments as well!"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import sqlite3\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python (learn-env)",
    "language": "python",
-   "name": "python3"
+   "name": "learn-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -53,7 +40,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

From 8b8b1c9a37f3b0643582498e08333f4f820d49fb Mon Sep 17 00:00:00 2001
From: jackoti <jack.otieno@student.moringaschool.com>
Date: Sun, 28 Apr 2024 09:51:49 -0700
Subject: [PATCH 12/25] check columns and missing values

---
 student.ipynb | 239 ++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 233 insertions(+), 6 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 1f3f5c1e..220f662f 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,12 +41,22 @@
     "import sqlite3\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.api import OLS\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.preprocessing import LabelEncoder"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -251,15 +261,232 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data/kc_house_data.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
+      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
+      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
+      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "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": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data = pd.read_csv(\"data/kc_house_data.csv\")\n",
-    "data.head()"
+    "#checking null values\n",
+    "df.isna().sum()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 5cb2038f4b3b5262ca773d660e7975695db67766 Mon Sep 17 00:00:00 2001
From: jackoti <jack.otieno@student.moringaschool.com>
Date: Sun, 28 Apr 2024 10:07:16 -0700
Subject: [PATCH 13/25] check data info and statistical summary

---
 student.ipynb | 413 ++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 401 insertions(+), 12 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 220f662f..0183696a 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -336,31 +336,420 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\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": [
+    "#checking on data information\n",
+    "df.info()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>17755.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.580474e+09</td>\n",
+       "      <td>5.402966e+05</td>\n",
+       "      <td>3.373200</td>\n",
+       "      <td>2.115826</td>\n",
+       "      <td>2080.321850</td>\n",
+       "      <td>1.509941e+04</td>\n",
+       "      <td>1.494096</td>\n",
+       "      <td>1788.596842</td>\n",
+       "      <td>1970.999676</td>\n",
+       "      <td>83.636778</td>\n",
+       "      <td>98077.951845</td>\n",
+       "      <td>47.560093</td>\n",
+       "      <td>-122.213982</td>\n",
+       "      <td>1986.620318</td>\n",
+       "      <td>12758.283512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.876736e+09</td>\n",
+       "      <td>3.673681e+05</td>\n",
+       "      <td>0.926299</td>\n",
+       "      <td>0.768984</td>\n",
+       "      <td>918.106125</td>\n",
+       "      <td>4.141264e+04</td>\n",
+       "      <td>0.539683</td>\n",
+       "      <td>827.759761</td>\n",
+       "      <td>29.375234</td>\n",
+       "      <td>399.946414</td>\n",
+       "      <td>53.513072</td>\n",
+       "      <td>0.138552</td>\n",
+       "      <td>0.140724</td>\n",
+       "      <td>685.230472</td>\n",
+       "      <td>27274.441950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000102e+06</td>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98001.000000</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.123049e+09</td>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1190.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98033.000000</td>\n",
+       "      <td>47.471100</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.904930e+09</td>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.618000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98065.000000</td>\n",
+       "      <td>47.571800</td>\n",
+       "      <td>-122.231000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7620.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.308900e+09</td>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068500e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98118.000000</td>\n",
+       "      <td>47.678000</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10083.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.900000e+09</td>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>98199.000000</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
+       "count  2.159700e+04  2.159700e+04  21597.000000  21597.000000  21597.000000   \n",
+       "mean   4.580474e+09  5.402966e+05      3.373200      2.115826   2080.321850   \n",
+       "std    2.876736e+09  3.673681e+05      0.926299      0.768984    918.106125   \n",
+       "min    1.000102e+06  7.800000e+04      1.000000      0.500000    370.000000   \n",
+       "25%    2.123049e+09  3.220000e+05      3.000000      1.750000   1430.000000   \n",
+       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
+       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
+       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
+       "\n",
+       "           sqft_lot        floors    sqft_above      yr_built  yr_renovated  \\\n",
+       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  17755.000000   \n",
+       "mean   1.509941e+04      1.494096   1788.596842   1970.999676     83.636778   \n",
+       "std    4.141264e+04      0.539683    827.759761     29.375234    399.946414   \n",
+       "min    5.200000e+02      1.000000    370.000000   1900.000000      0.000000   \n",
+       "25%    5.040000e+03      1.000000   1190.000000   1951.000000      0.000000   \n",
+       "50%    7.618000e+03      1.500000   1560.000000   1975.000000      0.000000   \n",
+       "75%    1.068500e+04      2.000000   2210.000000   1997.000000      0.000000   \n",
+       "max    1.651359e+06      3.500000   9410.000000   2015.000000   2015.000000   \n",
+       "\n",
+       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
+       "count  21597.000000  21597.000000  21597.000000   21597.000000   21597.000000  \n",
+       "mean   98077.951845     47.560093   -122.213982    1986.620318   12758.283512  \n",
+       "std       53.513072      0.138552      0.140724     685.230472   27274.441950  \n",
+       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
+       "25%    98033.000000     47.471100   -122.328000    1490.000000    5100.000000  \n",
+       "50%    98065.000000     47.571800   -122.231000    1840.000000    7620.000000  \n",
+       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
+       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#statistical summary\n",
+    "df.describe()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>yr_built</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>1965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1987</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date     price  bedrooms  bathrooms  sqft_living  condition  yr_built\n",
+       "0  10/13/2014  221900.0         3       1.00         1180    Average      1955\n",
+       "1   12/9/2014  538000.0         3       2.25         2570    Average      1951\n",
+       "2   2/25/2015  180000.0         2       1.00          770    Average      1933\n",
+       "3   12/9/2014  604000.0         4       3.00         1960  Very Good      1965\n",
+       "4   2/18/2015  510000.0         3       2.00         1680    Average      1987"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#we'll focus on the columns mentioned above and drop the rest\n",
+    "df.drop(columns=['id','lat','long','sqft_lot','floors','waterfront','view','zipcode','sqft_living15','sqft_lot15','grade','yr_renovated','sqft_above','sqft_basement'],inplace=True)\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date           0\n",
+       "price          0\n",
+       "bedrooms       0\n",
+       "bathrooms      0\n",
+       "sqft_living    0\n",
+       "condition      0\n",
+       "yr_built       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for for null values\n",
+    "df.isna().sum()"
+   ]
   },
   {
    "cell_type": "code",

From 7b42ab768a5ec8dda38793a846996a72371a3af4 Mon Sep 17 00:00:00 2001
From: jackoti <jack.otieno@student.moringaschool.com>
Date: Sun, 28 Apr 2024 10:11:38 -0700
Subject: [PATCH 14/25] added additional information on the statistical summary

---
 student.ipynb | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/student.ipynb b/student.ipynb
index 0183696a..bece4158 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -613,6 +613,16 @@
     "df.describe()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistical summary observation\n",
+    "count for each column is 21597 this shows that we dont have missing values\n",
+    "The mean value of the house price is USD 540297 while the minimum house price is USD 78000 and maximum house price is USD 7700000\n",
+    "The standard deviation of the house price stands at USD 367368."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 68,

From eb067369b051a15a46737bc67ace4576e3c30230 Mon Sep 17 00:00:00 2001
From: jackoti <jack.otieno@student.moringaschool.com>
Date: Sun, 28 Apr 2024 10:34:20 -0700
Subject: [PATCH 15/25] added additional information

---
 student.ipynb | 650 +++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 612 insertions(+), 38 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index bece4158..55d0227c 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +261,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +324,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +603,7 @@
        "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
       ]
      },
-     "execution_count": 67,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -722,7 +722,7 @@
        "4   2/18/2015  510000.0         3       2.00         1680    Average      1987"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -751,7 +751,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -763,52 +763,626 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>yr_built</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>1965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1987</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   date     price  bedrooms  bathrooms  sqft_living  condition  yr_built\n",
+       "0  2014  221900.0         3       1.00         1180    Average      1955\n",
+       "1  2014  538000.0         3       2.25         2570    Average      1951\n",
+       "2  2015  180000.0         2       1.00          770    Average      1933\n",
+       "3  2014  604000.0         4       3.00         1960  Very Good      1965\n",
+       "4  2015  510000.0         3       2.00         1680    Average      1987"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#converting date to datetime format\n",
+    "df['date']=pd.to_datetime(df['date'])\n",
+    "#extracting year from date column\n",
+    "df.date=df['date'].dt.year\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>sell_yr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr\n",
+       "0  221900.0         3       1.00         1180    Average      1955     2014\n",
+       "1  538000.0         3       2.25         2570    Average      1951     2014\n",
+       "2  180000.0         2       1.00          770    Average      1933     2015\n",
+       "3  604000.0         4       3.00         1960  Very Good      1965     2014\n",
+       "4  510000.0         3       2.00         1680    Average      1987     2015"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Creating a new column for sell year\n",
+    "df['sell_yr'] = pd.to_datetime(df['date'],format='%Y').dt.year\n",
+    "df.drop(columns='date', inplace=True)\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>sell_yr</th>\n",
+       "      <th>house_age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr  \\\n",
+       "0  221900.0         3       1.00         1180    Average      1955     2014   \n",
+       "1  538000.0         3       2.25         2570    Average      1951     2014   \n",
+       "2  180000.0         2       1.00          770    Average      1933     2015   \n",
+       "3  604000.0         4       3.00         1960  Very Good      1965     2014   \n",
+       "4  510000.0         3       2.00         1680    Average      1987     2015   \n",
+       "\n",
+       "   house_age  \n",
+       "0         59  \n",
+       "1         63  \n",
+       "2         82  \n",
+       "3         49  \n",
+       "4         28  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating column house age at year of sale\n",
+    "df['house_age']=df['sell_yr']-df['yr_built']\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>sell_yr</th>\n",
+       "      <th>house_age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr  \\\n",
+       "0  221900.0         3       1.00         1180          3      1955     2014   \n",
+       "1  538000.0         3       2.25         2570          3      1951     2014   \n",
+       "2  180000.0         2       1.00          770          3      1933     2015   \n",
+       "3  604000.0         4       3.00         1960          5      1965     2014   \n",
+       "4  510000.0         3       2.00         1680          3      1987     2015   \n",
+       "\n",
+       "   house_age  \n",
+       "0         59  \n",
+       "1         63  \n",
+       "2         82  \n",
+       "3         49  \n",
+       "4         28  "
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#transformation of categorical values 'condition' column\n",
+    "df['condition'].replace(to_replace=['Poor', 'Fair', 'Average', 'Good', 'Very Good'], value=[1, 2, 3, 4, 5], inplace=True)\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>sell_yr</th>\n",
+       "      <th>house_age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr  \\\n",
+       "0  221900.0         3       1.00         1180          3      1955     2014   \n",
+       "1  538000.0         3       2.25         2570          3      1951     2014   \n",
+       "2  180000.0         2       1.00          770          3      1933     2015   \n",
+       "3  604000.0         4       3.00         1960          5      1965     2014   \n",
+       "4  510000.0         3       2.00         1680          3      1987     2015   \n",
+       "\n",
+       "   house_age  \n",
+       "0         59  \n",
+       "1         63  \n",
+       "2         82  \n",
+       "3         49  \n",
+       "4         28  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#converting the 'sell_yr' column to int64\n",
+    "df['sell_yr'] = df['sell_yr'].astype('int64')\n",
+    "df.head()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   price        21597 non-null  float64\n",
+      " 1   bedrooms     21597 non-null  int64  \n",
+      " 2   bathrooms    21597 non-null  float64\n",
+      " 3   sqft_living  21597 non-null  int64  \n",
+      " 4   condition    21597 non-null  int64  \n",
+      " 5   yr_built     21597 non-null  int64  \n",
+      " 6   sell_yr      21597 non-null  int64  \n",
+      " 7   house_age    21597 non-null  int64  \n",
+      "dtypes: float64(2), int64(6)\n",
+      "memory usage: 1.3 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
   },
   {
    "cell_type": "code",

From 77c916b1260f1ff52b6ac1e9dcb6d65eab356c41 Mon Sep 17 00:00:00 2001
From: Sandra Kiptum <100568848+Sandrakiptumm@users.noreply.github.com>
Date: Sun, 28 Apr 2024 14:56:07 +0300
Subject: [PATCH 16/25] Update student.ipynb

---
 student.ipynb | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 9959cf3b..49b18223 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -1472,16 +1472,18 @@
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-  feature/data-preparation
-   "version": "3.11.5"
-   main
-  }
+
+  {
+    "file_extension": ".py",
+    "mimetype": "text/x-python",
+    "name": "python",
+    "nbconvert_exporter": "python",
+    "pygments_lexer": "ipython3",
+    "version": "3.11.5",
+    "feature": "data-preparation",
+    "main": true
+}
+
  },
  "nbformat": 4,
  "nbformat_minor": 2

From 8ca69ac8fedf7107c0e7809bd2fd1077e4175a1e Mon Sep 17 00:00:00 2001
From: sandrakiptumm <sandrakiptumm@gmail.com>
Date: Sun, 28 Apr 2024 15:19:16 +0300
Subject: [PATCH 17/25] Revert "added additional information"

This reverts commit eb067369b051a15a46737bc67ace4576e3c30230.
---
 student.ipynb | 650 +++-----------------------------------------------
 1 file changed, 38 insertions(+), 612 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 49b18223..64a1b461 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +261,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +324,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +603,7 @@
        "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
       ]
      },
-     "execution_count": 81,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -722,7 +722,7 @@
        "4   2/18/2015  510000.0         3       2.00         1680    Average      1987"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -751,7 +751,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -763,626 +763,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>yr_built</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2014</td>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2014</td>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2015</td>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014</td>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>Very Good</td>\n",
-       "      <td>1965</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015</td>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1987</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   date     price  bedrooms  bathrooms  sqft_living  condition  yr_built\n",
-       "0  2014  221900.0         3       1.00         1180    Average      1955\n",
-       "1  2014  538000.0         3       2.25         2570    Average      1951\n",
-       "2  2015  180000.0         2       1.00          770    Average      1933\n",
-       "3  2014  604000.0         4       3.00         1960  Very Good      1965\n",
-       "4  2015  510000.0         3       2.00         1680    Average      1987"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#converting date to datetime format\n",
-    "df['date']=pd.to_datetime(df['date'])\n",
-    "#extracting year from date column\n",
-    "df.date=df['date'].dt.year\n",
-    "df.head()"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>sell_yr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2014</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>2014</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>Very Good</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>2014</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>2015</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr\n",
-       "0  221900.0         3       1.00         1180    Average      1955     2014\n",
-       "1  538000.0         3       2.25         2570    Average      1951     2014\n",
-       "2  180000.0         2       1.00          770    Average      1933     2015\n",
-       "3  604000.0         4       3.00         1960  Very Good      1965     2014\n",
-       "4  510000.0         3       2.00         1680    Average      1987     2015"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Creating a new column for sell year\n",
-    "df['sell_yr'] = pd.to_datetime(df['date'],format='%Y').dt.year\n",
-    "df.drop(columns='date', inplace=True)\n",
-    "df.head()"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>sell_yr</th>\n",
-       "      <th>house_age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>Very Good</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr  \\\n",
-       "0  221900.0         3       1.00         1180    Average      1955     2014   \n",
-       "1  538000.0         3       2.25         2570    Average      1951     2014   \n",
-       "2  180000.0         2       1.00          770    Average      1933     2015   \n",
-       "3  604000.0         4       3.00         1960  Very Good      1965     2014   \n",
-       "4  510000.0         3       2.00         1680    Average      1987     2015   \n",
-       "\n",
-       "   house_age  \n",
-       "0         59  \n",
-       "1         63  \n",
-       "2         82  \n",
-       "3         49  \n",
-       "4         28  "
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#creating column house age at year of sale\n",
-    "df['house_age']=df['sell_yr']-df['yr_built']\n",
-    "df.head()"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>sell_yr</th>\n",
-       "      <th>house_age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr  \\\n",
-       "0  221900.0         3       1.00         1180          3      1955     2014   \n",
-       "1  538000.0         3       2.25         2570          3      1951     2014   \n",
-       "2  180000.0         2       1.00          770          3      1933     2015   \n",
-       "3  604000.0         4       3.00         1960          5      1965     2014   \n",
-       "4  510000.0         3       2.00         1680          3      1987     2015   \n",
-       "\n",
-       "   house_age  \n",
-       "0         59  \n",
-       "1         63  \n",
-       "2         82  \n",
-       "3         49  \n",
-       "4         28  "
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#transformation of categorical values 'condition' column\n",
-    "df['condition'].replace(to_replace=['Poor', 'Fair', 'Average', 'Good', 'Very Good'], value=[1, 2, 3, 4, 5], inplace=True)\n",
-    "df.head()"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>sell_yr</th>\n",
-       "      <th>house_age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      price  bedrooms  bathrooms  sqft_living  condition  yr_built  sell_yr  \\\n",
-       "0  221900.0         3       1.00         1180          3      1955     2014   \n",
-       "1  538000.0         3       2.25         2570          3      1951     2014   \n",
-       "2  180000.0         2       1.00          770          3      1933     2015   \n",
-       "3  604000.0         4       3.00         1960          5      1965     2014   \n",
-       "4  510000.0         3       2.00         1680          3      1987     2015   \n",
-       "\n",
-       "   house_age  \n",
-       "0         59  \n",
-       "1         63  \n",
-       "2         82  \n",
-       "3         49  \n",
-       "4         28  "
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#converting the 'sell_yr' column to int64\n",
-    "df['sell_yr'] = df['sell_yr'].astype('int64')\n",
-    "df.head()"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 21597 entries, 0 to 21596\n",
-      "Data columns (total 8 columns):\n",
-      " #   Column       Non-Null Count  Dtype  \n",
-      "---  ------       --------------  -----  \n",
-      " 0   price        21597 non-null  float64\n",
-      " 1   bedrooms     21597 non-null  int64  \n",
-      " 2   bathrooms    21597 non-null  float64\n",
-      " 3   sqft_living  21597 non-null  int64  \n",
-      " 4   condition    21597 non-null  int64  \n",
-      " 5   yr_built     21597 non-null  int64  \n",
-      " 6   sell_yr      21597 non-null  int64  \n",
-      " 7   house_age    21597 non-null  int64  \n",
-      "dtypes: float64(2), int64(6)\n",
-      "memory usage: 1.3 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",

From 9a13fdc738c3b9d4503d3e747366b177a6f76813 Mon Sep 17 00:00:00 2001
From: Sandra Kiptum <100568848+Sandrakiptumm@users.noreply.github.com>
Date: Sun, 28 Apr 2024 15:41:34 +0300
Subject: [PATCH 18/25] Update student.ipynb

updating main json
---
 student.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 64a1b461..055a180a 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -906,8 +906,8 @@
     "nbconvert_exporter": "python",
     "pygments_lexer": "ipython3",
     "version": "3.11.5",
-    "feature": "data-preparation",
-    "main": true
+    "feature/data-preparation": "main"
+   
 }
 
  },

From 32ee66dcfacb98a831997b91eea0ffe5b29a8ac1 Mon Sep 17 00:00:00 2001
From: Sandra Kiptum <100568848+Sandrakiptumm@users.noreply.github.com>
Date: Sun, 28 Apr 2024 15:56:49 +0300
Subject: [PATCH 19/25] Update student.ipynb

---
 student.ipynb | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 055a180a..74572468 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -899,17 +899,16 @@
     "name": "ipython",
     "version": 3
 
-  {
-    "file_extension": ".py",
-    "mimetype": "text/x-python",
-    "name": "python",
-    "nbconvert_exporter": "python",
-    "pygments_lexer": "ipython3",
-    "version": "3.11.5",
-    "feature/data-preparation": "main"
-   
-}
-
+  },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5",
+  "feature/data-preparation":
+   "main"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

From ca3a1af0a138dcc0fc423c7e6f8321bd6ac24443 Mon Sep 17 00:00:00 2001
From: sandrakiptumm <sandrakiptumm@gmail.com>
Date: Sun, 28 Apr 2024 20:15:22 +0300
Subject: [PATCH 20/25] Reverting student.ipynb

---
 student.ipynb | 872 +-------------------------------------------------
 1 file changed, 3 insertions(+), 869 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 74572468..82ef194b 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -22,870 +22,6 @@
    "source": [
     "# Your code here - remember to use markdown cells for comments as well!"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "DATA PREPARATION"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import csv\n",
-    "import pandas as pd\n",
-    "import sqlite3\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.api import OLS\n",
-    "%matplotlib inline\n",
-    "\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.model_selection import cross_validate\n",
-    "from sklearn.preprocessing import LabelEncoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>...</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7129300520</td>\n",
-       "      <td>10/13/2014</td>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>5650</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NONE</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>98178</td>\n",
-       "      <td>47.5112</td>\n",
-       "      <td>-122.257</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>5650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6414100192</td>\n",
-       "      <td>12/9/2014</td>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>7242</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>NONE</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>1991.0</td>\n",
-       "      <td>98125</td>\n",
-       "      <td>47.7210</td>\n",
-       "      <td>-122.319</td>\n",
-       "      <td>1690</td>\n",
-       "      <td>7639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5631500400</td>\n",
-       "      <td>2/25/2015</td>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>NONE</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>770</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>98028</td>\n",
-       "      <td>47.7379</td>\n",
-       "      <td>-122.233</td>\n",
-       "      <td>2720</td>\n",
-       "      <td>8062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2487200875</td>\n",
-       "      <td>12/9/2014</td>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>NONE</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1050</td>\n",
-       "      <td>910.0</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>98136</td>\n",
-       "      <td>47.5208</td>\n",
-       "      <td>-122.393</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>5000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1954400510</td>\n",
-       "      <td>2/18/2015</td>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>8080</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>NONE</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>98074</td>\n",
-       "      <td>47.6168</td>\n",
-       "      <td>-122.045</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>7503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "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": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(\"data/kc_house_data.csv\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
-      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
-      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
-      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.columns)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "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": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#checking null values\n",
-    "df.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\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": [
-    "#checking on data information\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2.159700e+04</td>\n",
-       "      <td>2.159700e+04</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>2.159700e+04</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>17755.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "      <td>21597.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.580474e+09</td>\n",
-       "      <td>5.402966e+05</td>\n",
-       "      <td>3.373200</td>\n",
-       "      <td>2.115826</td>\n",
-       "      <td>2080.321850</td>\n",
-       "      <td>1.509941e+04</td>\n",
-       "      <td>1.494096</td>\n",
-       "      <td>1788.596842</td>\n",
-       "      <td>1970.999676</td>\n",
-       "      <td>83.636778</td>\n",
-       "      <td>98077.951845</td>\n",
-       "      <td>47.560093</td>\n",
-       "      <td>-122.213982</td>\n",
-       "      <td>1986.620318</td>\n",
-       "      <td>12758.283512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.876736e+09</td>\n",
-       "      <td>3.673681e+05</td>\n",
-       "      <td>0.926299</td>\n",
-       "      <td>0.768984</td>\n",
-       "      <td>918.106125</td>\n",
-       "      <td>4.141264e+04</td>\n",
-       "      <td>0.539683</td>\n",
-       "      <td>827.759761</td>\n",
-       "      <td>29.375234</td>\n",
-       "      <td>399.946414</td>\n",
-       "      <td>53.513072</td>\n",
-       "      <td>0.138552</td>\n",
-       "      <td>0.140724</td>\n",
-       "      <td>685.230472</td>\n",
-       "      <td>27274.441950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000102e+06</td>\n",
-       "      <td>7.800000e+04</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>370.000000</td>\n",
-       "      <td>5.200000e+02</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>370.000000</td>\n",
-       "      <td>1900.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98001.000000</td>\n",
-       "      <td>47.155900</td>\n",
-       "      <td>-122.519000</td>\n",
-       "      <td>399.000000</td>\n",
-       "      <td>651.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.123049e+09</td>\n",
-       "      <td>3.220000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.750000</td>\n",
-       "      <td>1430.000000</td>\n",
-       "      <td>5.040000e+03</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1190.000000</td>\n",
-       "      <td>1951.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98033.000000</td>\n",
-       "      <td>47.471100</td>\n",
-       "      <td>-122.328000</td>\n",
-       "      <td>1490.000000</td>\n",
-       "      <td>5100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.904930e+09</td>\n",
-       "      <td>4.500000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.250000</td>\n",
-       "      <td>1910.000000</td>\n",
-       "      <td>7.618000e+03</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>1560.000000</td>\n",
-       "      <td>1975.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98065.000000</td>\n",
-       "      <td>47.571800</td>\n",
-       "      <td>-122.231000</td>\n",
-       "      <td>1840.000000</td>\n",
-       "      <td>7620.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>7.308900e+09</td>\n",
-       "      <td>6.450000e+05</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>2550.000000</td>\n",
-       "      <td>1.068500e+04</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2210.000000</td>\n",
-       "      <td>1997.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98118.000000</td>\n",
-       "      <td>47.678000</td>\n",
-       "      <td>-122.125000</td>\n",
-       "      <td>2360.000000</td>\n",
-       "      <td>10083.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>9.900000e+09</td>\n",
-       "      <td>7.700000e+06</td>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>13540.000000</td>\n",
-       "      <td>1.651359e+06</td>\n",
-       "      <td>3.500000</td>\n",
-       "      <td>9410.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>98199.000000</td>\n",
-       "      <td>47.777600</td>\n",
-       "      <td>-121.315000</td>\n",
-       "      <td>6210.000000</td>\n",
-       "      <td>871200.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
-       "count  2.159700e+04  2.159700e+04  21597.000000  21597.000000  21597.000000   \n",
-       "mean   4.580474e+09  5.402966e+05      3.373200      2.115826   2080.321850   \n",
-       "std    2.876736e+09  3.673681e+05      0.926299      0.768984    918.106125   \n",
-       "min    1.000102e+06  7.800000e+04      1.000000      0.500000    370.000000   \n",
-       "25%    2.123049e+09  3.220000e+05      3.000000      1.750000   1430.000000   \n",
-       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
-       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
-       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
-       "\n",
-       "           sqft_lot        floors    sqft_above      yr_built  yr_renovated  \\\n",
-       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  17755.000000   \n",
-       "mean   1.509941e+04      1.494096   1788.596842   1970.999676     83.636778   \n",
-       "std    4.141264e+04      0.539683    827.759761     29.375234    399.946414   \n",
-       "min    5.200000e+02      1.000000    370.000000   1900.000000      0.000000   \n",
-       "25%    5.040000e+03      1.000000   1190.000000   1951.000000      0.000000   \n",
-       "50%    7.618000e+03      1.500000   1560.000000   1975.000000      0.000000   \n",
-       "75%    1.068500e+04      2.000000   2210.000000   1997.000000      0.000000   \n",
-       "max    1.651359e+06      3.500000   9410.000000   2015.000000   2015.000000   \n",
-       "\n",
-       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
-       "count  21597.000000  21597.000000  21597.000000   21597.000000   21597.000000  \n",
-       "mean   98077.951845     47.560093   -122.213982    1986.620318   12758.283512  \n",
-       "std       53.513072      0.138552      0.140724     685.230472   27274.441950  \n",
-       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
-       "25%    98033.000000     47.471100   -122.328000    1490.000000    5100.000000  \n",
-       "50%    98065.000000     47.571800   -122.231000    1840.000000    7620.000000  \n",
-       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
-       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#statistical summary\n",
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Statistical summary observation\n",
-    "count for each column is 21597 this shows that we dont have missing values\n",
-    "The mean value of the house price is USD 540297 while the minimum house price is USD 78000 and maximum house price is USD 7700000\n",
-    "The standard deviation of the house price stands at USD 367368."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>yr_built</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10/13/2014</td>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12/9/2014</td>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2/25/2015</td>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>12/9/2014</td>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>Very Good</td>\n",
-       "      <td>1965</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2/18/2015</td>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>1987</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         date     price  bedrooms  bathrooms  sqft_living  condition  yr_built\n",
-       "0  10/13/2014  221900.0         3       1.00         1180    Average      1955\n",
-       "1   12/9/2014  538000.0         3       2.25         2570    Average      1951\n",
-       "2   2/25/2015  180000.0         2       1.00          770    Average      1933\n",
-       "3   12/9/2014  604000.0         4       3.00         1960  Very Good      1965\n",
-       "4   2/18/2015  510000.0         3       2.00         1680    Average      1987"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#we'll focus on the columns mentioned above and drop the rest\n",
-    "df.drop(columns=['id','lat','long','sqft_lot','floors','waterfront','view','zipcode','sqft_living15','sqft_lot15','grade','yr_renovated','sqft_above','sqft_basement'],inplace=True)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "date           0\n",
-       "price          0\n",
-       "bedrooms       0\n",
-       "bathrooms      0\n",
-       "sqft_living    0\n",
-       "condition      0\n",
-       "yr_built       0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#checking for for null values\n",
-    "df.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -898,16 +34,14 @@
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
-
-  },
+   },
+   "feature/data-preparation": "main",
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5",
-  "feature/data-preparation":
-   "main"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

From b776935908faf95b524d4aaa36cea14334beab8f Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Sun, 28 Apr 2024 20:27:51 +0300
Subject: [PATCH 21/25] Update student.ipynb

---
 student.ipynb | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/student.ipynb b/student.ipynb
index c2d72a52..dcd5adbb 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -63,6 +63,10 @@
     "\n",
     "**The King County House Sales dataset contains the following columns;**\n",
     "\n",
+    "id - unique identified for a house\n",
+    "\n",
+    "date - Date house was sold \n",
+    "\n",
     "Price - Sale price (prediction target)\n",
     "\n",
     "bedrooms - Number of bedrooms,\n",

From f5f282a63536e227e6ad900d76b9885efc752b83 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Thu, 2 May 2024 13:00:27 +0300
Subject: [PATCH 22/25] Update student.ipynb

---
 student.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/student.ipynb b/student.ipynb
index dcd5adbb..e5cca6b8 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -31,7 +31,8 @@
     "\n",
     "The real estate market plays a crucial role in the economic health and stability of a region. Understanding the factors that influence house prices is essential for both buyers and sellers to navigate the market effectively. This project focuses on a specific northwestern county in the United States, aiming to shed light on the key determinants of property valuation in this area.\n",
     "   ##### Problem Statements:\n",
-    "What are the most significant factors influencing house prices in this northwestern county?How can we quantify the relationship between these factors and property value?Can we develop a reliable model to predict house prices based on relevant characteristics?\n",
+    "We want to find out what makes houses expensive in a certain county in the northwest US. We'll also look at ways to measure how much these things like number of bedrooms or location affect the price. Finally, we'll see if we can build a tool to predict house prices based on these important features.\n",
+    "\n",
     "   ##### Challenges:\n",
     "* Real estate data can be complex and multifaceted, encompassing various property features and local market trends.\n",
     "* Accurately identifying and quantifying the relative impact of each factor on house prices can be challenging.\n",

From 7a14aa6d5336297a947f1b4edcb9d4a4e148919c Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Thu, 2 May 2024 14:24:45 +0300
Subject: [PATCH 23/25] updating business understanding

---
 student.ipynb | 39 ++++++++++++++++++---------------------
 1 file changed, 18 insertions(+), 21 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 681ea55f..26b0cfb4 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -30,28 +30,29 @@
     "   ### <li>**Business Understanding**\n",
     "\n",
     "The real estate market plays a crucial role in the economic health and stability of a region. Understanding the factors that influence house prices is essential for both buyers and sellers to navigate the market effectively. This project focuses on a specific northwestern county in the United States, aiming to shed light on the key determinants of property valuation in this area.\n",
-    "   ##### Problem Statements:\n",
+    "\n",
+    "   ##### Challenges of a Fluctuating Real Estate Market:\n",
+    "* Market fluctuations make it difficult for real estate agents to price houses and guide clients on offers.\n",
+    "* Rapid price fluctuations create a challenging environment for homebuyers, making it difficult to secure a good deal and avoid overpaying.\n",
+    "* Trying to pick the perfect moment to sell a house for maximum profit feels like playing the lottery – stressful, unpredictable, and with slim odds of success.\n",
+    "* High land prices and buyers struggling to afford homes make it difficult for builders to build new houses.\n",
+    "\n",
+    "   \n",
+    "##### Problem Statements:\n",
     "We want to find out what makes houses expensive in a certain county in the northwest US. We'll also look at ways to measure how much these things like number of bedrooms or location affect the price. Finally, we'll see if we can build a tool to predict house prices based on these important features.\n",
     "\n",
-    "   ##### Challenges:\n",
-    "* Real estate data can be complex and multifaceted, encompassing various property features and local market trends.\n",
-    "* Accurately identifying and quantifying the relative impact of each factor on house prices can be challenging.\n",
-    "* External factors like economic conditions and interest rates might also influence prices, requiring careful consideration.\n",
+    "   ##### Conclusion\n",
+    " Our study looked at how the ups and downs of the housing market in a northwestern county are making things tough for everyone involved. To help out, we're building a tool to predict house prices. This will give real estate agents valuable information so they can give their clients the best advice in this unpredictable market.\n",
     "\n",
-    "##### Proposed Solutions:\n",
+    "   ##### Proposed Solutions:\n",
     "We propose utilizing multiple linear regression, a powerful machine learning technique. This method allows us to analyze a large dataset of house sales and identify the statistical relationships between various property features (e.g., square footage, number of bedrooms, location) and the corresponding sale prices.\n",
+    "\n",
+    "\n",
     "   ##### Objectives:\n",
     "1. Develop a robust multiple linear regression model that accurately predicts house prices in the chosen northwestern county.\n",
     "2. Identify the most significant factors influencing property value within this specific market.\n",
     "3. Provide valuable insights into the housing market dynamics of the region, benefiting potential buyers, real estate agents, and other stakeholders.\n",
-    "   \n",
-    " \n",
-    "**Research questions that would help to achieve the objectives**:\n",
-    "\n",
-    "1. How does the number of bedrooms, bathrooms, grade and square footage of a house correlate with its sale price in King County?\n",
-    "2. How much can a homeowner expect the value of their home to increase after a specific renovation project?\n",
-    "3. Which renovation projects have the most significant impact on a home's market value in the northwestern county?\n",
-    "4. Are there specific combinations of renovation projects that provide an interdependent effect on a home's market value?"
+    "   "
    ]
   },
   {
@@ -60,7 +61,8 @@
    "source": [
     "   ### <li> **Data Understanding**\n",
     "\n",
-    "Our analysis leverages the King County House Sales dataset - a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n",
+    "Our analysis leverages the King County House Sales dataset.This information is stored in a file called \"kc_house_data.csv\".\n",
+    " It's a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n",
     "\n",
     "**The King County House Sales dataset contains the following columns;**\n",
     "\n",
@@ -103,12 +105,7 @@
     "sell_yr - Date house was sold.\n",
     "\n",
     "\n",
-    "We need to be aware of certain constraints within the data, as these might influence our analysis and interpretation of the results. From the sources;\n",
-    "\n",
-    "1. The data may contain anomalies or inconsistencies that require careful examination during analysis. For instance, a record lists a house with 33 bedrooms, which appears to be an outlier\n",
-    "\n",
-    "2. It's important to consider the time frame of the data (May 2014 - May 2015) as it may not fully capture the current market dynamics in King County.\n",
-    "3. It's important to acknowledge the scope of the data. While it provides details on house features, it may not capture external factors such as interest rates or the overall economic climate, which can also play a role in determining property values."
+    "\n"
    ]
   },
   {

From a1ab02ca4716f962b9264aafc3a1e78af92c6507 Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Thu, 2 May 2024 14:58:36 +0300
Subject: [PATCH 24/25] Data understanding

---
 student.ipynb | 368 +++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 351 insertions(+), 17 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 26b0cfb4..7e236fa4 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -62,8 +62,355 @@
     "   ### <li> **Data Understanding**\n",
     "\n",
     "Our analysis leverages the King County House Sales dataset.This information is stored in a file called \"kc_house_data.csv\".\n",
-    " It's a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n",
+    " It's a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the data\n",
+    "data = pd.read_csv('data/kc_house_data.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataframe Info:\n",
+      "<class 'pandas.core.frame.DataFrame'>\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",
+      "(21597, 21)\n",
+      "\n",
+      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
+      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
+      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
+      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
+      "      dtype='object')\n",
+      "\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",
+      "dtype: object\n",
+      "\n",
+      "None\n",
+      "\n",
+      "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
+      "count  2.159700e+04  2.159700e+04  21597.000000  21597.000000  21597.000000   \n",
+      "mean   4.580474e+09  5.402966e+05      3.373200      2.115826   2080.321850   \n",
+      "std    2.876736e+09  3.673681e+05      0.926299      0.768984    918.106125   \n",
+      "min    1.000102e+06  7.800000e+04      1.000000      0.500000    370.000000   \n",
+      "25%    2.123049e+09  3.220000e+05      3.000000      1.750000   1430.000000   \n",
+      "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
+      "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
+      "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
+      "\n",
+      "           sqft_lot        floors    sqft_above      yr_built  yr_renovated  \\\n",
+      "count  2.159700e+04  21597.000000  21597.000000  21597.000000  17755.000000   \n",
+      "mean   1.509941e+04      1.494096   1788.596842   1970.999676     83.636778   \n",
+      "std    4.141264e+04      0.539683    827.759761     29.375234    399.946414   \n",
+      "min    5.200000e+02      1.000000    370.000000   1900.000000      0.000000   \n",
+      "25%    5.040000e+03      1.000000   1190.000000   1951.000000      0.000000   \n",
+      "50%    7.618000e+03      1.500000   1560.000000   1975.000000      0.000000   \n",
+      "75%    1.068500e+04      2.000000   2210.000000   1997.000000      0.000000   \n",
+      "max    1.651359e+06      3.500000   9410.000000   2015.000000   2015.000000   \n",
+      "\n",
+      "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
+      "count  21597.000000  21597.000000  21597.000000   21597.000000   21597.000000  \n",
+      "mean   98077.951845     47.560093   -122.213982    1986.620318   12758.283512  \n",
+      "std       53.513072      0.138552      0.140724     685.230472   27274.441950  \n",
+      "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
+      "25%    98033.000000     47.471100   -122.328000    1490.000000    5100.000000  \n",
+      "50%    98065.000000     47.571800   -122.231000    1840.000000    7620.000000  \n",
+      "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
+      "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def dataset_info(file_path):\n",
+    "  data = pd.read_csv(file_path)\n",
+    "  print(\"Dataframe Info:\")\n",
+    "  print(data.shape, data.columns, data.dtypes, data.info(), data.describe(), sep=\"\\n\\n\")\n",
     "\n",
+    "dataset_info('data/kc_house_data.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Data Buckets**\n",
+    "\n",
+    "There are 6 categorical columns, there are 12 numeric columns and 3 columns that contain temporal data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "**The King County House Sales dataset contains the following columns;**\n",
     "\n",
     "id - unique identified for a house\n",
@@ -102,34 +449,21 @@
     "\n",
     "sqft_lot15 - The square footage of the land lots of the nearest 15 neighbors, and\n",
     "\n",
-    "sell_yr - Date house was sold.\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "sell_yr - Date house was sold."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (learn-env)",
+   "display_name": "learn-env",
    "language": "python",
-   "name": "learn-env"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "feature/data-preparation": "main",
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",

From c18538a0d7b7cdfc84d56ec76f040d6b735b0dfb Mon Sep 17 00:00:00 2001
From: Calvine Dasilver <162190387+Cdasilver29@users.noreply.github.com>
Date: Thu, 2 May 2024 20:02:18 +0300
Subject: [PATCH 25/25] update

---
 student.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/student.ipynb b/student.ipynb
index 7e236fa4..139e4f02 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -404,7 +404,9 @@
    "source": [
     "**Data Buckets**\n",
     "\n",
-    "There are 6 categorical columns, there are 12 numeric columns and 3 columns that contain temporal data."
+    "* We have 6 categories for descriptive information like location(categorical data).\n",
+    "* There are 12 columns with numerical values like square footage and bedrooms(numeric data).\n",
+    "* Three columns contain details related to time like year built(temporal data).\n"
    ]
   },
   {