student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Project Submission\n",
    "\n",
    "Please fill out:\n",
    "* Group members: Myra Kadenge, Edmund Nyaribo ,Frank Oyugi, Silvia Gworit\n",
    "* Student pace: Full time Hybrid\n",
    "* Scheduled project review date/time: \n",
    "* Instructor name: Maryann Mwikali\n",
    "* Blog post URL:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "The real estate market in King County is characterized by its dynamic nature, driven by factors such as population growth, economic trends, and housing demand. The county's diverse geography offers a wide range of housing options, from  townhouses to suburban single-family homes and waterfront properties. The King County Housing Data Set provides information about houses in King County, including their size, location, condition, and other features. The objective of this project is to create multiple regression models that can accurately predict the price of a house.\n",
    "\n",
    "The project is commissioned by a commercial real estate agaency interested in assisting homeowners to buy or sell homes within specific areas. Their main concerns revolve around  obtaining predicted prices for homes in the area based on renovation and further assessing any notable disparities between the actual prices and our model's predicted prices.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding\n",
    "\n",
    "The primary goal of the client is to create a platform that delivers accurate estimates of house prices which are crucial for both buyers and sellers in King county. To achieve this objective we need to develop a model that can identify the key factors influencing house prices. To effectively train this model the client requires precise and representative data related to the real estate market in king county, including historical sales, current listings, property size, and other relevant features.\n",
    "\n",
    "Once trained successfully the model will be capable of providing accurate estimations of house values based on their features. It can then seamlessly integrate into the platform offering reliable price estimates to both buyers and sellers. \n",
    "\n",
    "This information derived from the model will empower buyers and sellers to make informed decisions regarding property transactions.\n",
    "By leveraging technology to provide a dependable and precise platform for buying and selling houses in king county, the client aims to establish themselves as a trusted authority in the local real estate market thus attracting a loyal customer base.\n",
    "\n",
    "QUESTIONS\n",
    "1. What feature has the highest correlation to the home price?\n",
    "\n",
    "2. Does the view from a home increase its estmated value?\n",
    "\n",
    "3. What combinations of features leads to the highest values of homes?\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data understanding\n",
    "For this project, we will be using the King County House Sales dataset, which contains  20 columns and over 21500 records, covering house sales between May 2014 and May 2015. The data is suitable for the project as it provides relevant information about the features that affect the house prices in King County.\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",
    "\n",
    "Some limitations of the data that may have implications for the project are:\n",
    "\n",
    "The data may not reflect the current market conditions as it was collected from 2014 to 2015.\n",
    "\n",
    "The data may not capture all the factors that affect the house prices such as the interest rates and the economic situation.\n",
    "\n",
    "The data may have some outliers or errors that need to be handled carefully during the analysis.\n",
    " For example, there is a house with 33 bedrooms which seems unrealistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n",
    "\n",
    "In our data preparation process, we imported the necessary libraries, loaded the data, then proceeded to clean the data - dealing with null values and transforming the data as needed. Afterward, we visualized the data to identify correlations between the variables"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score,  mean_absolute_error\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "  <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>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</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",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7129300520</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>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</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",
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       "    <tr>\n",
       "      <th>6414100192</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>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</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>5631500400</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>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</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",
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       "    <tr>\n",
       "      <th>2487200875</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>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Very Good</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>1954400510</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>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
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       "      <td>Average</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",
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      "text/plain": [
       "                  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "id                                                                             \n",
       "7129300520  10/13/2014  221900.0         3       1.00         1180      5650   \n",
       "6414100192   12/9/2014  538000.0         3       2.25         2570      7242   \n",
       "5631500400   2/25/2015  180000.0         2       1.00          770     10000   \n",
       "2487200875   12/9/2014  604000.0         4       3.00         1960      5000   \n",
       "1954400510   2/18/2015  510000.0         3       2.00         1680      8080   \n",
       "\n",
       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
       "id                                                                          \n",
       "7129300520     1.0        NaN  NONE    Average      7 Average        1180   \n",
       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
       "\n",
       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "id                                                                            \n",
       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
       "6414100192         400.0      1951        1991.0    98125  47.7210 -122.319   \n",
       "5631500400           0.0      1933           NaN    98028  47.7379 -122.233   \n",
       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
       "\n",
       "            sqft_living15  sqft_lot15  \n",
       "id                                     \n",
       "7129300520           1340        5650  \n",
       "6414100192           1690        7639  \n",
       "5631500400           2720        8062  \n",
       "2487200875           1360        5000  \n",
       "1954400510           1800        7503  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "data=pd.read_csv('data/kc_house_data.csv', index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></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",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
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       "    <tr>\n",
       "      <th>mean</th>\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>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>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>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>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>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>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",
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      ],
      "text/plain": [
       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  2.159700e+04   \n",
       "mean   5.402966e+05      3.373200      2.115826   2080.321850  1.509941e+04   \n",
       "std    3.673681e+05      0.926299      0.768984    918.106125  4.141264e+04   \n",
       "min    7.800000e+04      1.000000      0.500000    370.000000  5.200000e+02   \n",
       "25%    3.220000e+05      3.000000      1.750000   1430.000000  5.040000e+03   \n",
       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068500e+04   \n",
       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
       "\n",
       "             floors    sqft_above      yr_built  yr_renovated       zipcode  \\\n",
       "count  21597.000000  21597.000000  21597.000000  17755.000000  21597.000000   \n",
       "mean       1.494096   1788.596842   1970.999676     83.636778  98077.951845   \n",
       "std        0.539683    827.759761     29.375234    399.946414     53.513072   \n",
       "min        1.000000    370.000000   1900.000000      0.000000  98001.000000   \n",
       "25%        1.000000   1190.000000   1951.000000      0.000000  98033.000000   \n",
       "50%        1.500000   1560.000000   1975.000000      0.000000  98065.000000   \n",
       "75%        2.000000   2210.000000   1997.000000      0.000000  98118.000000   \n",
       "max        3.500000   9410.000000   2015.000000   2015.000000  98199.000000   \n",
       "\n",
       "                lat          long  sqft_living15     sqft_lot15  \n",
       "count  21597.000000  21597.000000   21597.000000   21597.000000  \n",
       "mean      47.560093   -122.213982    1986.620318   12758.283512  \n",
       "std        0.138552      0.140724     685.230472   27274.441950  \n",
       "min       47.155900   -122.519000     399.000000     651.000000  \n",
       "25%       47.471100   -122.328000    1490.000000    5100.000000  \n",
       "50%       47.571800   -122.231000    1840.000000    7620.000000  \n",
       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max       47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21597 entries, 7129300520 to 1523300157\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           21597 non-null  object \n",
      " 1   price          21597 non-null  float64\n",
      " 2   bedrooms       21597 non-null  int64  \n",
      " 3   bathrooms      21597 non-null  float64\n",
      " 4   sqft_living    21597 non-null  int64  \n",
      " 5   sqft_lot       21597 non-null  int64  \n",
      " 6   floors         21597 non-null  float64\n",
      " 7   waterfront     19221 non-null  object \n",
      " 8   view           21534 non-null  object \n",
      " 9   condition      21597 non-null  object \n",
      " 10  grade          21597 non-null  object \n",
      " 11  sqft_above     21597 non-null  int64  \n",
      " 12  sqft_basement  21597 non-null  object \n",
      " 13  yr_built       21597 non-null  int64  \n",
      " 14  yr_renovated   17755 non-null  float64\n",
      " 15  zipcode        21597 non-null  int64  \n",
      " 16  lat            21597 non-null  float64\n",
      " 17  long           21597 non-null  float64\n",
      " 18  sqft_living15  21597 non-null  int64  \n",
      " 19  sqft_lot15     21597 non-null  int64  \n",
      "dtypes: float64(6), int64(8), object(6)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 21597 entries and 20 columns. Most of the columns consist of numerical data which make it suitable for linear regression analysis.\n",
    "The only columns with missing values is the year renovated column, waterfront and view hence we will handle the  missing values later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing\n",
    "In this section, we will prepare the data for the analysis by converting some of the categorical features such as waterfront into numeric ones. This will allow us to apply mathematical operations and statistical methods on the data.\n",
    "\n",
    "We will also use pandas functions to convert the date feature from string to datetime format and extract only the year of sale. We will create a new column for sell year and drop the original date column.\n",
    "\n",
    "Moreover there will be truncating of the bedrooms column as we cannot have half a bedroom.\n",
    "And lastly handling of missing values if any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Waterfront' column transformation\n",
    "#data['waterfront'].replace(to_replace=['NO', 'YES'], value=[0, 1], inplace=True)\n",
    "data['waterfront'] = data['waterfront'].map({'NO': 0, 'YES': 1}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing the date to date time\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "# Extracting only the year from the column Date\n",
    "data.date = data['date'].dt.year\n",
    "\n",
    "# Creating a new column for sell year\n",
    "data['sell_yr'] = pd.to_datetime(data['date'],format='%Y').dt.year\n",
    "data.drop(columns='date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop unnecessary columns to reduce the lneght of interpretation in our model\n",
    "data.drop(columns=[ 'lat', 'long','sqft_lot15', 'zipcode', 'sqft_basement','sqft_living15', 'sqft_above'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Truncate the bedrooms bathrooms and floors columns as we cannot have 1/2 a bedroom, bathroom or floor.\n",
    "def truncate_columns(data, columns):\n",
    "    \n",
    "    for column in columns:\n",
    "        data[column] = data[column].apply(lambda x: int(x))\n",
    "    \n",
    "    return data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "truncated_data= truncate_columns(data, ['bedrooms', 'bathrooms', 'floors'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "id                                                                          \n",
       "7129300520   221900.0         3          1         1180      5650       1   \n",
       "6414100192   538000.0         3          2         2570      7242       2   \n",
       "5631500400   180000.0         2          1          770     10000       1   \n",
       "2487200875   604000.0         4          3         1960      5000       1   \n",
       "1954400510   510000.0         3          2         1680      8080       1   \n",
       "7237550310  1230000.0         4          4         5420    101930       1   \n",
       "1321400060   257500.0         3          2         1715      6819       2   \n",
       "2008000270   291850.0         3          1         1060      9711       1   \n",
       "2414600126   229500.0         3          1         1780      7470       1   \n",
       "3793500160   323000.0         3          2         1890      6560       2   \n",
       "1736800520   662500.0         3          2         3560      9796       1   \n",
       "9212900260   468000.0         2          1         1160      6000       1   \n",
       "114101516    310000.0         3          1         1430     19901       1   \n",
       "6054650070   400000.0         3          1         1370      9680       1   \n",
       "1175000570   530000.0         5          2         1810      4850       1   \n",
       "9297300055   650000.0         4          3         2950      5000       2   \n",
       "1875500060   395000.0         3          2         1890     14040       2   \n",
       "6865200140   485000.0         4          1         1600      4300       1   \n",
       "16000397     189000.0         2          1         1200      9850       1   \n",
       "7983200060   230000.0         3          1         1250      9774       1   \n",
       "\n",
       "            waterfront  view  condition          grade  yr_built  \\\n",
       "id                                                                 \n",
       "7129300520         NaN  NONE    Average      7 Average      1955   \n",
       "6414100192         0.0  NONE    Average      7 Average      1951   \n",
       "5631500400         0.0  NONE    Average  6 Low Average      1933   \n",
       "2487200875         0.0  NONE  Very Good      7 Average      1965   \n",
       "1954400510         0.0  NONE    Average         8 Good      1987   \n",
       "7237550310         0.0  NONE    Average   11 Excellent      2001   \n",
       "1321400060         0.0  NONE    Average      7 Average      1995   \n",
       "2008000270         0.0   NaN    Average      7 Average      1963   \n",
       "2414600126         0.0  NONE    Average      7 Average      1960   \n",
       "3793500160         0.0  NONE    Average      7 Average      2003   \n",
       "1736800520         NaN  NONE    Average         8 Good      1965   \n",
       "9212900260         0.0  NONE       Good      7 Average      1942   \n",
       "114101516          0.0  NONE       Good      7 Average      1927   \n",
       "6054650070         0.0  NONE       Good      7 Average      1977   \n",
       "1175000570         0.0  NONE    Average      7 Average      1900   \n",
       "9297300055         0.0  GOOD    Average       9 Better      1979   \n",
       "1875500060         0.0  NONE    Average      7 Average      1994   \n",
       "6865200140         0.0  NONE       Good      7 Average      1916   \n",
       "16000397           0.0  NONE       Good      7 Average      1921   \n",
       "7983200060         0.0  NONE       Good      7 Average      1969   \n",
       "\n",
       "            yr_renovated  sell_yr  \n",
       "id                                 \n",
       "7129300520           0.0     2014  \n",
       "6414100192        1991.0     2014  \n",
       "5631500400           NaN     2015  \n",
       "2487200875           0.0     2014  \n",
       "1954400510           0.0     2015  \n",
       "7237550310           0.0     2014  \n",
       "1321400060           0.0     2014  \n",
       "2008000270           0.0     2015  \n",
       "2414600126           0.0     2015  \n",
       "3793500160           0.0     2015  \n",
       "1736800520           0.0     2015  \n",
       "9212900260           0.0     2014  \n",
       "114101516            NaN     2014  \n",
       "6054650070           0.0     2014  \n",
       "1175000570           0.0     2015  \n",
       "9297300055           0.0     2015  \n",
       "1875500060           0.0     2014  \n",
       "6865200140           0.0     2014  \n",
       "16000397             0.0     2014  \n",
       "7983200060           0.0     2015  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for removing the Digits in the grade values\n",
    "def extract_grade_desc(s):\n",
    "    grade_desc = ''\n",
    "    for char in s:\n",
    "        if not char.isdigit() and char != ' ':\n",
    "            grade_desc += char\n",
    "    return grade_desc.strip()\n",
    "\n",
    "data['grade'] = data['grade'].apply(lambda x: extract_grade_desc(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling missing values\n",
    "In this section, we will check the percentage of missing values in each column of the data and handle them accordingly.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check for missing values in a DataFrame and return a Data Frameshowing the percentage of missing values for each column.\n",
    "    \n",
    "def check_missing_values(data):\n",
    "    \n",
    "    # Check for missing values\n",
    "    missing_values = (data.isnull().sum())/len(data) * 100\n",
    "    \n",
    "    # Create a DataFrame to display the count of missing values\n",
    "    missing_df = pd.DataFrame({'Missing Values': missing_values})\n",
    "    \n",
    "    return missing_df\n",
    "missing_df = check_missing_values(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>11.001528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0.291707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>17.789508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell_yr</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing Values\n",
       "price               0.000000\n",
       "bedrooms            0.000000\n",
       "bathrooms           0.000000\n",
       "sqft_living         0.000000\n",
       "sqft_lot            0.000000\n",
       "floors              0.000000\n",
       "waterfront         11.001528\n",
       "view                0.291707\n",
       "condition           0.000000\n",
       "grade               0.000000\n",
       "yr_built            0.000000\n",
       "yr_renovated       17.789508\n",
       "sell_yr             0.000000"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentages of missing values are not too high. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill missing values of view with median and waterfront with 0\n",
    "data['waterfront'].fillna(0, inplace=True)\n",
    "\n",
    "#drop remaing misisng values in other columns\n",
    "data.dropna(axis=0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17704 entries, 7129300520 to 1523300157\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   price         17704 non-null  float64\n",
      " 1   bedrooms      17704 non-null  int64  \n",
      " 2   bathrooms     17704 non-null  int64  \n",
      " 3   sqft_living   17704 non-null  int64  \n",
      " 4   sqft_lot      17704 non-null  int64  \n",
      " 5   floors        17704 non-null  int64  \n",
      " 6   waterfront    17704 non-null  float64\n",
      " 7   view          17704 non-null  object \n",
      " 8   condition     17704 non-null  object \n",
      " 9   grade         17704 non-null  object \n",
      " 10  yr_built      17704 non-null  int64  \n",
      " 11  yr_renovated  17704 non-null  float64\n",
      " 12  sell_yr       17704 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(3)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We chose to fill in the missing values of waterfront since it has the highest percentage. We filled in the missing values with 0 meaning the houses are not on waterfronts because if we drop the rows we would end up working with about 15000 entries of data which would be a huge decrease of data from having 21597 entries of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
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       "    <tr>\n",
       "      <th>1321400060</th>\n",
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       "    <tr>\n",
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       "      <td>1780</td>\n",
       "      <td>7470</td>\n",
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       "      <th>3793500160</th>\n",
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       "      <td>1890</td>\n",
       "      <td>6560</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1160</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Good</td>\n",
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       "      <td>2014</td>\n",
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       "      <td>1370</td>\n",
       "      <td>9680</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>Average</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
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       "    <tr>\n",
       "      <th>1175000570</th>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1810</td>\n",
       "      <td>4850</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Average</td>\n",
       "      <td>1900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9297300055</th>\n",
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       "      <td>4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875500060</th>\n",
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       "      <td>1890</td>\n",
       "      <td>14040</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Average</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6865200140</th>\n",
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       "      <td>1</td>\n",
       "      <td>1600</td>\n",
       "      <td>4300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>Average</td>\n",
       "      <td>1916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16000397</th>\n",
       "      <td>189000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>9850</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>Average</td>\n",
       "      <td>1921</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7983200060</th>\n",
       "      <td>230000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1250</td>\n",
       "      <td>9774</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>Average</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6300500875</th>\n",
       "      <td>385000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1620</td>\n",
       "      <td>4980</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>Average</td>\n",
       "      <td>1947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524049179</th>\n",
       "      <td>2000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3050</td>\n",
       "      <td>44867</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EXCELLENT</td>\n",
       "      <td>Average</td>\n",
       "      <td>Better</td>\n",
       "      <td>1968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7137970340</th>\n",
       "      <td>285000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2270</td>\n",
       "      <td>6300</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Good</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "id                                                                          \n",
       "7129300520   221900.0         3          1         1180      5650       1   \n",
       "6414100192   538000.0         3          2         2570      7242       2   \n",
       "2487200875   604000.0         4          3         1960      5000       1   \n",
       "1954400510   510000.0         3          2         1680      8080       1   \n",
       "7237550310  1230000.0         4          4         5420    101930       1   \n",
       "1321400060   257500.0         3          2         1715      6819       2   \n",
       "2414600126   229500.0         3          1         1780      7470       1   \n",
       "3793500160   323000.0         3          2         1890      6560       2   \n",
       "1736800520   662500.0         3          2         3560      9796       1   \n",
       "9212900260   468000.0         2          1         1160      6000       1   \n",
       "6054650070   400000.0         3          1         1370      9680       1   \n",
       "1175000570   530000.0         5          2         1810      4850       1   \n",
       "9297300055   650000.0         4          3         2950      5000       2   \n",
       "1875500060   395000.0         3          2         1890     14040       2   \n",
       "6865200140   485000.0         4          1         1600      4300       1   \n",
       "16000397     189000.0         2          1         1200      9850       1   \n",
       "7983200060   230000.0         3          1         1250      9774       1   \n",
       "6300500875   385000.0         4          1         1620      4980       1   \n",
       "2524049179  2000000.0         3          2         3050     44867       1   \n",
       "7137970340   285000.0         5          2         2270      6300       2   \n",
       "\n",
       "            waterfront       view  condition      grade  yr_built  \\\n",
       "id                                                                  \n",
       "7129300520         0.0       NONE    Average    Average      1955   \n",
       "6414100192         0.0       NONE    Average    Average      1951   \n",
       "2487200875         0.0       NONE  Very Good    Average      1965   \n",
       "1954400510         0.0       NONE    Average       Good      1987   \n",
       "7237550310         0.0       NONE    Average  Excellent      2001   \n",
       "1321400060         0.0       NONE    Average    Average      1995   \n",
       "2414600126         0.0       NONE    Average    Average      1960   \n",
       "3793500160         0.0       NONE    Average    Average      2003   \n",
       "1736800520         0.0       NONE    Average       Good      1965   \n",
       "9212900260         0.0       NONE       Good    Average      1942   \n",
       "6054650070         0.0       NONE       Good    Average      1977   \n",
       "1175000570         0.0       NONE    Average    Average      1900   \n",
       "9297300055         0.0       GOOD    Average     Better      1979   \n",
       "1875500060         0.0       NONE    Average    Average      1994   \n",
       "6865200140         0.0       NONE       Good    Average      1916   \n",
       "16000397           0.0       NONE       Good    Average      1921   \n",
       "7983200060         0.0       NONE       Good    Average      1969   \n",
       "6300500875         0.0       NONE       Good    Average      1947   \n",
       "2524049179         0.0  EXCELLENT    Average     Better      1968   \n",
       "7137970340         0.0       NONE    Average       Good      1995   \n",
       "\n",
       "            yr_renovated  sell_yr  \n",
       "id                                 \n",
       "7129300520           0.0     2014  \n",
       "6414100192        1991.0     2014  \n",
       "2487200875           0.0     2014  \n",
       "1954400510           0.0     2015  \n",
       "7237550310           0.0     2014  \n",
       "1321400060           0.0     2014  \n",
       "2414600126           0.0     2015  \n",
       "3793500160           0.0     2015  \n",
       "1736800520           0.0     2015  \n",
       "9212900260           0.0     2014  \n",
       "6054650070           0.0     2014  \n",
       "1175000570           0.0     2015  \n",
       "9297300055           0.0     2015  \n",
       "1875500060           0.0     2014  \n",
       "6865200140           0.0     2014  \n",
       "16000397             0.0     2014  \n",
       "7983200060           0.0     2015  \n",
       "6300500875           0.0     2014  \n",
       "2524049179           0.0     2014  \n",
       "7137970340           0.0     2014  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the phase in the EDA(Exploratory Data Analysis) process where we examine summary statistics,visualize distributions and identify correlations between variables.Additionally we can also perform a linearity check at this stage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List Of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price\n",
      "bedrooms\n",
      "bathrooms\n",
      "sqft_living\n",
      "sqft_lot\n",
      "floors\n",
      "waterfront\n",
      "view\n",
      "condition\n",
      "grade\n",
      "yr_built\n",
      "yr_renovated\n",
      "sell_yr\n"
     ]
    }
   ],
   "source": [
    "columns_list = data.columns.tolist()\n",
    "for column_name in columns_list:\n",
    "    print(column_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List Of Numerical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors']\n"
     ]
    }
   ],
   "source": [
    "numerical_columns = data.select_dtypes(include=['number']).columns.tolist()\n",
    "columns_to_exclude = ['waterfront','yr_built','yr_renovated','sell_yr']\n",
    "\n",
    "for column_to_exclude in columns_to_exclude:\n",
    "    if column_to_exclude in numerical_columns:\n",
    "        numerical_columns.remove(column_to_exclude)\n",
    "print(numerical_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linearity Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check for linearity in order to determine which idependent variables have a linear relationship with the target variable,which is the price.This is important beacause when performing linear regression,the linearity assumption must be satisfied and cannot be violated.If this assumption is violated,the model may fail to accurately capture the true relationship between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JEw2es6FnYeLEiXj//feRm5sLlmVx5swZu0kqMDAQn332Gb744gt89dVXTs8xfPhw/PLLL/jzzz9hNBrxv//9D2KxGAkJCQ3KV1paip9//hm1tbUQi8WQy+UQCATcuF988QWKiopQVVWFVatW1TuWs/t7M4wYMQK///471q9fX68Jqj6GDRuGvXv34ujRozAYDPjggw/A1tNt4VaereHDh2PVqlWoqqpCUVERvvjiiwY/s2bNGlRVVaGwsBCff/45RowY0ahz1fccTpkyBe+99x5nESkvL8eePXsafR23SqtVFh4eHvjtt9+Qk5ODe+65BykpKbh8+TKUSiXy8vLQo0cPmEwmnD59Gnl5ebh8+TKee+45dOnSBbNnz0Z6ejr69euH7Oxs9OzZkxu3b9++6NKlC/r374+kpCQAwH333Yfz58+jV69eWLBgAQQCAVasWIEzZ85gyJAh6Nu3L1544QWo1eobuoZRo0bho48+4qJB3n77bafHvfjii5DJZEhJScHUqVMxatQoTJgwwaW8DbFgwQLExsZy8d0xMTFYsGDBDcl+M3h4eGDNmjXYtWsXBgwYgP79++Odd97hFN3LL7+MDz74AAkJCfjoo48wfPjwGxp/xowZ+PHHH9G7d2+8/vrr3Otjx45Fdna23Q7SmWz/+9//sG/fPtx9990YNmwYDh482OA5G3oWZs2aheHDh2P27Nno2bMnFi9eDL1ebzdGaGgoPvvsM6xevRqbNm1yOEfnzp3x9ttvY8mSJejbty/27duHlStXNipMnGEYrF27FgMGDECfPn1w6NAhvPzyywCASZMmoX///khLS8O4ceMwdOjQesdydX9vlMDAQMTHx+PYsWONnkTr0rVrV7z44ot44oknMGDAACgUCvj6+rq8J7fybD366KMIDQ3FkCFDMHv27HqfIytDhgzB+PHjMXbsWAwaNAj33Xdfo85V33M4Y8YMJCcnY/bs2UhISMCkSZNueIF6S7jNld5EDB48mD179izLspZIiqlTpzo97uTJk2xycrLda927d2fLysqaXcbGUDfShGg+/v77b3bgwIFcVBThnLrRSK0JtVrNRkdHs/n5+bdbFLtIsLZEq91ZAJYyE3l5eXax6SdPngTLsoiNjYVcLudMTIcOHYKXl5ddJArR9jEajfj8889x33332TkGidaPNaqqtrYWS5cuhUqlQvv27W+3WG2WVuPgfv311/HTTz+htLQUs2bNgre3N3bu3ImPP/4Yb7/9Nt58800YjUZ06NABK1euBJ/Px5tvvonnnnsOBoMBMpkMH374IWWj3kHk5ORgwoQJiIqKwltvvXW7xWlTHD58GPPmzXP6nrtKdvz88894+umnucXhu+++S7/vZoTHstSDmyAIgqgft+0s9u3bh/fffx8sy4JhGDz22GMNOtQIgiCIloFbdhYsy6JPnz746quvoFKpcObMGUyZMgVHjhxp0I7MMAw0Gg1EIhFtMQmCIBoJy7IwGo1QKBRN4q9z286Cz+ejpqYGgKUwXmBgYKMuQKPR2CUkEQRBEI1HpVJBqVTe8jhuURY8Hg/vvfceFixYALlcDo1Gg08++aRRnxWJRAAsF1w3hjorKwuxsbFNLq87aK2yk9zuheR2P61V9rpyGwwGZGdnc3PoreIWM5TJZMLcuXPx2GOPITExEUeOHMG///1v7Ny5EwqFot7P6vV6ZGVlNbeIBEEQbZLY2NhbqvVmxS07i9OnT6OkpISraZKYmAiZTIacnJxGd7VzdsFHjhy5bXVSbpXWKjvJ7V5IbvfTWmWvK3dTL7TdkqUUHByMoqIiXLhwAYAl/r20tLTeapwEQRBEy8EtO4uAgAC88sorWLhwIRfR9NZbb9VbUpggCIJoObgtGqq1NCUnCIIgHKFiOQRBENdgGBZCuR8yz1/FlRI1GIYKXFhpNbWhCIIgmhOGYfFnZiGWrc+C3miGRCTAoik9cVdcCPh8SgimnQVBEASAwlINlq0/Cr3RDADQG81Ytv4oCks1t1mylgEpC4IgCADl1VpOUVjRG80or9HeJolaFqQsCIIgAPh6yiARCexek4gE8FXKbpNELQtSFgRBEABC/BVYNKUnpzCsPosQ//qrTNwpkIObIAgCAJ/Pw11xIfAQx4IvVsBXKUOIv4Kc29cgZUEQBHENPp8HU20ZEqM73m5RWhxkhiIIgiAahJQFQRAE0SCkLAiCIIgGIWVBEARBNAgpC4IgCKJBSFkQBEEQDULKgiAIgmgQUhYEQRBEg5CyIAiiSaGeEG0Tt2RwX758GY888gj3d01NDdRqNf7++293nJ4gCDdBPSHaLm5RFu3bt8f27du5v9944w2YzeZ6PkEQRGvEVU+IjiGD0C7Q4zZLR9wKbjdDGQwGfP/995gwYYK7T00QRDNDPSHaLm5XFnv37kVQUBBiYmLcfWqCIJoZ6gnRduGxLOtW79O8efMwYMAAzJgxo1HH6/V6ZGVlNbNUBEHcCkKhEBB7QaNnIRZLsWpbFgrLaiERCfDw+G7wl9TAZDLdbjHvSGJjYyGRSG55HLeWKC8uLsahQ4fw3//+94Y/6+yCjxw5gsTExKYSz620VtlJbvfSGuTmnNrrjnJO7QUT4hAcoICXXNrqekK0hnvujLpyN/VC261mqK1bt2LgwIHw8fFx52kJgmhGnDm1P96cCS+5FO0CPVqVoiBc43ZlQY5tgmhbkFP7zsCtZqgff/zRnacjCMINWJ3atgqDnNptD8rgJgjilgjxV2DRlJ5cFJTVqR3ir7jNkhFNCfXgJgjiluDzebgrLgQdQwahvEYLX6UMpYU55KtoY5CyIAjiluHzeWgX6MFlaRddojDZtgaZoQiCIIgGIWVBEARBNAgpC4IgCKJBSFkQBEEQDULKgiAIgmgQUhYEQRBEg5CyIAiCIBqElAVBEATRIKQsCIIgiAYhZUEQBEE0CCkLgiAIokFIWRAEQRANQsqCIAiCaBBSFgRBEESDuK1EuV6vx5tvvok///wTEokE8fHxWLJkibtOTxAEQdwCblMWb7/9NiQSCX788UfweDyUlpa669QEQRDELeIWZaHRaLBt2zb88ssv4PEs3bP8/f3dcWqCIAiiCXCLz+LSpUvw9vbGhx9+iPHjx2P69Ok4fPiwO05NEARBNAE8lmXZ5j5JVlYWJkyYgHfeeQejR4/GiRMnMH/+fGRkZMDDw6Pez+r1emRlZTW3iARBEG2S2NhYSCSSWx7HLWao0NBQCIVCjBo1CgDQo0cP+Pj44OLFi4iLi2vUGM4u+MiRI0hMTGxyed1Ba5Wd5HYvJLf7aa2y15W7qRfabjFD+fr6IikpCb///jsA4OLFiygrK0N4eLg7Tk8QBEHcIm6Lhnr11Vfx/PPPY+nSpRAKhfjvf/8LT09Pd52eIAiCuAXcpiw6dOiAL774wl2nIwiCIJoQyuAmCIIgGoSUBUEQBNEgpCwIgiCIBiFlQRAEQTQIKQuCIAiiQUhZEARBEA1CyoIgCIJoEFIWBEEQRIOQsiAIgiAahJQFQbRAGIbFlRI1dHwfXClRg2GavTg0QdSL28p9EATROBiGxZ+ZhVi2/ij0RjMkIgEWTemJu+JCwOfzbrd4xB0K7SwIooVRWKrhFAUA6I1mLFt/FIWlmtssGXEnQ8qCIFoY5dVaTlFY0RvNKK/R3iaJCIKUBUG0OHw9ZZCIBHavSUQC+Cplt0kigiBlQRAtjhB/BRZN6ckpDKvPIsRfcZslI+5kyMFNEC0MPp+HpJhgvLngbhSXqRHk54HOoV7k3CZuK6QsCKKFwTAsDp4qomgookXhNmWRnJwMsVgMiUQCAHjyyScxYMAAd52eIFoNrqKhOoYMQrtAj9ssHXGn4tadxQcffACVSuXOUxJEq6O+aChSFsTtghzcBNHCoGgooiXiVmXx5JNPYvTo0XjllVdQXV3tzlMTRKuBoqGIlgiPZVm3FJ0pLCxESEgIDAYD3njjDWg0GrzzzjsNfk6v1yMrK8sNEhJEy0EoFAJiL6h1DDykfMBQBZPJdLvFIlohsbGxnK/4VnCbzyIkJAQAIBaLMXXqVDz88MM39HlnF3zkyBEkJiY2mYzupLXKTnK7lyNHjqBHdOuUuzXeb6D1yl5X7qZeaLvFDFVbW4uamhoAAMuy2LVrF6Kjo91xaoIgCKIJcMvOoqysDI899hjMZjMYhkFERARefvlld5yaIAiCaALcoiw6dOiAbdu2ueNUBEEQRDNAGdwEQTQahmFRWKpBebUWvp4yhPgrKKv8DoGUBUEQjYKaMt3ZUFIeQRCNgpoy3dmQsiAIolFQU6Y7G1IWBEE0CipDcmdDyoIgYLHHXylRI/P8VVwpUYNhWKev3clQGZI7G3JwE3c8rhy3IiEf//3iMDlzr8Hn83BXXAg6hgxCeY0WvkqKhrqToJ0FccfjynF77lIlOXPrwOfz0C7QA3ERAWgX6EGK4g6ClAVxx+PKccvUqbFJzlziToaUBXHH48pxy+fxHF4jZy5xp0LKgrjjceW47drBm5y5BHENcnATdzyuHLcA8P4T5MwlCOAmlEVhYSGKi4sRHx/fDOIQxO3B6rit2+Pa2Ws3CtVTItoCjVYWBQUFeOKJJ3DmzBnweDwcO3YMP/zwA3799Ve88cYbzSkjQbRaqJ4S0VZotM/ipZdewqBBg3D06FFLy0cAd999N/74449mE44gWjtUT4loKzRaWWRmZuLBBx8En88H71qUiFKp5DrgEQThCNVTItoKjVYWfn5+yMvLs3vt/PnzXG9tgiAcoXpKRFuh0cpi9uzZmD9/PjZv3gyTyYQdO3Zg0aJFmDdv3g2d8MMPP0RkZCSys7NvWFiCaG1QPSWirdBoB/d9990Hb29vfPPNNwgJCcG2bduwcOFCpKSkNPpkp06dwvHjxxEaGnpTwhJES6S+aCeqp0S0FW4odDYlJeWGlIMtBoMBr732Gt555x3MnDnzpsYgiJZGY6KdXIXlEkRrotFmqNdffx1Hjx61e+3o0aONDpt9//33MWbMGHTo0OHGJCSIFgxFOxF3CjyWZRtVpL9v3744cOAAxGIx95rBYMDAgQPx559/1vvZY8eOYdmyZVi3bh14PB6Sk5OxcuVKqFSqBs+r1+uRlZXVGBEJwu3o+D74z5eZDq8/Oy0OUqbiNkhEEPbExsZCIpHc8jiNNkPxeDzU1StmsxkMwzT42UOHDuHChQsYMmQIAKCoqAhz5szBW2+9hf79+zfq/M4u+MiRI0hMTGzkFbQsWqvsLVnu+nwHzSX3lRI1JCKBXXisRCRAx3aBaBfY+ZbHb8n3uz5aq9xA65W9rtxNvdButLLo1asX3nvvPTz11FPg8/lgGAbLly9Hr169Gvzsgw8+iAcffJD7+0Z2FgRRF2dKAcBtyZQO8pVj8aw++OdiGRgW+PXYZcwcGUPRTkSbo9HKYvHixXjooYfQv39/hIaGorCwEAEBAVi5cmVzykcQdrhyKHcK9XTqO+gYMqjZHMsMw+LgqSI7WR6bFI+kmGCKdiLaHI1WFsHBwdi6dStOnDiBoqIihISEoHv37uDzb7zK+d69e2/4MwQBuHYov/pgX5eZ0s2lLJzJsnzjcXRp702RT0Sb44ZCZ/l8PhISEppLFoJoEFflM8qqdAjxk6OwrJZ7vbkzpesr5UHKgmhr1Ksshg8fjt27dwMABg4cyNWEqsv+/fubXDCi7VLX5xDkK0dxeW2jSnj7ekqRnhrJtTzdezgfNRojLhWr8fCEHnhj7d925qnm9B1YS3nUdW5TKQ+iLVKvsliyZAn3/7fffrvZhSHaPnV9DiF+ckxKicTKLScbdEybTAzO5FVg6/7z3LHpqSpIxAJs2X8ecV383NqsyFrKo67/hJzbRFukXmVhjXQym83YvHkzlixZYpdnQRA3SmGpBut2nkLaPREAD+gYrMQXu087+CDCggeiQ5DS7rMXCqqwYvNJu2M3ZGRjUkpX1GiM8POUuTVTmkp5EHcSjfJZCAQC/P777y7NUATRWKo0OqQmheObjGxuNT45VYVdf1xEaaUOgEUJFJdpHJRFaZVzH4HRxNy2FT2V8iDuFBodyjRz5kwsX74cRqOxOeUh2jhCgYBTFIBlsv8mIxvJiWHcMRKRAFKJ4zrG38t5ue+ekYHUeY4gmplGR0N9+eWXKC0txdq1a+Hr68tldPN4PHJwE41Gpzc53R1YI7CtfghfT6nDZzuHemH++O52/o3547ujawcfUhQE0cw0WlmQg5twRn0lNpwdK5UInUYQhQV5YlKKCnweD2HBngj0keNKidph3JjOvnhlXl9UqfUI8JahcztvCIU3nutDEMSN0WhlER8fjxUrVmDnzp0oKSlBYGAgRowYgYcffrg55SNaMI0pz1332HU7T2FyqsrOZ2HNwPb2FMNXaQmlrZsZvWhKT4iEfPz3i8N2r3XpQDsKgnAHjVYWr7zyCi5evIjFixejXbt2uHLlClatWoXi4mK89dZbzSkj0UJxlU3trMSG7bG7/riItHsiwOcDvbsFo0t7b/D5PIQGWD5zpURtN65SIUJuYTUCfWRIGxiBvYfzUVqpa/ZyHgRBXKfRyuLnn39GRkYGPD09AQBdunRBjx49MHTo0GYTjmjZVGl0XAgsAG4Sd5bBbJvtXFqpw8afLW11u3f1d9iF2B7r7y3FiH6dXEZPuTNb+kZMbrdzTIJoDhqtLPz9/aHVajllAVhK4AYEBDSLYETLhmFYXK3QYfuBHLtJPONgntMM5hvJdrY9NjkxzGn0VNo9Edh+IMdt2dI3YnK7nWMSRHPRaGWRlpaGuXPnYvr06QgKCkJRURG++uorpKWl2TU/uuuuu5pFUKJlUViqwfKNxx0m8cWz+nD5DrarZj8vGZftrFSIkNI7HGHBSrAsC4Zh7SZH28xo8OAyeupmcytuZjXvzOS2bucpBPjIoNObIJUIYTKb4aWQNnp3cCNmPIK43TRaWWzYsAEAHEqSb9iwgXuPx+Ph559/bkLxiJaKqyJ6AgEPfD7P6ar56em98N4TA5GdX4GPv3Vd3sM2M7pCrcP2X3IcdiS2vo4b4WZX83Wv199bitSkcDz/8e8OO6uZI2MatTugQoREa6LRyoLKihO2uDIr+XlazELOVs3//eIw3lxwN6corK87W01bM6Nd1V+6GUXhSq7GrObrXm995rHG7g6oECHRmqAAdaJeGIbFlRI1Ms9fxZUSNYRCy/rCOolbM6pti+hZzDxqp6tmVyU7ymu0DueymqfuigvB+08MwpsL+uH9Jwbdkk2/vtV8fdS9Xj7fuXnMajZraDxnY1IhQqIlc0P9LIg7C2cmmwXju9lN4nWL6AGW9qa5hdVOV83Wkh11X/f2kNZrHmqq+ks3u5qve71SsRBb9zuaxyQifqN3B1SIkGhNuG1nsWDBAowZMwZjx47F1KlTcfr0aXedmrhJrlxVO5hsPt7yD65cVQO4biqKiwhAu0AP8Pk8zsyz51AeJqeq7FbN88d3R8dgT6eraQGf59Q8VFiqadJrupXVvO31KqQiS3l0m3HSU1UQCvg3tDtwdg8JoiXitp3F0qVLoVRaqoju2bMHzz//PLZu3equ0xM3QXGZxqmpxVlFWMCyE6mo0SFtYAQA4J8LpXh8cjx0ejP8vaXY8NMZRHf0RVJMMN5ccDfKqixRUp1DvXA6t8yleSjEX9FkuQhNtZovq9Ji57XkQvAAsMDOPy7ikQk90DMqiBuP8iiItoLblIVVUQCAWq2mcuetAFd1nJxVhDWZGPx+sgDLNx7nQmMH9uyAkopa7Pz9Imo0RkxOVUGt0yP3VLWduemxSfHo3M7rpsxTNwOfz+NW/uXVFt/CjU7ivp4y1GiMXHKhVd4Qfw87RUF5FERbwa0+i8WLF+P3338Hy7JYvXq1O09NNBLblbBMKsLUYVH4+sczdp3p6laEZRgWmTmlnKKom3E9Z0wMNDojfvorD7Gd/RzMTcs3HsdLc5Oc5mHoDCas23mqSXMRmmISb0yXPMqjINoSPJa91szYjWzbtg07d+7Ep59+2uCxer0eWVlZbpCKEAqFKNUrsWLLPzar/u4oqdBBZzBbKsIGSeHJr4LJZLr+ObkfsvK0lq51Q1RcVrcViUiAcYO6QCziIyxIgdfXHnY4d3qqCj06WybaokoGK7dkcTJMHRYJhmGhNVjG3Hs4H3NHdYWUqbCTHWIvqHUMlFIBWEOlnYx21yn3w5J1WQ4yvjgzFqbashu6X9Zzekj5gMH+vuj4PvjPl5kOn3t2Wpyd7ATRnMTGxkIikdzyOLclGmrs2LF46aWXUFFRAR8fn0Z9xtkFHzlyBImJic0hYrPTEmW/UqLGknf311n1n8SbC+6G3miCr1KG0sIc9OjRw+5zmeevgmG1Fmevi4xrhmXxTUY2Xn2wr1Nzk0jIBwRyAMDKLX/ZyfD1j2cxblAXbNyTze1uOrUPRGhAZwA2O4V1dXYKCdd3Crb3O/P8VedZ4WIFEqM7NtHdtNxPZ9fasV0g2gV2btQYLfE5aQytVW6g9cpeV+6mXmi7JRpKo9GgsLCQ+3vv3r3w8vKCt7e3O05PNBJXOQh6o4mL1nG2Wvf1lOHXY5cxOdXSj8JZNzuwlrFqao14bFK8XRTR1GGRkEuEeOXTv3As2/lEzlzbAFv7bpuZ6xtiV+YeV5FU1vDZujI2dTIc5VEQbQm37Cy0Wi0WLlwIrVYLPp8PLy8vrFy5kpzcLYzG5iDUjfAJ8pVj5sgYrNt5CkOTwjE3LQart59yqBJryfCWolOoFzwVYvxzsQwMYxnzs52nufM6kwE2xlK90YxKtY6LyLrRshnO/A2PTYpHkK/8lu5fXSiPgmhLuEVZ+Pv7Y+PGje44FXEL2E6iror9CYVCp87hpJhghAUrkVtYjasVtZg2PApKmRhF5bXY9YclGio9VQWZVAihkI8eXQMQ6CNHeY0Wer2Zm+z3Hs53aI40Ny0Waq0Bk1JU2Hs4HzUao50Cu9FEOz6fh6SYYCye1YdTWDt/y4GnQgwBnwc/r6ab1G8kodBZmC1BtBQog5vgsK6EO4UOwpm8cqfF/iD24nwDgH311Wq1HvlFNdhzKA+llTr4e0uR0jscU1KjUFxei51/XERkRx+0D1TaTaK2tv3SSh12/XER4wZ1QWS4NwR8PlZsPoHCslrOXxEW7Gk3kTYmMqkuxeW1eGPt39AbzVzPDOvftyPE1VWEloeQfqJEy4BqQxF28Pk8sCycFvsrLNVArWNcVl99dc1BbN1/HiP6dYK/txSllTpsyDiL6lo9ACCldxh4PB4KrlrqPlmpa9uv0RjRMcQTQb4KvLH2bxSW1XJybMjI5pL0rDWkANxw/Shb05WzooDNkT1eH678LhB7uU0GgqgPWrbcYTQmo7g+H4BSKmh09dWNP2cjxE8OhVSEDT9Zjtm6P4fbHfSKtmQ6u7LtZ+WUOpUjt7Aa72845rALuJH6UXamKxcRXO4sFe7qnqt1jFvOTxANQTuLOwBrNdd/Lpbi1+NXsPDd/Xh+xR9Y+O5+/JlZaLfKB+qPFmINlRYTj58ck4aoEOwnd1l9VSIS4KHx3bHmO/ukug0Z2dAbTDh/uZI7d90aSQBgZlincuQX1dzyLsBZpJKz63UXru65h5R+okTLgJ7ENo7VFr7w3f04cvqqQ3c724mWYVgUXFWjUq3D45PjkZ4aCX9vqZ0PwGQyISkmGPffG43tB3JQVF7rdJKLVwXg/ScGQSTgO98dFNXg+Y9/d6qsAItZZsXmEw7FCB+e0B17DuU5jFdW3XBJcFtsS58nRgc4hPM25PNwVk79VnAVZgtD1S2NSxBNBZmh2jh2tnAbc4u/txTJiWEAD6io0SHIV46jZ0uQX1RtWflbS5Lf1x3RHX1hMrM4daEUIrk/Csuut1TdezgfU4dF4usfz3KfmTc2FiaTmZtsXYXC1lf+orxai8IySySVtVifRMSHr6cYNRqj3bESkQAiAd+hPWtDcA2WGAW8FRq8Mq8vdHoTgvwUaBfgWAHWasIrq9LCzLB2jvemqFflzBR37Fhhwx8mCDdAyqKNU9cWLhEJHOo3bf8lB49NikdZlZZTFMC1kuTfnsTiWX3sIoUWpifYjekhF2HRlJ7ILawCwwCb957DvXd1glIhQcdgT/xrSgLeW3/dxzB1WCS+/+0Cdw5nvgGrWaa0UscV60tPjcSKzZkOobVzxsTgdG4Zyqv1Nzxh141CCvGT4+EJPVBRrbMLoXUWrWTNHymt1DVJzaem7NtBEE0NKYs2jq0j15rDYDAyDk7p5RuP4/HJ8VwoqXXXAQAXCyrtjs0vquHGTE4Mw9UKHVbtt6+19PWPZxDgHY8ajR4CHg/jBnUBw7Lg83iW0h7XsDQMEjrsCkL8FXhsUjy3g5GIBAjylTvsNsACWr0RGu31XYqzkuauKCrTILewGmkDIyCTCMDn8ZyG0DqLVrJ15DeFQ5zyLIiWDCmLNk6QrxzPzuyFs3kVYFjg0KlCjL6ni1OlwLIsQvzkSE0Kd1i5W0NhAWDPoTwuSxs8gGFZp34Jg5FBrc5sN8kCFgWRdk8Eth/IweRUFd758jBmjoyx2xXw+Tzc3T3ULtO7Vmd02G1IRAI8eX8iPtlmCfWtqtUhN7O6UfkKDMPiTF45tu4/D73RjPTUSO7/1muwKiBX0UrWe3erDnHKsyBaOuTgbsMwDIuDp4rwn3WHsSEjG9t/ycHgXmEor9IixE+OEf06YfuBHGzcY3nPZGYxZ3Ssw65jzXenLErlGjUaI9S1RqTdE4GOIUqX9aA8PcSo1RmdTrLBfnKk3ROBXX9chNHMILewGkfPFNs5i62Z3vcktEfHUE8E+ci57nT+3lKkp0Zi/vjuAM9yfIifHDyWh9zCKqQNjIC/t7TefIXCUo1dPokrpVdeo3UZrQS2aWo+UZ4F0dKhZUsbxjoBKRUipCVGQCziw0MuhpAPPDguDv9Zd9huclqx+aSDP8L6XrCfHJNSLIUCPRUibNl/nsvSHj+oCx4YGY0qjZEzNfkoJfh48wmk9A536uAuKqvFxp+zuexp251MXWcxn8eDl0IMAPgjswDpqSooZCIuJFciEuCBkdGQSUV48ZM/nfoUnOUrONstuCob4qqeVICvFMm9OtxyeRDKsyBsaYkdFklZtGHKq7UOzuwQPznmjomFRmdC2sAI7D2cz5mXLJMVy02YVjMVn2/pmnfsbDESo4KhlIsxLy0O3+7NxrlLVfj12GUMu6sTVwzQ6sQGLCar9FRVnQirHtjw0xkArrOnPeRJEAp4qFYb8H9fX5+g01NVYFjWIXejSmO0K0Zo61PYfiDHab5C3ZpSew/nO8hq3TE0d1FAV/WtKM/izqOldlgkZdGG8fWUIaX3df+DtTTHf788YuePyPg7D+cuVVkmJ5kQT0zticslNWgXqMS6Hae48NC5abHYsu8c9/f88d1RqzMgLMQLr685aDdRf/3jWc75u/OPi3jy/p4wmhjw+DwYjWZMHRqJD7896TJ7+uT5Umz/xZLtrVSIoK80cwl9D42Lc/iMKxOSQibE4ll9oNFqcKVEbTfB190t1GiMCAv2xLJFA1Gp1jkohOaMVnJV3wqGq01+LqJl01I7LJKyaIPY5gNEd/LBnkOWydbZKn7Nd6ewaEpPrNt5CqMHdMbFArVdG1VbU87q7Vl20T8b95zFtOHRuFpeW6/zVyTgQ6MzYcXm64UJHxoXhwdGRsNLKak3D2ODTcSRddwgX7nDZ6x+k7rjdAhSusyHqG+3YC1/7i4oz4KwcqMl990F7XHbGLYZ24tX/oE31x7CyGuF/Vyt4nMLqzBteDS0ehOnKKzvfZORzTm3bRWAdZfywTfHXWZxW52/D0/ozikK6zifbM2Ev7ccW/efd8jSnjMmBseyix3OaX1fLhM5ZDt37eDtkIU9OVWFT7dlYkB8e26suqVB6pYZuZ3b/JYkC3H7cFdzrhuFdhZtDGdb2A0Z2Vyeg7PVN8MAeUU13PG21A0PtTYhst2lOOtB8fCE7tDUGjB1WCQ0OpPTcWtqDUhQBeHPzAI8MbUnLhZYkvq27j+P1KRwVNToUaMxgn+tSZZVkazacgL3DYl0MBedySuzy7+w7ohslU1LWKERRH3cTMl9d0DKoo3hagsb4C2DWqvHgvu62/WpmJyqQsbBPEwbHg0ez3VpDktRwDhs2mMxB/H51xVLaaUOf2YW4PHJ8WBYFgIeD9//moMeqiBEdfTh8iPqjisRW3p2J0YF492v7XMxvrmm4Py8pGgf5AGAtVMkq7dn4uW5dyEuIoD7jJdCiu0HcpzKb/v37V6hEUR9tNQOi25RFhUVFXj66aeRn58PsViM8PBwvPbaa/D19XXH6e8oXEXVFJdbQlWjw73xzIxeyM6vAMMAGQfzMDQpHGt3nIJIwMe8tFh8uj2LUybzx8ehVmvJqegQ6IF/pXeHwcRDrc6E9NRIrqjfXXGh+OCb43ZKSNXeCzw+H8XltQ5RRumpKpRWapGgCoBO73znEeAtQ3WtHieyr2JDRjb3njXKqe4OwdmKbP747ti45yx3H1rCCo0gGqIlln5xi7Lg8XiYO3cukpKSAABLly7FO++8gzfffNMdp28TuIq7NpkYXCioQmmVFv5eMoQFKh3KZFid1ABwOq8SeUXV6BTqBZOZAZ/fATut5hoAm/edw4tzkpCVUwqGATbuyUZqUjh+PX4ZvbsFoqisFh9vPmU36TMs67SnxeJZfSAW8vDTX3kYPaCzXcmPQF8ZvDwkiAr3RfE1n0ddBXe1Unvt2u3vhd5oBp8P+CplDvclKSYY7z9xfUUW5CtHdEdf5F4pQcd2gS1ihUYQrRG3KAtvb29OUQBAfHw81q9f745Tt2oaqnLaOzoIB45fwcot9lFG/1y4ikVTEgAAuYU11233sEzCsZ39oNaaoNEasSHjrN05C8tqkZVT6rCSf/L+RFRrjCgs0yNtYAQA4NjZYuiNDMKCPZzuDLLzK/DzoXxMHKJCxsFcjBoQAb3BDH9vGb47cB6ZOeVc/+66O4L0VBWkYgH8vGRYu+OU3dgSkQDdOvkhyFfuMh7ddkXWLtADRZfOol1gZ7v72hwJTy0xmYogmgK3+ywYhsH69euRnJzs7lO3KhpT5fSNh/uhuLyWm7z3Hs7Hpp+z8eikeBSX1aJWZ4SXQsSV9JaIBJg5Mhp5RTX4dFsW0gZGuHR426I3msHjWQ3/ln8zz5VgaFJHSziti3FC/BUoLKtFxsFcDOvXycFMlVtUg2Xrj3JtUMOCB6LgqhpiER9SiRBmkxleShnmpsXhv18ctsucjovwR3F57Q3HozeU8HQrk/3NJFORciFaCzyWZW+ta8sN8uqrr6K4uBgffvgh+PyGI3f1ej2ysrLcIFnLQij3w5J1WQ4TsDXnwN9bivuHRWHllkxuYpo5MhpGE2PXW2La8CiEBXpAozNBq7ckqa3b+Q8Ky2rh7y3FpCEqu7IZc8bEYOv+81zfa8BSc2lSSqTdDsZ2J2At2ZFxMA8D4tuDzwe6dvBGlVqPDzaewKQhKqeOZ+u1PDstDh78GpTqlVix5R87WbR6I8KDlJBLzCivNlgymq81BNLAF299ftTh3j03oydYxgilVADWUAmTydTgfX1xZixgqHKQ4eHx3eAvqbEb40a/sxdnxsJUW+Z4vFB4S+cjiMYQGxsLiURyy+O4dWexdOlS5OXlYeXKlY1SFLY4u+AjR44gMTGxKUV0Gw3Jnnn+ar1hrCm9wzlFYX2vWmN0qJr65e4zeHpaIt7/xtGHUVqpg0ZntAs3zfg7z6Hq7LyxcVj6+fU6UkqFCAzD2O1o/rlQ6qBQHhoXh4fGxcJgNLu8FolIgAA/H8gkAViy7BeHhMG0eyLw1udH8NikeAyI72TXWyK3sNrpjuZioQYbMs5ySi2pRzBOncsHX6QAj8fjMsKtKBUiCMQe0LFSFBVWcu8rFSIUlRvgFRaEkBCPBlf9rr4zvliBxOiODsdfKVFjybv77a55xZZ/8P4T13dGrfUZb61yA61X9rpyN/VC223KYtmyZcjKysKqVasgFovdddpWi6uoJmsYq7Pe165KXpy7XMkVEwQPMBgZTBjUBRU1BnjKJdAbtNhzKI/za6hrjVg0JQEMywIsD1qbaCXrLqKu8gn193AwCX2yNRPPz+oNtdaIED+53W5FIrL0jpgzJgZZOaUI9HXdy1tvtPTbCPGTo3M7b1woqOIKJNbN70hPVWHnNWe+1Sy1eFYfvLEuy+EYayHEkf064ZVP/7K7nj8zC3BXXGi9BQ4b+525CtVtqZm6BOEMt2Rwnzt3DitXrkRJSQnS09ORlpaGRx55xB2nbrU468n80Lg4qMK8MGNEFBQykUOWp6tS4SIh364c+S9HL0EsFmL7gRysz7AU9Lt/WDRmjIji+lno9EYI+HzkF1dDIuIjPTUSk1JUmD0qBhkH8xwin8wM43TiO32xHCVltXh0UjzSU1WYlKJCiJ8c88fHoUs7L2h0Rmj1ZogEfJdZ4NaxDp0uxv6jl3A8uwR6oxmllTquEdKkFBUWz+5jF9ll/dy5SxUOSYopvcMBWHZodbsDfpORjbEDuzgtcGib/d2Y76y+UN2WmqlLEM5wy86ia9euOHv2bMMHEhx8Pg9JMcF4a8HdKCzT4FKxGl//dAY1GiMeGhcHTa0e88d3tzP7eCpEDv2w54/vDqVMiDXfn+LMTR2Dlfhi92mHirTWvAozw0AiEeJiQTWEAj5YFvjl6CUuEsvaFtW2Wq2HXOTSWb4hIxsLJ8djQ0Y2V3XWbDbj7a8sBQ1D/OTo2sHbrixI3ZBf61grt2Ri4eR47lzWRkgSkQBJMcFO+3O3C1DaNW/SG82IDPPGmwv6Qa93biLTGZy/Xt+q/0aTqVpqpi5BOIMyuFso1sZFBaVquxUuAGz6ORvjB3fFln2Wyq58PtAxxAub92WjokaPcYO6INhXDqVChNXbszC0b7iDH2Jyqgqsk/yIlVsy8eT9PXG5RM35P+r6Ob7+8SwmpXTFF7stuxKJSIDLxTWYmxaL1TYJfdbP6I1maPW2fb1P4IXZffDM9F6o0RqgkImwensmjGYGk1NVCPaTg8/jY93OUyit1NkpKL3RjOKKWgfz06IpPdE51Mtpjsm6nZbmTbbd9UL8LQlPV0rUTpVcqL/ihkxKVhqTTGUbAdUp1HWVW4JoSZCyaKFYazw9ONaxHHdKnzBuUradAK29GzwVIkglAqzenoUB8e3RuZ0X3lx7yMHU4mxsvdEMgYDv1DRjW3HWRynlzjtvbCw27z0HD7kIi2f1xj8Xy8Ew12szSUQCVKrtTUNllTqssNkVWZUBy7J4f8NxKBUiruUrn8eDUMDjxmIYFn9lFVp6hhvM8POWggfLRO2tEDmtD2WNp6i7ene1uo8K922WVX9L7VVAEA1ByqKFYnV+yqQCuxWuv7cUvp4yp5N853aemD++O+RSIUora7ndhFjEd3p83bEBy2SqdVF+w7agoLeHBJNSLIlzoQEKTB0WhStXNfhi92n079Hersz51GFR+P63HLtzFNmUNdcbr/e/8PawtELVV15XhADw2ESL6emBkdEI8lNAKRfb5W2kp6oQ5KuAQChwGqYb18Ufndt5QS4Vwd9Lyr1X13Tk5ymDmWFxOrcM4cFKfPDvQSirbrr6PC21VwFBNAQpi9uMrUnCRykFn89DWZUWUomQq+lkW1cppXe4y/IYuYU1XMjo4ll98Mbav6E3mmEwMU6P9/eW4aFxcfhk6/VcjYfGxcFsdn68NRIrPVWFvKJq7D2cjxH9OuHVTw9CqRAhpXc4xgyIQICvDK891BfHzl6FQiaCl0Jslxj44Lg4/PDnRbv7YFVGrnwfPp4SpKeqYDAxyM6vxNb95+0ivPRGBpUaPXyUEqd1qHR6M/7vq+ur+X9NSUC/uFCur0W7QEtobHOv+ikCimitkLK4jTgzSaSnqvBHZgGSEzsgPVWFn/7Kw31DuuKp+xOh1hnBMCy+/ulMgyGj5y5VcJOpTGJJcLNNvktPVaG6xoBNP2fbmW02/ZyNBRO6Y/74OLuEv7lpsVBrDRg3qAskYgG27D/PlSl32ro1LQ7tAj0g4PPx/YHzduf49udsDOzZAecuVXH3QiISQCLiQ28wOUz2U4dFIa+wGlq9GdsP5CBtYIRT53ywnxx6TxPkUktIrZdCApnE4vj/6gf7Ph3vrT+G8GBPuyZH7lj132h4LUG0FEhZuIm6ZR2EQqHL3hOPT47HB98cR1iwB6YNj4ZSLsIbaw9ZTC1+CtRojPgzswCLpiTAZGahlIvw5Q+n7UJG+XweRvbrxE26IX5yLJrSEyzL4mJBNXb+cRHD7+qIwrJaO3MPAFRpDGgX6IFpw6OgN5jRPlAJAR8QCnjwVkqwamumxX8gtpi30hIjHFu32pTnsHWOW2kXcN2BbI2Q8vOSYMmav6FUiJCeqrLkXhjMCPSRY92uU0iMCuLulW27WOu9W7H5pEVJeIihlIuxalumnQxXq3R2EVHFZRo7ZeFq1V9WfX3Vf6vlOSgCimitkLJwAyYTg99PFthF6Tw8vhtEGh30RjO6dvDC2IFdoNNb/AhiAR/pqSooZCJ8ufs0JqVEQm80QyoRgM8D/j21JzQ6E5atP2YXIqszGFFRY8Dew/lgWdhNpoVlljpKi6b05MqFtAtUOl3l5hepsXzjCSxMj0dZlc5uYpuXFotHJvZAtcYAL4WlJapMKuB2DuHBSs6XAFx3jj8+OR55RTXYezgfNRojfDyleGZGLxhNDORSIS5cqYTJZMnVUEIEFrDzScwZEwPfay1Y9x7Ox/3Dop1O7F4KCa5WaB0y2W0d9NbrlErsH39Xq36zmQXDWBI+btVM1VJ7FRBEQ1Bb1WaGYVhk5pRyigK4XtaBBx6SugUiPTUKl4prUFxRiy92nUa11gi5VIit+89j5shuYFkW89Ji4ecpRW5hDYxmhsuvsI63cstJ+Chl+PXYZYy9JwKqMG+kDbQkq/l7S7njLpdYOuIlJ4Zh3Y5TmD8+zi6JbHKqCnuP5ENvNONqhRYarYkbR6kQ4dPtWeDzLI/N+csVmDkyGh4yMZfwd6m4xukknldUg+2/5GBkv05YOCUetbUGXK2ohVDAx6kLZdDozFBrDZCIBC57heeXqDFnTAxqNEZU1GidJrT5eEpcZrLbRkRNHRYFX0+p3TEh/gqnrVlXbD6BwlKNSzNVfYl6zqD2qURrhHYWzcyVq2r8c7HM6eR1tbIW/Xq0dzDZZPyViwdGx2DiEBW3ewjxk2PC4K7Yuv880gZGOB0vt7AKA+Lb44vdZzBuUBds3JNtV74iMSoYAT4yPD0tEeABG3+uxe8nruC5mb1RVq1DcVktZy7y95ZCJhHaJfhZzUknz1+Fn5cMfp4yiEQCvPv1Uc4/EhbsfLcC9rqZ7YXZfaA3GMHn8+2ufW5aLB6b1B18Ht+u7lRppWUHFugjR02tHi/MToJIyMOC+2QOXf8uFddwmex1w28j2nkiPVWFjiFeEAn5CPazN/3w+TwE+Eidht6W12i5a6h738k5TdwJkLJoRhiGRW5hNRjWRbtS8PDxt44mmxdmJ0GnN3NRSgAwIL4918HO+vm64zEMuFpKEjEfM0dEI9BXDoORwf3DovDJ1kwuC3tuWixC/OQ4fOYq4lWBUGtNdiGnyYlhnEPcVrZxg7qAYVhLafJrCYG2zmarv2FDnQTAXTbO91MXytC1gw9Wbz9mN/6WfecwOTUSKzY7Fj2s0RiRX1SD7QdyONNPZJgvQvwUOHq2BCIhHzwARhMDVZg3Zo+OQa3O6OAoDw/xhIdUAD9vudPvzFVrVqsDmpzTxJ0KmaGaGIZhcaVEjczzV3H+ciWuVtQi81wJ5qbFOpg3Cks1DitVpUIEk8mMsiod0gZGcCYkqxIALKvtOWNiHMb79fhlgAWiw70R6GOZwD745jje/+YY3lp3GCPu7oSZI6KRPlQFhVSEOWmxeHZmb3gqxIgK98YDI6O5MW17bFvRG80I8pVzZirwAIa1dzaXVuqw84+LGDeoCx6fFI9FU3riz8wCu+ZLDAO7ek1WBsS358p9WM/3zbU6TrbmMavpRyjkw2RmsOdQPng8HjZkZOOHv3KRnV+JEH+5Q2Lh1z+eQUW1DidzyvHLscs4ce4qTCb75h311Xe60dpPBNGWoJ1FE1I3FDbET44Hx8Uhon03fLTpuKWPdbAHLhWpseuPi0juFeaQcDeyXyf853PHSCLg+qq2tFKHjL/zsGhKT+QWVnG9tFOTwnE2twwTUyK5XATbyfLL3ZaQW6lIgKuVtZBJhHbhtPPGxuLFOX1wtUIHD7nQ6Sq6vFrLZVKDBfYeyce0e+2dzaWVOmzIOItJKSps/yUH4wZ1wblLVXbXU/faAdcKKsBbxhU8nDREBbGIjwq1DlUaHSRiIUb374yvfzyLsGAPTBisQm5hFdRao9NS5EIBn9s5SESCa6XP23F+g4Yc0OScJu5USFk0AdZwykq1zq4h0OgBnZFbUA290cyFqD49PZGbrPYezrcr/OeqAmraPRH49dhlPHxfd6y4ZqPPL1KjvFoLsUgAX6UU04ZHo1qjx739OuFsXgUYF/b1sCAPXCpWQ2+83iTJ+t6n27IwblAXxHXxRUWVzsGcNH14FLYdyHEIh3WVSGe18XcKtfgKbEuA/HrsMhamxyO/SM315e4a5u10HGsv7rp5FZNTVcg4mIdJKSrERfji7h7tHHJWbKvQpvQOdzCtLd94HF3ae9v5HOqr79SY2k8E0RYhM9QtYt1NLHx3v8V0dK1c9uxRMTCaGGzIyOZ8FgC4jGyJSMBNYuMGdcGkFBUCvJ2X8Qj2k+Px9AQoJEKk3ROBafdGWeoiGS3VUSvUOqzdcQpeCimnKFyXKxdck8l5xBDDslDXmrB800nstCn/PW5QF4QFeWDcPRFYNKUnMg7mcTsMhUzo1My290i+3WvbD+RwnxkzoDOMJgZb95/Hxj3Zlu58V9V45L7udp+ZOiwSPkqJ07yKbzKyMSC+PVZuycSYe7o4NIOyLUUuEQkQ5KJnRmGpmguNJQjCObSzuEWs4ZRhwR4wmhg7E8ecMTFQKkTYezify7je+ftFTBjUBS/M7oOrFVpIxAK8/eURAMDD18JY666svTzEuHilCn5eMvx6/DJSk8LtchDmpcViwqAuyC+2ONN/PXYZowd0dloFtrxaX6+TXCEVQiYROkQjAeB2B1sPZCI5MQx8PtAzKhBXSjTYsu8cJqV0hZ+nDEXltZxTev74OBSUqjnFY40yqtWb8NnO03aT+xe7z2Da8CjOcd6tkx+uXK1Bu0APeMhFTid6qy+ntNIxoU6pEEEV5o2npyXCx1Pi8prP5ldCb2SomB9B1AMpi1ukvFqLsGAPPDAyBpk5pUgbGMFNsGu+O4Vxg7pgQ8ZZrkkPnw+0C/RAaaUOn2zNRNrACIT4yTHi7k7wkIvxxNSe+GzHKZveEVGoqNGhXYAHzl+uxMMTemD9j6e5iVcmEaC61gBvDwnClBJ8fyAHqUnh+P7XC7h/WBTSU1UID/FEldoAmcTSCMma2GZbMiTET445o2OhM5rwz8VyLnnONhqJYSx+hdJKHbYfyMHjk+NRUa3ncj6+2H0G/t5SpPQOx9ShUZBLRfD1kqBSrUeNxmiXKZ6eqnI6+dfUGrH9QA4W3NcdpZW1EAkFWLLmINIGRtRbr8rPW+rU/7PUxv8zdVgkHhgZzSmputFWTV3M71azvQmiJUHK4gapOwEE+MgwLKkjXltz0G5HkfF3Hs5dqkJYkAfSUyM5u7ynQgSdwYyNeyxVVn2UYtw3RIVV18JkQ/zkmDc2Dlq9CUq5GCIhUFiqxZufHeLeHz+4K1Zvz4JSIcLIfp2QcTAPKX3C4AMp0odGobJGh6FJ4ZBKLOacTXvOYtSACGj1Jngq5Hh6ek+cu1SNPzMLMG5QF3QKVaJWf70Zke0kag2X9fWUYOv+85g2PBrpqSqown2gkAlRWqFz6tz+V3oCGJZBdY0WEe28HPwfnUK9nE7+Ee288MLsPvj6h9NIG9iV80HUVW62PovJqSps+PGM3U7Kmf/H2odj4eR45BbV2OVRAGjSfAkqRU60NdyiLJYuXYoff/wRV65cwffffw+VSuWO0zY5dSeA6HBvzBgZY5f/YM02fnpaIkqrtDAxrF0ToanDIiEW8rny4ZYeFKc5p3hqUrjdavjBcXHYczDXLt/COiGmJUbgp4N5uPeujnbJc+mpKgT4SOHlIcHmveeQ3NvebDU3LRYXr1RgytAo6I2W/hUbM/7hZEhODIPBaMbsUTH4345T6BDkgdLKWkxI7optv5zHuUtVSOnVDt06B6CkQut00peKhdj+yzmMHdQVVWqDgxnq273ZTosbyiQCfL7rH5y7VIUehVXcuLYtVEP9FQj0lePcpXIMiG/PTfiVagMenxyPS8U1CAtWOt256I0Mcq9lkzdnvgSVIifaGm5RFkOGDMGMGTNw//33u+N0zUZRmQa5hdVIGxgBbw8RJCKh01wJvdGMc5crERbkife/sU88++HPXCxMT0CHQA+8OCcJ1RoDZ7pyVuZi1dZMPD45HlerLN3egnzl3PHgWZRH3aimDRnZmJyqglIuQfrQKG5XYn3f4l9Q2Skla5b3XXGhDtVsvZRiiIV8/O/7U0jpEwYvhRhD+nTEK5/+BaVC5LDinzMmBpv3ZeOuuFC8+/VRTE7t6mCGkogEMJrMSLsnAkF+chSX1WLnHxdxb9+OXDXausmMVvPXwskJqFLrYWZYyCQCJPcKA2DxrwiFfHTr5IecK5UuzVa/Hr/s0FGvqfMlqBQ50dZwi7Lo1auXO07TrDAMizN55dwuIT01Elv3n8HC9HgHW3lK73AEeMsglQjsYv2tO4f3NxxzaHM6Z0wMREKB0wnGaGIcwkZnjoyGr6cUV66qnX7GRynFm58dctoNzxpBVDey6PkHejvUnLIqHgGfB6OZQXiQEp1CvVBapeWU1i6bXUNMJ19Ua3QYO7ALt5vhgee0x4TBaAkIsBb4k4gECPFXYFKKZeeZea4E89JisXnfOQyIbw8+H+jawQff/nwWp/MsyuCBkdFgWBZGE4N5aXEI8ZOjQ6Angnzl8PWUOpQDyTiYh5kjY5AUE4wu7b2bLV+CSpETbQ3yWTSA1UdRUKpGYWktwoI9kKAKQucQJR6fHA8eD1gwsQc+3nSC8yHUnRStsf7JiWHIOJiHacOjHSqzrvnuFBamJzifYLykEIsESB+qglZvxrGzxdAbzNj+y3lMTIl0+pnicouswf5ySxQTez2yyVXy2+nccqQmhXNJgNa6SqH+ChSX12LOmFiIxQJcLKiGRmcCn8fD+EFdsGX/eW6yj1cFwM9bDoOBwYNj41Cl0QE8YOfv9maonX9cRErvMO7+WE1uX+4+zTn3HxwbC4mYj8lDI7n8Euukf7XK4mew+H/Oce89PKE7Kmv06NE1EMF+CnTt4IO8omqwDFBcocHAnh0gEvKbPF+iri8ryFdOpciJNgWPZVm3BZgnJydj5cqVN+Sz0Ov1yMrKakapXCMUClHNeCG/WMc5qDuGeECrM0EoEuBKiQZHzhRh+F0dIZeKweMB73591GHiHjeoC/YcysPs0TEoKquF4dqKvS4zR0YDLJwqmxqNEXPGxECrN8HbQ4wN13IMrKG0truOhyd0x4nsYvRQBXHlM2xX1nPHxuG/10xQtnJae3hPSukKHo9nN+bctFj8dDAX+UVqO5nSUy3f5YaMbDw0Lg5SMR/F5Vq7a5ibFost+86hsKzW7nyLZ/VGtcYAD5kYMqkAn31/CqfzKu2OWTg5wc6UZysrAKd1nBZOToCn1AhTbRmEcj8sWZflcMyLM2Nhqi27hafjOkKhEKV6JVZs+ef6dzC+G4IUWpj5Cqh1DDykfMBQBZPJ1CTnJIjGEhsbC4lEcsvjtJqdhbMLPnLkCBITE5vlfCYTg3OXK6AvsZSfvnC5Avf27YQarQErt17PXZg/Pg5KuQjVGoNLM1JYsBJzRsfCWynB+xuOuwwD1erM2HskH88/0BtqrRGXitV2GchrvjuFqcMi4SGX4IFR3SARCbH3yHUzkEwqQKCPHEIBD0P6dMSSaxFaVjmspiaphI+pw6Ls+mRbo5+sJizbIoZ6oxmrt2dh0ZSeWP1dJjbY9IbYkJGN5x7ojZfn9kVBaQ2uVhqgNzJ2eRrWz9qush8aH4cVm09yu4j0VBUGJLR3aFDUUD9wZ+9pDSZ4eyqQGN0RmeevOj2GL7a83xRcKVFjybv77e7Xii3/4P0nbt2Z3ZzPeHPSWuUGWq/sdeVu6oV2q1EW7sRgMOPA8ct2bUXnj48DeHCw9a/ckolxg7qgawdvGM2Mg8lHIhKAz+OhqEwNpUKM9KGWlbhtmY+68f5qrRFFZRpsyDhrJ5dSIYJSLrYr621d5e89YumHvePXHIwaEIGKGh0Wpsdj6/7znMNYbzTj1MVydAxWonOoEgsnJ1gS+WzKcEhEAvh7S51OsLmFVUhODLM4qm0ma53ehNLKWtTUGqGQipxeF8MyWDyrDy4VV6N9oBIrt5zkdhpW38i4QV2ujw9wsoT4yTEgvj13zl+PWQom8vk8p0rX30sKvd6MKyVq+Hk1v++AnNnEnYBblMXrr7+On376CaWlpZg1axa8vb2xc+dOd5z6hjGZGGRfqsDKLZlcjwbwgOJyLXg8ntNJoWuYN2o0Bqcmn4lDVFi383qSnfX1+4Z0xb/v7wmDkQGfz8PW/ecgEvAtrU/BIrqTL0L85NyE6u8txexRsQ7RVdZJlmFYHDpV6DRMFsjlCvnxeTzkF1vqMe09nI9JQ1R24auTU1W4WFDtdIK1lkC3RhVZXxfy+Vz70w++OW533wxGBqP7d0ZuQQ1+PX4Zc9PiUKs3YUBCe7vscL3RUmrEtkHRvLRY7Pr9ApdXYhtO7CkX4WqlFvPSYrnQZYlIgIfGxXGKyOoneHp6LzsF29S+A3JmE3cCblEWL7zwAl544QV3nOqWMBjM+D2zAAVX1XY9Gqw5Fd1HdoNEdL2pjljER/sgJcRCPlZsPmnp5dDveu/ohekJWHctGxu4bgpKT1WhWm3AKhtz1ryxsRAL+Q6mmk17smE0MxjZrxPyi6udKqtOoZ4wmRmEhzi2NF29PYvr6T1/fHfwwOLLH89gRL+OqNEYodEZHZr9ALArWmir5Ab27GDnlJ6XFos132dh+vBoFJRqHO6bdQLPPH+13t7cVkUWFqTEy3OS4KkQ452vjtjllVivadXWTExK6Qq9gYGXhwivPtgXVWoDlHIxlm88Zne/l60/imWLBuLFmbHgixXNEvlEfbWJOwEyQ13DZGKQdbEUH206gbSBEXaF67p28EJy73As/+Y4Zo6Mht5gtnPgPjqxB5QKEcYP6gK9wWy3sp86LNLBDh/oK3eY1K0VX21f+2RLJl6akwSjmcFbnx1y6etgWRY+SgmKymqdKhOWZfH0tER8uzcbfeNC4aOUQCoWWkpuGBj8evwyZ+ZJ7hWGX49dho9CjKenJ+LcpUquBPq4QV1gNJkR5KdASu8wdAzxwuZ92Sgsq4VAwEdogMJpwb9PruWKOOvNbXWqp6eqIBEL8L8dWXhiak/ojZZKvbZ9PGyvSW9kOHPVmwv6ITzYE/uPXrJzoluPrVTrYKotazIfRV2orzZxJ0DK4hq5RdUor7IU2Tt2thhThkZh4pCuCPVXwFMhxpL//Q290Qwzw3KKwprtLBDwkNI7HNUaI7buP29nhtHqzRjZrxPW7ToNAAjxs3RoczYBMnUC0/RGSx/sCrWlpIazkhfpqSqs/i4LIgEfj06Md24O8ZJhy95snM6rxIWCGjz/QG+czi2HWMRHoI/UrtyIdZfz/W8XMHpAZwA8gMdiQHx7bPw5GzUaIxZOtpznytUaJEQGITEqGEoPMfKuVKF9oIfTazMaGaevdwxV4l9TElBUqsGW/edRozGCBx7EYgFsq8/WvSZbM5ivUobyaq3LjoS+ShmKKm/8mbgRqHQ50dYhZQHLrkKrM6K4vBYhfnLcFRdql9382KQemJTSFQqpEKEBSqQNjIBMIoBcIkRFjQFVaj3CQ5S4UqJGWLAHUvuE2/kBHp7QHa/PvwvVGiO8PcTIK3LuE5CKr5cUtyb3MSyLsCAlosO9cTqvkot8UsgE6NzOG6WVWsweHYPi8lrsO5yHh8bH4RMbx/zDE7qjpEyNu3u0R7fOftj1Ry5O55ZjQ0Y2Zy6pW5bi021ZmDY8CkVltZCI+Ha7qAfHxkIpF+FqJR8bbKKp/L2lUMiE8PWSOL02H0/nr8skQvxnnb3D/v++PgKRgI/547tj456zThWk1Qxma+759dhlh2MfmxSPEH8Fii4191NEEG2bO1ZZWJOoSiu1MJkZ5BVVYc+hPMwdE2fXwCjtngiwLODlIUGgjwyllVp0DFFCKhaiuEyDX45eQmpSOAzGWnRu54X2gUqHyXfF5pNc9VmrDX9uWgxWb7evi+TnZYn8sfooNtTJnahUn0VhWS1+PX4ZE1NUeP3absf6+c7tfRDoJcELs3qjptYIrd6MihodpCIBNu8/C5GAj7lj4ixmqemJ2Lr/PHJt6i9Z0RvN8PWUXndWX/Np8Hk86AwmnLpY4dCFz2pGk0kcy39MTlXBaGYwf3x3LkPcqngKSy15G14eEpRUaO1ChTfuOYtFU3ridG4ZHp8cj8JSDSRiAUJ8FZg5shv8PKWIDPMFn89DiL8CM0fGYN3OU3YlzuMi/MkcRBBNwB2pLEwmBr+fLLCrDTR/fBwmJncBj8dyforR/TujoLQWJjMDD5kYb6w9xB3/yH1x6BDsifShkSgsrYVSLkRuYTUMRnO9JiarDf+F2X0sUUwsC7DAH5kFSIwKxgOjYiAVCxzKbnyTcRbzx3dHWZUOQX5yvLbaPofCGhUlEovwz4Uyh8S+hyf0QFml1s4JOzctFnqDyeWKX280Q19ptqvp9PT0XpBLnec+MCwLM8Mi42CendM842AeHhoXBx+lGP+akoBarQlVGj2qNQZsyMjG3LRY+HlJ8MHG43ZjFpbVguWxCPL1wLL1R7kMedvquLZtUS1+A0/yGxBEM3DHKQuGYZF1oYxTFMD1fInnZvaCSChAiJ8cU4ZGIa+wGlv3n0faPRHY8FMWt9sYfldHADxUawwor7asgr08JJCKBbhUoq7Xxm49n0Zr5BTIsexihwJ+tpFC1ppS1oKArnpBMCwLjdboUJp7Q0Y2npney6Gl6OrtWXjq/kSHFf/UYVGQS4VOc0byi2rAsKzTa+TzeCgur8WEwV3twlmnDovEJ1szOb+HlUkpKk6O5x7obRcqbB3TSy5FVJgfvDz6orxa5+Akt22LSn4Dgmg+7hhlYTU7VWn00OmNdhnPhaUaCAQ8CAR88HgsnpjaE4ZrLVH1xuvd2Py9pQ5hoVOHRUImEcBoYiESAnKJCDNHRmOdTYMdq43dikQkQG5hDTbuce03+MYmS7puNVpXjlw+j+cy41lrcP66zmgGyzLcLkcqFsBHKcbLq/5yCJsdmhTOXYczP0KgrwzeSimq1Hq7ncX3v12wmJZsFvm2ClRvNCPnciUenRSPzPOlXLe/mSNjuN2Bj1KKE+ecZ2NT8htBND93hLKw7UPx5P09Uas3obxKg8GqcJRX66AK84ZWZ8AvR/KREBWEarUBPp5SrmSFj1KM9NRIdAjycFjZfv3jWYwb1AXf/3YaKb3DEeQrh4dcyq3+ozr6orisFjUaIwA4KA+lQoRandEuuso6qSqkFod33cJ/ew/nO2SAp6eq4HvNiexMkQgEfKevCwV88PlCdAr1hFZngq+XFG+s/dtBcT3/QG8s33Sc8ydYHe0dgj3A5/FQWqmFVCzChxuPY+aoGKc1m/g8Hvd/687J+ne7ACVnWrOal5JigjkzUoi/At06+VHyG0HcJu4IZXHlqppbuYuEAmRmFaBb5wC7yWnBfd0xon9nVNZYahstW3/MLpT0l6MXMCChvdOVrZeHyCET+qFxcdDqjSgsVYNhWDz/gKVonoDPw/92nOLMSyP6dUJJhRYhfnKHgoDzx3fHq/OSoDMweHlOEswMg2/2ZOPcpSr88GcuFk3pye0GLhXX4IsfziDAS4qHxsVxtZ2s8u85mOuwG3hoXByUCgFyC2pRpTZanNguKtKWVuk4hQdc7y1hzZOYnKrC6u0WU9O6HaecmLYiAQD/Sk+Al4cYq7ZmcqatOWMsjmlX5iXAEpoaF+Hf7H0oCIJwTptWFlbT06Xiai73wcwyGNw73MFB/PG3J/HMjF7Izq/EL0cvXTejANi819JPAXA0/4T4yRHoLcd/vzxiN94nWzMxOVWFIF85dHozNDoTvv7xDAYktOcmXat5SakQYV5anF3FWosfxT6KKj1VhdH9O2P3HxeR3Dsc63aewrTh0VyYr3XMTT9n25mBrPJnHMzD09MSoTOa4e0hwZe7/8HUe6Ox47eLKCyrhb+3FHPT4pyu3pVykUNnuzljYrgMcK49KQ/XSm1YSpdcLqlBqL8HFDIBTudW4vvfTsNHKcHMkTHILawCw8CiVJ0k09U1LwmFfAyIb9esfSgIgnBOm1UWJhODrAtlKK3Uws9LgoWT4nHuchVkYiEqavROV8/Z+RWQiAUOK/zJqSrwrvVkqLs6nzkyBucuVzodL8BHxoWfpvQOx6SUSGi0Bs6nYfWFKCGCzoVPwTaKyhrxlD40Cht+OoO5aXHQXDNhcVybrG0dyQAQFqy0lM74Lgvz0uLw2c5TOHepCpdL1BiaZOlXnZwYhnU7TjntfLdqWyYAYNygLgjylUEmFdmVMgGu+yEkIgEKyzTQGxls/yUHi6b0RPcugdDqGdRojCit1GHdzlN4eEIPCAQ8SMVCSETZDgrKmXmJnNgEcXtok8qCYVgc+qcIFwqqIRTwIJMKwWMtLTjLq3XwdVGJlGHAFcOra7N/fHI81wd63KAuCPaVI9BXhvJqnUuH89UKrdNaSVOHRSI9VYUgPzkkIgGSE8NQWFrbqCgqhmWh1hoxakAEvv35LNIGdXX6ubp/83iW3g/zx3fHrj8ucIUFfZRS8D1ZzBrdDWKhAIVltXad78BaVv5Wk1GgjwyVakvY633JXbFqW5adUs04mMf9+9jkBCQnduBW/65KYjAMS7WVCKKF0yaVRUGpGleuqrnEMetEJuDzoNEaIRZa8ipsS5DPGRODrfvPQywKc7rCLyqz9LWo0RghFvHxzZ6zmD48GlVqg9PM4blpsVDXGpz21bY6xQFg6rAoaPVG7DmU5zJT2YrVSezrKcWx7KuI6xKIzXuz7T7367HLDpVYLUlvYjwzoxc+3ZZpVwF33c5TeHxyPEzM9ZLfpZU6uzLhi2f1xvMP9IZSIUZ2XgW0ejNX9O/VB/tCqzdBLBLg/KUKztw1c2QMYjr52ZmIXO0KqLYSQbR82qSyKKvSOeQaWHcHAj4PFwqqcfFKBZ6d2Qtn8yrAMMC+w/mYmKJCSbnW6cq8awcfS97BtaJ64wd3xS9HL+HuHu0wNCkcP11LROPzgc7tvLDp52wkRgW5LIQX5CtHjcaAQF8pWFaGGo3RbkUvFVv6MtSNovL3kgI8Ftt/uR5tVFGjx7hBXRDoI4OPpwRqjZ4LheXzeJCIBfho0wnMGtXtel8I9noPi2PZpfj12GUsmtoTD0/obldqPT1VBaOJRVJMCADArFeDL/Z2mNAZhoWfpwzlNVok9+pww5M9mZcIomXTJpVFbV07Pq7lFBjM4POAPYfyMKJfJ5RVadExxAu5hVWI6xIIo9EMVZi3QzTR3LQYfLH7HySoLJP/gPj2+OlgLqYMjcLSzw9zJcstDnEerpSokV+kxoTBKuQWOq8DJZMIIZXwEeyvQHmlniv/Ye1lPTctFrv+uIhJKV3ho5TCx1MKhmGxIeMMxg/qigfHxXHF/2o0RgT7yeHtIUatzgRfbxl8vORQ1xrgIRdDpzPisUnxYFnWZUjr5NRIdG3vg67tfRAWpERplRZyqQh+XlKE+ntwE7+r6q002RNE26ZNKgt/b+c+iVqdEZ1DvbhV/OxRlpBN62q7VmeC0cRg08/ZmJTSFd4eUijkInjIhMgvUnMd56zj6Y2W0h51S2Kkp6qu9aZmoJQLHepAzR8fB0+FENt/ycHoeyKg1RtQqzPh8cnxMJoY+HvLYDabMX1EDE7nlqGorBZf/3SG8xuIRXxUa8x4ZkYvVKkN8PWSgDGbcaGgGoE+MvDAh0jEwstDgooaHbw9pCir1KC8xoAHx8ba+RkentAd4SGe6BjsCaHQ0nmoa5gPusLHbd8XQRAtnzapLDqFeGHBfd3xcZ3mPQzD4svd/3Cr8v/tOIXR/SPselE/OC4Wk1Mj7Uwxj03q4dCzesF9PeCpEDtVStEdfcHjWTYaWj0DPhi89lBfVNYYIBULYDKz+GLXaaT27QiWYRHgI0egjwKVaj085CJIRHxAxEdNrRH+3jK763hkoiWCqH2QEnqDGSH+crBgUVFrhEjIx1c/nMGkFBXCQzyhMxihkAohEfHh6yVHaKAnWDB4c8Hd0BlM8PMk3wBBEI3Dbcri4sWLePbZZ1FZWQlvb28sXboUHTt2bJZzCYV8dOvki8WzeiM7vxIh/grwAPB4PJzOq4RCdgWLZ/VBebUOoQEKTE5VwUcpRXF5Ldb/dBYBXlI8N7MXKmsMKCqvxfYDOejfox2ee6A39AYzrlxV44vd/0Ak4GPe2Fh8arNSnzMmBrmF1QjwkcHXSwovDzFyC6vxxY9HEOAlxbQR3VCrM2DaiG7ggYVGa8KFgmoE+MpQqzXCTygDeAxMJh54fEvm8vOzekNnMEMhFUEqFkAk4qNKbQDDsBALBQCfhb+3DAIhH09O64XOoV7cLoEgCKIpcJuyePnllzF16lSkpaVh+/bteOmll/D555832/muVmhRWKrBpp/PQW80Y1KKClKxpeTF4TNXkVtUY/EzAPh812mu1EZyrzBIRHyIRXz4eUng6yVF53aekEtFAMvgm/3n0KtbMKYOjYKvlxRCPrhdS6i/B4QCIDzEEzKZAJVVBjAMg6hwX0sZEJkIl4troJCLIBLyYTYzAI+HrmE+qNUa4RsohVDIh8nMQG80IcBLDtG1v4N8FLQLIAjituEWZVFWVoZ//vkHa9euBQCMGjUKS5YsQXl5OXx9fZvlnL6eMizfeJxzVgNAxsHrneas5SqemdHLabjo8w/0hlgkgN5gglQiBHgspBIhZoyMQaVaD28PCbR6E/giAbp28EalWg+FTAgej0VFtQ7vrf8HA+Lbg88HYjv7wddLghqNESEBHhDweeAB6NLOB1Jpw19BaAA5jQmCuL3wWLZOL89mICsrC8888wx27tzJvTZixAi8/fbbiImJqfezer0eWVlZN3xOoVCIUr0SP/51CZOHRsJgMKNKY8SWfee4SVwV5gOBAMgtUNv5I+aPj4NOb8J3v17AhOSuMJsZBPkpkHO50jK2gA8/bxkCvGVgGDMYlodanRFKuQh8Vg+YNDDxvVCpNsLbQwSeoQx6ne6Gr4EgCOJWiY2NhUQiueVxWo2D29kFHzlyBImJiS4/wzAsojoGoqpWBx9PKXy8zHh4Qg9o9SZ4eYih1ZsgkQgR3ckHL8zuA63eBLlUBKGQhxqNAQvTE6DWGqGQiiAW8RGvCrxJx3CwwysNyd5SIbndC8ntflqr7HXlvtmFtivcoixCQkJQXFwMs9kMgUAAs9mMkpIShISENOt5udh/kBmHIAjiVnBLyIyfnx+io6OxY8cOAMCOHTsQHR3dbP4KgiAIomlxmxnqlVdewbPPPouPP/4Ynp6eWLp0qbtOTRAEQdwiblMWERER2LRpk7tORxAEQTQhlLlFEARBNEiLj4ayRvYaDAan7+v1eneK06S0VtlJbvdCcruf1iq7rdzWObOpsiPckmdxK9TU1CA7O7vhAwmCIAgHVCoVlErlLY/T4pUFwzDQaDQQiUTg8ajUBUEQRGNgWRZGoxEKhQJ8/q17HFq8siAIgiBuP+TgJgiCIBqElAVBEATRIKQsCIIgiAYhZUEQBEE0CCkLgiAIokFIWRAEQRANQsqCIAiCaJBWqywuXryIyZMnY9iwYZg8eTJyc3NvixwVFRWYN28ehg0bhtGjR+PRRx9FeXl5gzLe7HvNwYcffojIyEguU741yK3X6/Hyyy9j6NChGD16NF588cVWIfu+ffswduxYpKWlYfTo0fjpp59apNxLly5FcnKy3XPRXHI25TU4k7u+32hLkduV7LbU/Z26XXa2lTJ9+nR227ZtLMuy7LZt29jp06ffFjkqKirYv/76i/v7P//5D/vcc881KOPNvtfUZGVlsXPmzGEHDRrEnj17ttXIvWTJEvaNN95gGYZhWZZlr1692uJlZxiG7dWrF3efT58+zcbHx7Nms7nFyX3o0CG2oKCAHTx4MCfvrcjirmtwJnd9v9GWIrcr2a04+526W/ZWqSxKS0vZxMRE1mQysSzLsiaTiU1MTGTLyspus2Qs+8MPP7AzZ86sV8abfa+p0ev17KRJk9j8/HzuAW0NcqvVajYxMZFVq9V2r7d02RmGYfv06cMePnyYZVmW/fvvv9mhQ4e2aLltJ67mkLO5rsHZhGvF+httrmu6VerK7ux3ejtkb/FVZ51RWFiIoKAgCAQCAIBAIEBgYCAKCwtva/c9hmGwfv16JCcn1ysjy7I39V5TX9v777+PMWPGoEOHDtxrrUHuS5cuwdvbGx9++CEOHjwIhUKBhQsXQiqVtmjZeTwe3nvvPSxYsAByuRwajQaffPJJq7jnQPM8G+6+BtvfaHNdkzt+p7dD9lbrs2iJLFmyBHK5HNOmTbvdojTIsWPHkJmZialTp95uUW4Yk8mES5cuoVu3btiyZQuefPJJPPbYY6itrb3dotWLyWTCJ598go8//hj79u3DihUrsGjRohYvd1uiNf1GgZb1O22VO4uQkBAUFxfDbDZDIBDAbDajpKQEISEht02mpUuXIi8vDytXrgSfz69XRpZlb+q9puTQoUO4cOEChgwZAgAoKirCnDlz8Nxzz7VouQEgNDQUQqEQo0aNAgD06NEDPj4+kEqlLVr206dPo6SkBImJiQCAxMREyGQySCSSFi23leZ4pt15DXV/o811TU2Jq9/pW2+9hejoaLfK3ip3Fn5+foiOjsaOHTsAADt27EB0dPRtM0EtW7YMWVlZ+OijjyAWixuU8Wbfa0oefPBB/Pbbb9i7dy/27t2L4OBgrFmzBiNGjGjRcgOAr68vkpKS8PvvvwOwRHaUlZWhY8eOLVr24OBgFBUV4cKFCwCAnJwclJaWIjw8vEXLbaU5nml3XYOz32hzXVNT4up32r9/f/fLfnMumNvP+fPn2fvuu48dOnQoe99997E5OTm3RY7s7GxWpVKxQ4cOZceMGcOOGTOGXbBgQYMy3ux7zYWt46w1yJ2fn89OmzaNHTVqFDt27Fh2//79rUL27du3s6NGjWJHjx7Njh49ms3IyGiRci9ZsoQdMGAAGx0dzfbr148dMWJEs8nZlNfgTO76fqMtRW5XstelrvPbnbJTPwuCIAiiQVqlGYogCIJwL6QsCIIgiAYhZUEQBEE0CCkLgiAIokFIWRAEQRANQsqCuKO5cOECxo4di4SEBHz++eeN/tzly5cRGRkJk8kEAJg7dy62bt3a4OcOHz6MYcOG3bS8BHG7oNBZ4o7m+eefh4eHB55//nkAQHJyMl5//XX069ev3s9dvnwZQ4YMwalTpyAUtspCCARxQ9DOgrijKSgoQNeuXW+3GATR4iFlQbQZVq1ahQEDBiAhIQHDhg3Dn3/+CZ1Oh2effRa9e/fGiBEjsHr1atxzzz0AgBkzZuDgwYN47bXXkJCQgCeeeAIFBQWYP38+EhIS8Omnnzb63NOnT8emTZtgMBjQq1cvuwY15eXl6N69O8rKynDw4EHu/IBlJ7NmzRqMHj0aiYmJ+Ne//gW9Xs+9/+mnn6J///7o378/Nm3ahMjISOTl5TXB3SKIG4P2z0Sb4MKFC/jqq6/w7bffIigoCJcvXwbDMPjwww+Rn5+PjIwMaLVazJs3j/vM559/junTp2PMmDGYOHEiAOD48eONMkO5QiwWIzU1FTt37oRKpQIA7N69G71794afnx/Onz/v8Jndu3dj9erVkEgkmDJlCrZs2YIpU6bgwIED+Oyzz/DZZ5+hffv2eOmll25KJoJoCmhnQbQJBAIBDAYDcnJyYDQa0b59e4SFhWH37t2YP38+vL29ERISgunTpze7LKNHj+aKtAHA999/j9GjR7s8fvr06QgKCoK3tzcGDx6M06dPA7AokfHjx6Nr166QyWR49NFHm112gnAFKQuiTRAeHo7nn38ey5cvR79+/bBo0SIUFxc7lF4ODQ1tdln69u0LvV6PEydO4MqVKzhz5gxSUlJcHh8QEMD9XyaTcf0tSkpKEBwczL13O0vwEwQpC6LNMHr0aKxfvx779u0Dj8fDO++8g4CAABQWFnLH2P6/ueDz+bj33nuxY8cO7NixA4MGDYKHh8cNjxMYGIji4mLub3fIThCuIGVBtAkuXLiAP//8EwaDAWKxGBKJBAKBAMOHD8eqVatQVVWFoqIifPHFF/WO4+/vj0uXLt2yPKNHj8bu3bvx/fffc02abpR7770XW7ZsQU5ODrRaLT766KNblosgbhZSFkSbwGAw4P/+7/+QlJSE/v37o7y8HIsWLcKjjz6K0NBQDBkyBLNnz0ZaWlq94zz44INYsWIFevXqhTVr1ty0PD169IBMJkNJSYld9NONMHDgQEyfPh0zZsxAamoq4uPjAcCueQ9BuAtKyiPuKA4ePIinnnoKBw4cuN2i3DA5OTkYNWoUMjMzKRGQcDu0syCIFkxGRgYMBgOqqqrw9ttvY/DgwaQoiNsCPXUE4YLvvvsOL7/8ssProaGh2Llzp1tk2LBhA5599lkIBAL07t3bqTwE4Q7IDEUQBEE0CJmhCIIgiAYhZUEQBEE0CCkLgiAIokFIWRAEQRANQsqCIAiCaBBSFgRBEESD/D+/IVOy8E2G6wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in numerical_columns:\n",
    "    # Create scatter plot using Seaborn\n",
    "    sns.scatterplot(x=column, y='price', data=data)\n",
    "    \n",
    "    # Label axes and title\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('price')\n",
    "    plt.title(f'Scatter plot for linearity check of {column} and price')\n",
    "    \n",
    "    # Display plot\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above scatter plots,we can see that there is a linear relationship between 'price' and 'sqft_living'.The other relationships are discontiuous."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation checks are performed during data exploration to understand the relationship between the variables.Correlation analysis helps to identify the presence and stregth of the relationships between the variables.Uderstanding these relationships will be crucial for gaining insights into the underlying structure of the data.We can also use coreelation analysis to detect multicollinearity between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in numerical_columns:\n",
    "    #create heatmaps\n",
    "    sns.heatmap(data[[column,'price']].corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "    #Label axes and title\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('price')\n",
    "    plt.title(f'Heatmap showing correlation of {column} and price')\n",
    "    #Display heatmap\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heatmaps above,we can conclude that there is a positive corelation relationship between all numerical independent variables and the target variable,which is the price.This means that if any of these independent variables increases,the price will also increase.While all these relationships are positive,it is important to note that 'sqft_living' has the strongest positive relationship with 'price',meaning it can be a good predictor for it and 'sqft_lot' has the weakest postive relationship with 'price'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frequency distribution refers to a table or graph the count of each distinct value or range of values in a dataset.It summarizes the distribution pf values in a dataset by showing how often each value occurs.We can represent this using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i, column in enumerate(numerical_columns):\n",
    "    # Set the ranges\n",
    "    if i == 0:\n",
    "        range_min = 0\n",
    "        range_max = 2000000\n",
    "    elif i in [1, 2, 5]:\n",
    "        range_min = 0\n",
    "        range_max = 5\n",
    "    elif i == 3:\n",
    "        range_min = 0\n",
    "        range_max = 8000\n",
    "    elif i == 4:\n",
    "        range_min = 0\n",
    "        range_max = 20000\n",
    "    \n",
    "    # Create histogram using Seaborn\n",
    "    sns.histplot(data=data, x=column, bins=4, binrange=(range_min, range_max), color='lightblue', edgecolor='black')\n",
    "    \n",
    "    # Label title and axes\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title(f'Histogram Showing Frequency Distribution of {column}')\n",
    "    \n",
    "    # Display histogram\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above histogram plots,we can make some conclusions:  \n",
    "1.Most houses cost between 200,000 and 500,000.  \n",
    "2.Most houses have 3 bedrooms and 3 bathrooms.  \n",
    "3.Most houses have about 1,000 square feet for living.  \n",
    "4.Most houses have a lot square feet of 5,000 to 10,000 feet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical data is data that represents groups or categories.They are often represented using labels or strings rather than numerical values.Analyzing categorical data involves summarizing the frequency,comparing distributions and identifying patterns or associations.We can analyze categorical data using bar charts,which are graphical representations which display the frequency,proportion or count of each category using rectangular bars.We can also show how categorical variables relate to numerical variables using bar charts.In this dataset,we can see'condition','waterfront','view' and 'grade' as the columns representing categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_columns = ['waterfront', 'view', 'condition', 'grade']\n",
    "\n",
    "#Set the Style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Iterate over each categorical column\n",
    "for column in categorical_columns:\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    \n",
    "    # create the bar chart\n",
    "    sns.countplot(x=column, data=data, palette=\"pastel\")\n",
    "    \n",
    "    # Label axes and title\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Count')\n",
    "    plt.title(f'Bar chart showing frequency count of {column}')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    # Display the bar chart\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the bar charts above,we can make some observations:  \n",
    "1.The chances of finding a house with a waterfront are almost zero.  \n",
    "2.Most houses do not have a view.  \n",
    "3.Most of the houses have an average grade and condition.  \n",
    "4.Mansions are scarce in number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between grade and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# create the bar plot\n",
    "sns.barplot(x='grade', y='price', data=data, ci=None)\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Grade')\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Average price')\n",
    "# Set the title of the plot\n",
    "plt.title('Grade vs Prices')\n",
    "#Fix Overlapping\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the bar chart above,we can conclude that mansions fetch the highest prices while houses of poor or low grades fetch the lowest prices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between condition and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6)) \n",
    "\n",
    "# create the bar plot\n",
    "sns.barplot(x='condition', y='price', data=data, ci=None)\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Condition')\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Average price')\n",
    "# Set the title of the plot\n",
    "plt.title('Condition vs Prices')\n",
    "#Fix Overlapping\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can conclude that houses with in very good conditions fetch the highest prices while houses in poor or fair conditions are the cheapest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between waterfront and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Set the figure size to (8, 6)\n",
    "\n",
    "# create the bar plot\n",
    "sns.barplot(x='waterfront', y='price', data=data, ci=None)\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Waterfront')\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Average price')\n",
    "# Set the title of the plot\n",
    "plt.title('Waterfront vs Prices')\n",
    "#Fix Overlapping\n",
    "plt.xticks(ticks=[0,1],labels=['Waterfront','No Waterfront'],rotation=45, ha='right')\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the bar graph above,we can observe that houses without a waterfront sell for higher prices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between view and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))  # Set the figure size to (8, 6)\n",
    "\n",
    "#create the bar plot\n",
    "sns.barplot(x='view', y='price', data=data, ci=None)\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Grade')\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Average price')\n",
    "# Set the title of the plot\n",
    "plt.title('View vs Prices')\n",
    "#Fix Overlapping\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar graph above shows that houses with an excellent view are the most expensive and houses without a view are the cheapest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modelling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>sell_yr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7129300520</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Average</td>\n",
       "      <td>1955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6414100192</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Average</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487200875</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Average</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954400510</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Good</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7237550310</th>\n",
       "      <td>1230000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "id                                                                          \n",
       "7129300520   221900.0         3          1         1180      5650       1   \n",
       "6414100192   538000.0         3          2         2570      7242       2   \n",
       "2487200875   604000.0         4          3         1960      5000       1   \n",
       "1954400510   510000.0         3          2         1680      8080       1   \n",
       "7237550310  1230000.0         4          4         5420    101930       1   \n",
       "\n",
       "            waterfront  view  condition      grade  yr_built  yr_renovated  \\\n",
       "id                                                                           \n",
       "7129300520         0.0  NONE    Average    Average      1955           0.0   \n",
       "6414100192         0.0  NONE    Average    Average      1951        1991.0   \n",
       "2487200875         0.0  NONE  Very Good    Average      1965           0.0   \n",
       "1954400510         0.0  NONE    Average       Good      1987           0.0   \n",
       "7237550310         0.0  NONE    Average  Excellent      2001           0.0   \n",
       "\n",
       "            sell_yr  \n",
       "id                   \n",
       "7129300520     2014  \n",
       "6414100192     2014  \n",
       "2487200875     2014  \n",
       "1954400510     2015  \n",
       "7237550310     2014  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>sqft_lot</th>\n",
       "      <th>floors</th>\n",
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       "      <th>grade_Fair</th>\n",
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       "      <th>grade_LowAverage</th>\n",
       "      <th>grade_Luxury</th>\n",
       "      <th>grade_Mansion</th>\n",
       "      <th>grade_Poor</th>\n",
       "      <th>grade_VeryGood</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "      <th>7129300520</th>\n",
       "      <td>221900.0</td>\n",
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       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "id                                                                          \n",
       "7129300520   221900.0         3          1         1180      5650       1   \n",
       "6414100192   538000.0         3          2         2570      7242       2   \n",
       "2487200875   604000.0         4          3         1960      5000       1   \n",
       "1954400510   510000.0         3          2         1680      8080       1   \n",
       "7237550310  1230000.0         4          4         5420    101930       1   \n",
       "\n",
       "            waterfront  yr_built  yr_renovated  sell_yr  ...  grade_Better  \\\n",
       "id                                                       ...                 \n",
       "7129300520         0.0      1955           0.0     2014  ...             0   \n",
       "6414100192         0.0      1951        1991.0     2014  ...             0   \n",
       "2487200875         0.0      1965           0.0     2014  ...             0   \n",
       "1954400510         0.0      1987           0.0     2015  ...             0   \n",
       "7237550310         0.0      2001           0.0     2014  ...             0   \n",
       "\n",
       "            grade_Excellent  grade_Fair  grade_Good  grade_Low  \\\n",
       "id                                                               \n",
       "7129300520                0           0           0          0   \n",
       "6414100192                0           0           0          0   \n",
       "2487200875                0           0           0          0   \n",
       "1954400510                0           0           1          0   \n",
       "7237550310                1           0           0          0   \n",
       "\n",
       "            grade_LowAverage  grade_Luxury  grade_Mansion  grade_Poor  \\\n",
       "id                                                                      \n",
       "7129300520                 0             0              0           0   \n",
       "6414100192                 0             0              0           0   \n",
       "2487200875                 0             0              0           0   \n",
       "1954400510                 0             0              0           0   \n",
       "7237550310                 0             0              0           0   \n",
       "\n",
       "            grade_VeryGood  \n",
       "id                          \n",
       "7129300520               0  \n",
       "6414100192               0  \n",
       "2487200875               0  \n",
       "1954400510               0  \n",
       "7237550310               0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(data, columns=[\"view\", \"condition\" , \"grade\"], drop_first=True, dtype=int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17704 entries, 7129300520 to 1523300157\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   price                17704 non-null  float64\n",
      " 1   bedrooms             17704 non-null  int64  \n",
      " 2   bathrooms            17704 non-null  int64  \n",
      " 3   sqft_living          17704 non-null  int64  \n",
      " 4   sqft_lot             17704 non-null  int64  \n",
      " 5   floors               17704 non-null  int64  \n",
      " 6   waterfront           17704 non-null  float64\n",
      " 7   yr_built             17704 non-null  int64  \n",
      " 8   yr_renovated         17704 non-null  float64\n",
      " 9   sell_yr              17704 non-null  int64  \n",
      " 10  view_EXCELLENT       17704 non-null  int64  \n",
      " 11  view_FAIR            17704 non-null  int64  \n",
      " 12  view_GOOD            17704 non-null  int64  \n",
      " 13  view_NONE            17704 non-null  int64  \n",
      " 14  condition_Fair       17704 non-null  int64  \n",
      " 15  condition_Good       17704 non-null  int64  \n",
      " 16  condition_Poor       17704 non-null  int64  \n",
      " 17  condition_Very Good  17704 non-null  int64  \n",
      " 18  grade_Better         17704 non-null  int64  \n",
      " 19  grade_Excellent      17704 non-null  int64  \n",
      " 20  grade_Fair           17704 non-null  int64  \n",
      " 21  grade_Good           17704 non-null  int64  \n",
      " 22  grade_Low            17704 non-null  int64  \n",
      " 23  grade_LowAverage     17704 non-null  int64  \n",
      " 24  grade_Luxury         17704 non-null  int64  \n",
      " 25  grade_Mansion        17704 non-null  int64  \n",
      " 26  grade_Poor           17704 non-null  int64  \n",
      " 27  grade_VeryGood       17704 non-null  int64  \n",
      "dtypes: float64(3), int64(25)\n",
      "memory usage: 4.5 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17704, 27), (17704,))"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# spliting the data into x and y\n",
    "X = df.drop('price', axis = 1)\n",
    "y = df['price']\n",
    "X.shape , y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape:  (14163, 27) (14163,)\n",
      "Testing set shape:  (3541, 27) (3541,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and testing\n",
    "X_train, X_test ,y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42 )\n",
    "print(\"Training set shape: \", X_train.shape , y_train.shape)\n",
    "print(\"Testing set shape: \", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a basic linear regression model\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model on the training set\n",
    "model =lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3541,)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make predictions based on the training set\n",
    "y_predicted = lr.predict(X_test)\n",
    "y_predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared:  0.63294095371061\n",
      "MSE:  51731331281.42515\n",
      "MAE:  138025.63692848737\n",
      "intercept:  -39132123.255265504\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the performance of the model\n",
    "\n",
    "R_squared = r2_score(y_test,y_predicted)\n",
    "MSE = mean_squared_error(y_test,y_predicted)\n",
    "MAE = mean_absolute_error(y_test,y_predicted)\n",
    "intercept = lr.intercept_\n",
    "\n",
    "print('R_squared: ', R_squared)\n",
    "print('MSE: ', MSE)\n",
    "print('MAE: ', MAE)\n",
    "print('intercept: ',intercept )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the model\n",
    "plt.scatter(y_test, y_predicted)\n",
    "plt.plot([y.min(), y.max()] ,[y.min(), y.max()] , color= 'black')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted values')\n",
    "plt.title('Actual Values vs Predicted values(Linear Regression)')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the residuals\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(y_predicted, y_test - y_predicted)\n",
    "ax.axhline(y=0, color=\"black\")\n",
    "ax.set_xlabel(\"Predicted Values\")\n",
    "ax.set_ylabel(\"Residuals\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our model, about 63% of the variance of the price can be explained by the all the factors in the dataset hence based on the fact our model is able to moderetely predict the price of a house given the above factors.\n",
    "\n",
    "When we plot the Actual values against the predicted values we can see that our model has a moderete fit as the points are somehow close to the line indicating a moderete linear relationship between the accurate values and the predicted values.\n",
    "\n",
    "When we plot the residuals we can still conclude that our model has a moderete fit as our points are somehow scattered around the zero line.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Model : Simple linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our target variable is price. We now look at the correlation coefficients for all of the predictor variables to find the one with the highest correlation with price. We will then use that predictor to build our baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price           1.000000\n",
       "bedrooms        0.306229\n",
       "bathrooms       0.509650\n",
       "sqft_living     0.703204\n",
       "sqft_lot        0.086619\n",
       "floors          0.236302\n",
       "waterfront      0.262063\n",
       "yr_built        0.050399\n",
       "yr_renovated    0.129887\n",
       "sell_yr         0.002535\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the predictor variable with the highest correlation with price\n",
    "data.corr()['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have found out that the 'sqft_living' column has the highest co-effecient and we will use it to build our baseline model. We then initialize our X and Y variables for our simple regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a design matrix X and target variable y\n",
    "X = data[['sqft_living']]\n",
    "y = data['price']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.494\n",
      "Model:                            OLS   Adj. R-squared:                  0.494\n",
      "Method:                 Least Squares   F-statistic:                 1.732e+04\n",
      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:25   Log-Likelihood:            -2.4596e+05\n",
      "No. Observations:               17704   AIC:                         4.919e+05\n",
      "Df Residuals:                   17702   BIC:                         4.919e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const       -4.731e+04   4878.907     -9.697      0.000   -5.69e+04   -3.77e+04\n",
      "sqft_living   282.0264      2.143    131.592      0.000     277.826     286.227\n",
      "==============================================================================\n",
      "Omnibus:                    12367.185   Durbin-Watson:                   1.967\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           490037.577\n",
      "Skew:                           2.873   Prob(JB):                         0.00\n",
      "Kurtosis:                      28.125   Cond. No.                     5.65e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.65e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Library to be used in building the model\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Building the model\n",
    "baseline_model = sm.OLS(y, sm.add_constant(X))\n",
    "\n",
    "# fitting the model\n",
    "baseline_results = baseline_model.fit()\n",
    "\n",
    "# viewing the summary of the model\n",
    "print(baseline_results.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall this base model is statistically significant and explains about 49% of the variance in price. \n",
    "\n",
    "* The intercept is at about -47.3k.\n",
    "* This means that home with 0 Square footage of living space  would have a price of -47.3k dollars.\n",
    "* The coefficient for Square footage of living space is about 282.\n",
    "* This means for each additional Square footage of living space, the sale price of the home will increase with about 282 dollars more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9090492497284.77"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = ((baseline_results.resid ** 2).sum() / len(y) ** 0.5)\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error(RMSE) Interpretation\n",
    "For this RMSE value in our model, it means that our model is off by a price of about 9090492497284 in a given prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model fit (scatter plot and regression line)\n",
    "fig, ax = plt.subplots()\n",
    "df.plot.scatter(x=\"sqft_living\", y=\"price\", label=\"Data points\", ax=ax)\n",
    "sm.graphics.abline_plot(model_results=baseline_results, label=\"Regression line\", ax=ax, color = 'black')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression\n",
    "## Model 1\n",
    "Since we have our baseline model, we will then include other features that seem to have an inpact on the price for our model having multiple features.The features selected are listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_model1 = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'waterfront','floors', 'yr_built', 'yr_renovated']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then build our first Multiple linear regression model based on these features, fit the model and get its summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.588\n",
      "Model:                            OLS   Adj. R-squared:                  0.588\n",
      "Method:                 Least Squares   F-statistic:                     3159.\n",
      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:26   Log-Likelihood:            -2.4414e+05\n",
      "No. Observations:               17704   AIC:                         4.883e+05\n",
      "Df Residuals:                   17695   BIC:                         4.884e+05\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const         6.168e+06   1.53e+05     40.321      0.000    5.87e+06    6.47e+06\n",
      "bedrooms     -5.916e+04   2369.111    -24.970      0.000   -6.38e+04   -5.45e+04\n",
      "bathrooms     6.329e+04   3707.196     17.072      0.000     5.6e+04    7.06e+04\n",
      "sqft_living    296.9789      3.012     98.602      0.000     291.075     302.883\n",
      "sqft_lot        -0.3104      0.043     -7.199      0.000      -0.395      -0.226\n",
      "waterfront    7.561e+05   2.19e+04     34.536      0.000    7.13e+05    7.99e+05\n",
      "floors        5.319e+04   4217.185     12.613      0.000    4.49e+04    6.15e+04\n",
      "yr_built     -3163.7835     79.456    -39.818      0.000   -3319.526   -3008.041\n",
      "yr_renovated    12.9784      4.660      2.785      0.005       3.844      22.113\n",
      "==============================================================================\n",
      "Omnibus:                    10625.148   Durbin-Watson:                   1.977\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           359044.067\n",
      "Skew:                           2.330   Prob(JB):                         0.00\n",
      "Kurtosis:                      24.564   Cond. No.                     3.86e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.86e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model1 = sm.OLS(y, sm.add_constant(X_model1))\n",
    "results1 = model1.fit()\n",
    "print(results1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the model results\n",
    "Overally the model performed marginally better. It explained 58.8% rather than 49% of the\n",
    "variance in price.\n",
    "All of the coefficients are statistically significant  with an F-statistic and t-statistic p-values well below 0.05\n",
    "\n",
    "* The intercept is at about 6168000. Meaning when all features are at a 0 ath the year 1900, the price of a home will be 6168000.\n",
    "* For each increase of 1 in number of bedrooms, we see an associated decrease in salePrice of about 59160\n",
    "* For each increase of 1 in number of bathrooms, we see an associated increase in salePrice of about 63290\n",
    "* For each increase of 1 Square footage of living space, we see an associated increase in salePrice of about 297\n",
    "* For each increase of 1 Square footage of lot space, we see an associated negligable decrease in salePrice\n",
    "* The presence of a waterfront increase in salePrice with about 756100\n",
    "* For each increase of a floor, we see an associated increase in salePrice of about 53190\n",
    "* For each year after the year built, we see an associated decrease in salePrice of about 3163\n",
    "* For each year a house is renovated there is an associated increase in salePrice of about 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residual Plot of Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'residuals')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(10,7), sharey=True)\n",
    "\n",
    "y_predicted1 = results1.predict(sm.add_constant(X_model1))\n",
    "\n",
    "weight_ax = axes\n",
    "weight_ax.scatter(y_predicted1, results1.resid)\n",
    "weight_ax.axhline(y=0, color=\"black\")\n",
    "weight_ax.set_xlabel(\"Predicted Values\")\n",
    "weight_ax.set_ylabel(\"residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235926.6751450893"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = ((results1.resid ** 2).sum() / len(y)) ** 0.5\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error(RMSE) Interpretation\n",
    "For this RMSE value in our model, it means that our model is off by a price of about 235926 in a given prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Categorical features to the model: \n",
    "## Model 2\n",
    "For now the model is behaving quite well with the numerical features we included. We now include some categorical features to see if the model can perform any better.<br>\n",
    "We add the `condition` to see if it will have an impact on the price of the home."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_model2 = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
    "       'waterfront', 'yr_built', 'yr_renovated','condition']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding categorical columns\n",
    "When dealing with categorical features we have to create dummy variables, using one-hot encoding, that contain integer values that can be used by our model. Since we have only one categorical feature, we will perform one-hot encoding to that column only.<br>\n",
    "We also need to drop the first column of the dummy variable so that it can be used as a reference variable in the interpretation of results of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating dummy variables of the condition column and dropping its first dummy column\n",
    "X_two = pd.get_dummies(X_model2, columns=['condition'], drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>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>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>condition_Fair</th>\n",
       "      <th>condition_Good</th>\n",
       "      <th>condition_Poor</th>\n",
       "      <th>condition_VeryGood</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7129300520</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6414100192</th>\n",
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       "      <td>2</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2487200875</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>8080</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>263000018</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6600060120</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523300141</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291310100</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523300157</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17704 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "id                                                                           \n",
       "7129300520         3          1         1180      5650       1         0.0   \n",
       "6414100192         3          2         2570      7242       2         0.0   \n",
       "2487200875         4          3         1960      5000       1         0.0   \n",
       "1954400510         3          2         1680      8080       1         0.0   \n",
       "7237550310         4          4         5420    101930       1         0.0   \n",
       "...              ...        ...          ...       ...     ...         ...   \n",
       "263000018          3          2         1530      1131       3         0.0   \n",
       "6600060120         4          2         2310      5813       2         0.0   \n",
       "1523300141         2          0         1020      1350       2         0.0   \n",
       "291310100          3          2         1600      2388       2         0.0   \n",
       "1523300157         2          0         1020      1076       2         0.0   \n",
       "\n",
       "            yr_built  yr_renovated  condition_Fair  condition_Good  \\\n",
       "id                                                                   \n",
       "7129300520      1955           0.0               0               0   \n",
       "6414100192      1951        1991.0               0               0   \n",
       "2487200875      1965           0.0               0               0   \n",
       "1954400510      1987           0.0               0               0   \n",
       "7237550310      2001           0.0               0               0   \n",
       "...              ...           ...             ...             ...   \n",
       "263000018       2009           0.0               0               0   \n",
       "6600060120      2014           0.0               0               0   \n",
       "1523300141      2009           0.0               0               0   \n",
       "291310100       2004           0.0               0               0   \n",
       "1523300157      2008           0.0               0               0   \n",
       "\n",
       "            condition_Poor  condition_VeryGood  \n",
       "id                                              \n",
       "7129300520               0                   0  \n",
       "6414100192               0                   0  \n",
       "2487200875               0                   1  \n",
       "1954400510               0                   0  \n",
       "7237550310               0                   0  \n",
       "...                    ...                 ...  \n",
       "263000018                0                   0  \n",
       "6600060120               0                   0  \n",
       "1523300141               0                   0  \n",
       "291310100                0                   0  \n",
       "1523300157               0                   0  \n",
       "\n",
       "[17704 rows x 12 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the space in the column names to make them more understandable\n",
    "X_two.columns = X_two.columns.str.replace(' ', '')\n",
    "X_two"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After encoding the categorical columns we then get back to building the model, fitting it and interpreting its results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.589\n",
      "Model:                            OLS   Adj. R-squared:                  0.589\n",
      "Method:                 Least Squares   F-statistic:                     2115.\n",
      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:31   Log-Likelihood:            -2.4412e+05\n",
      "No. Observations:               17704   AIC:                         4.883e+05\n",
      "Df Residuals:                   17691   BIC:                         4.884e+05\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const               5.879e+06   1.62e+05     36.188      0.000    5.56e+06     6.2e+06\n",
      "bedrooms            -5.99e+04   2369.261    -25.282      0.000   -6.45e+04   -5.53e+04\n",
      "bathrooms           6.213e+04   3713.149     16.732      0.000    5.49e+04    6.94e+04\n",
      "sqft_living          296.4294      3.011     98.452      0.000     290.528     302.331\n",
      "sqft_lot              -0.3039      0.043     -7.044      0.000      -0.388      -0.219\n",
      "floors              5.546e+04   4253.067     13.041      0.000    4.71e+04    6.38e+04\n",
      "waterfront          7.545e+05   2.19e+04     34.492      0.000    7.12e+05    7.97e+05\n",
      "yr_built           -3019.6403     83.928    -35.979      0.000   -3184.148   -2855.133\n",
      "yr_renovated          17.2284      4.727      3.645      0.000       7.963      26.494\n",
      "condition_Fair     -4.951e+04   1.99e+04     -2.482      0.013   -8.86e+04   -1.04e+04\n",
      "condition_Good      1.311e+04   4449.683      2.946      0.003    4385.616    2.18e+04\n",
      "condition_Poor     -6.245e+04   4.93e+04     -1.266      0.205   -1.59e+05    3.42e+04\n",
      "condition_VeryGood  4.282e+04   7111.824      6.021      0.000    2.89e+04    5.68e+04\n",
      "==============================================================================\n",
      "Omnibus:                    10701.460   Durbin-Watson:                   1.978\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           368079.052\n",
      "Skew:                           2.349   Prob(JB):                         0.00\n",
      "Kurtosis:                      24.838   Cond. No.                     4.11e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.11e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model2 = sm.OLS(y, sm.add_constant(X_two))\n",
    "results2 = model2.fit()\n",
    "print(results2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the model results\n",
    "The model explained 58.9%  of the variance in price which is just similar to the previous model.\n",
    "The Model is statistically significant  with an F-statistic well below a significance level of 0.05\n",
    "Most of the coefficients are statistically significant apart from `condition_poor` which has a pvalue of 0.205\n",
    "\n",
    "The reference category for condition is `condition_average`.\n",
    "The interpretation of the results of the categorical column are as follows\n",
    "* For a home having a fair condition compared to a home with average condition, we see an associated decrease in price of about 49510\n",
    "* For a home having a good condition compared to a home with average condition, we see an associated increase in price of about 13110\n",
    "* For a home having a very good condition compared to a home with average condition, we see an associated increase in price of about 42820.<br>\n",
    "We did include the homes with poor conditions since it was not statistically significant at a significance level of 0.05\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residual Plot of Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'residuals')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(10,7), sharey=True)\n",
    "\n",
    "y_predicted2 = results2.predict(sm.add_constant(X_two))\n",
    "\n",
    "weight_ax = axes\n",
    "weight_ax.scatter(y_predicted2, results2.resid)\n",
    "weight_ax.axhline(y=0, color=\"black\")\n",
    "weight_ax.set_xlabel(\"Predicted Values\")\n",
    "weight_ax.set_ylabel(\"residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235598.03527158155"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = ((results2.resid ** 2).sum() / len(y)) ** 0.5\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error(RMSE) Interpretation\n",
    "For this RMSE value in our model, it means that our model is off by a price of about 235598 in a given prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3 with `grade`\n",
    "This model doesnt have any change as compared to the previous model, we surely need to improve this model in order to get a better performing model. We decided to use the `grade` feature in place of the condition to get which is a better feature to be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_model3 = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
    "                 'waterfront', 'yr_built', 'yr_renovated','grade']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then go ahead and do some cleaning on the grade columnn so as to make it more understandable. When we try to do value counts in the grade column, we see that its values have a mixture of numbers and words. We want to remove the numbers and remain with the words for easier understanding and interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Average       7338\n",
       "Good          4979\n",
       "Better        2168\n",
       "LowAverage    1674\n",
       "VeryGood       930\n",
       "Excellent      324\n",
       "Fair           190\n",
       "Luxury          71\n",
       "Low             18\n",
       "Mansion         11\n",
       "Poor             1\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['grade'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We the one-hot encode the `grade` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_three = pd.get_dummies(X_model3, columns=['grade'], drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.669\n",
      "Model:                            OLS   Adj. R-squared:                  0.669\n",
      "Method:                 Least Squares   F-statistic:                     1987.\n",
      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:33   Log-Likelihood:            -2.4220e+05\n",
      "No. Observations:               17704   AIC:                         4.844e+05\n",
      "Df Residuals:                   17685   BIC:                         4.846e+05\n",
      "Df Model:                          18                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const             7.115e+06   1.43e+05     49.743      0.000    6.83e+06    7.39e+06\n",
      "bedrooms         -2.659e+04   2206.262    -12.052      0.000   -3.09e+04   -2.23e+04\n",
      "bathrooms         5.261e+04   3335.205     15.774      0.000    4.61e+04    5.91e+04\n",
      "sqft_living        153.5781      3.529     43.524      0.000     146.662     160.495\n",
      "sqft_lot            -0.2745      0.039     -7.083      0.000      -0.350      -0.199\n",
      "floors            2.256e+04   3884.072      5.809      0.000    1.49e+04    3.02e+04\n",
      "waterfront         7.36e+05   1.97e+04     37.424      0.000    6.97e+05    7.75e+05\n",
      "yr_built         -3560.4006     73.806    -48.240      0.000   -3705.068   -3415.733\n",
      "yr_renovated        17.3870      4.193      4.147      0.000       9.169      25.605\n",
      "grade_Better       2.37e+05   6492.137     36.506      0.000    2.24e+05     2.5e+05\n",
      "grade_Excellent   6.736e+05   1.46e+04     46.091      0.000    6.45e+05    7.02e+05\n",
      "grade_Fair       -1.434e+05   1.58e+04     -9.072      0.000   -1.74e+05   -1.12e+05\n",
      "grade_Good        9.697e+04   4363.765     22.222      0.000    8.84e+04    1.06e+05\n",
      "grade_Low        -1.212e+05   5.01e+04     -2.420      0.016   -2.19e+05    -2.3e+04\n",
      "grade_LowAverage -9.015e+04   6064.547    -14.865      0.000   -1.02e+05   -7.83e+04\n",
      "grade_Luxury      1.141e+06   2.78e+04     40.966      0.000    1.09e+06     1.2e+06\n",
      "grade_Mansion     2.275e+06    6.6e+04     34.471      0.000    2.15e+06     2.4e+06\n",
      "grade_Poor       -8.892e+04   2.12e+05     -0.420      0.674   -5.04e+05    3.26e+05\n",
      "grade_VeryGood    4.164e+05   9334.852     44.610      0.000    3.98e+05    4.35e+05\n",
      "==============================================================================\n",
      "Omnibus:                    10178.371   Durbin-Watson:                   1.972\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           349460.879\n",
      "Skew:                           2.181   Prob(JB):                         0.00\n",
      "Kurtosis:                      24.324   Cond. No.                     5.96e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.96e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model3 = sm.OLS(y, sm.add_constant(X_three))\n",
    "results3 = model3.fit()\n",
    "print(results3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the model results\n",
    "The model explained 66.9%  of the variance in price which is a better performance as compared to the previous model.\n",
    "The Model is statistically significant  with an F-statistic well below a significance level of 0.05.\n",
    "All of the coefficients are statistically significant with pvalues way below the significance level.\n",
    "\n",
    "The reference category for `grade` is `grade_Average`.\n",
    "The interpretation of the results of the `grade` categorical column are in comparison to the reference column, `grade_10VeryGood`.\n",
    "Homes having a grade above the average grade have an associated increase in price while those that have a grade lower than the average ave an associated decrease in price.<br>\n",
    "* For a home having a Better grade compared to a home with a Average grade, we see an associated increase in price of about 237000.\n",
    "* For a home having a fair grade compared to a home with a Average grade, we see an associated decrease in price of about 143400.<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residual Plot of Model 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'residuals')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(10,7), sharey=True)\n",
    "\n",
    "y_predicted3 = results3.predict(sm.add_constant(X_three))\n",
    "\n",
    "weight_ax = axes\n",
    "weight_ax.scatter(y_predicted3, results3.resid)\n",
    "weight_ax.axhline(y=0, color=\"black\")\n",
    "weight_ax.set_xlabel(\"Predicted Values\")\n",
    "weight_ax.set_ylabel(\"residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "211466.87900318124"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = ((results3.resid ** 2).sum() / len(y)) ** 0.5\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error(RMSE) Interpretation\n",
    "For this RMSE value in our model, it means that our model is off by a price of about 211466 in a given prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4 : Including an additional categorical column; `view`\n",
    "For this new model we want to improve the previous model in order to obtain more optimal results. We are adding the `view` column to the model to see its impact to the model.<br>\n",
    "since we have two categorical columns, the same process of one-hot encoding still needs to be done and reference columns are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.678\n",
      "Model:                            OLS   Adj. R-squared:                  0.677\n",
      "Method:                 Least Squares   F-statistic:                     1690.\n",
      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:34   Log-Likelihood:            -2.4197e+05\n",
      "No. Observations:               17704   AIC:                         4.840e+05\n",
      "Df Residuals:                   17681   BIC:                         4.842e+05\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const             6.817e+06   1.42e+05     48.032      0.000    6.54e+06     7.1e+06\n",
      "bedrooms         -2.332e+04   2183.604    -10.678      0.000   -2.76e+04    -1.9e+04\n",
      "bathrooms         5.122e+04   3293.532     15.553      0.000    4.48e+04    5.77e+04\n",
      "sqft_living        144.9802      3.507     41.339      0.000     138.106     151.854\n",
      "sqft_lot            -0.2788      0.038     -7.277      0.000      -0.354      -0.204\n",
      "floors            2.682e+04   3839.921      6.985      0.000    1.93e+04    3.43e+04\n",
      "waterfront        5.271e+05   2.36e+04     22.347      0.000    4.81e+05    5.73e+05\n",
      "yr_built         -3377.3486     73.530    -45.931      0.000   -3521.475   -3233.222\n",
      "yr_renovated        14.7974      4.142      3.573      0.000       6.679      22.916\n",
      "grade_Better      2.241e+05   6441.490     34.792      0.000    2.11e+05    2.37e+05\n",
      "grade_Excellent   6.519e+05   1.45e+04     45.042      0.000    6.24e+05     6.8e+05\n",
      "grade_Fair       -1.412e+05   1.56e+04     -9.048      0.000   -1.72e+05   -1.11e+05\n",
      "grade_Good        8.985e+04   4324.765     20.777      0.000    8.14e+04    9.83e+04\n",
      "grade_Low         -1.19e+05   4.95e+04     -2.406      0.016   -2.16e+05   -2.21e+04\n",
      "grade_LowAverage -8.777e+04   5988.021    -14.657      0.000   -9.95e+04    -7.6e+04\n",
      "grade_Luxury      1.108e+06   2.75e+04     40.222      0.000    1.05e+06    1.16e+06\n",
      "grade_Mansion     2.235e+06   6.52e+04     34.282      0.000    2.11e+06    2.36e+06\n",
      "grade_Poor       -8.057e+04   2.09e+05     -0.386      0.700    -4.9e+05    3.29e+05\n",
      "grade_VeryGood    4.018e+05   9243.986     43.462      0.000    3.84e+05     4.2e+05\n",
      "view_EXCELLENT    1.955e+05   1.76e+04     11.118      0.000    1.61e+05     2.3e+05\n",
      "view_FAIR         6.208e+04   1.46e+04      4.253      0.000    3.35e+04    9.07e+04\n",
      "view_GOOD         5.435e+04   1.28e+04      4.238      0.000    2.92e+04    7.95e+04\n",
      "view_NONE        -6.529e+04   7778.903     -8.393      0.000   -8.05e+04      -5e+04\n",
      "==============================================================================\n",
      "Omnibus:                    10311.058   Durbin-Watson:                   1.973\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           403953.781\n",
      "Skew:                           2.181   Prob(JB):                         0.00\n",
      "Kurtosis:                      25.991   Cond. No.                     5.96e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.96e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X_model4 = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
    "       'waterfront', 'yr_built', 'yr_renovated','grade','view']]\n",
    "X_four = pd.get_dummies(X_model4, columns=['grade', 'view'], drop_first=True)\n",
    "X_four.columns = X_four.columns.str.replace(' ', '')\n",
    "\n",
    "model4 = sm.OLS(y, sm.add_constant(X_four))\n",
    "results4 = model4.fit()\n",
    "print(results4.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the model results\n",
    "The model explained 67.7%  of the variance in price which is 1% better than the previous model.\n",
    "The Model is statistically significant  with an F-statistic well below a significance level of 0.05.\n",
    "Most of the coefficients are statistically significant with pvalues way below the significance level, apart from grade_Poor which has a pvalue of 0.7.\n",
    "\n",
    "The reference category for `grade` is `grade_Average` and for `view` is `view_AVERAGE`.\n",
    "##### The interpretation of the results of the `grade` categorical column are in comparison to the reference column, `grade_10VeryGood`.\n",
    "Homes having a grade above the average grade have an associated increase in price while those that have a grade lower than the average ave an associated decrease in price.<br>\n",
    "* For a home having a Excellent grade compared to a home with a Average grade, we see an associated increase in price of about 651900.\n",
    "* For a home having a LowAverage grade compared to a home with a Average grade, we see an associated decrease in price of about 87770.<br>\n",
    "\n",
    "##### The interpretation of the results of the `view` categorical column are in comparison to the reference column, `view_AVERAGE`.\n",
    "* For a home having a Excellent view compared to a home with a Average view, we see an associated increase in price of about 195500.\n",
    "* For a home having a NO view compared to a home with a Average view, we see an associated decrease in price of about 65290.<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residual Plot of Model 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'residuals')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(10,7), sharey=True)\n",
    "\n",
    "y_predicted4 = results4.predict(sm.add_constant(X_four))\n",
    "\n",
    "weight_ax = axes\n",
    "weight_ax.scatter(y_predicted4, results4.resid)\n",
    "weight_ax.axhline(y=0, color=\"black\")\n",
    "weight_ax.set_xlabel(\"Predicted Values\")\n",
    "weight_ax.set_ylabel(\"residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208713.42312955743"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = ((results4.resid ** 2).sum() / len(y)) ** 0.5\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error(RMSE) Interpretation\n",
    "For this RMSE value in our model, it means that our model is off by a price of about 208713 in a given prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 5: Optimizing model 4\n",
    "We have quite a bit of features that we are using in determining the price of the homes. Are all these features relevant? Can we get to use fewer features and get a better performing model?<br>\n",
    "In order to answer these questions we just pick a few of the features and use them to build another model and we compare its performance to the other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.583\n",
      "Model:                            OLS   Adj. R-squared:                  0.583\n",
      "Method:                 Least Squares   F-statistic:                     1766.\n",
      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
      "Time:                        14:54:34   Log-Likelihood:            -2.4425e+05\n",
      "No. Observations:               17704   AIC:                         4.885e+05\n",
      "Df Residuals:                   17689   BIC:                         4.887e+05\n",
      "Df Model:                          14                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const             1.701e+05   7747.727     21.948      0.000    1.55e+05    1.85e+05\n",
      "bedrooms         -2.644e+04   2459.832    -10.750      0.000   -3.13e+04   -2.16e+04\n",
      "bathrooms         9843.8252   3485.613      2.824      0.005    3011.683    1.67e+04\n",
      "sqft_living        181.9286      3.924     46.365      0.000     174.238     189.620\n",
      "sqft_lot            -0.3450      0.043     -7.960      0.000      -0.430      -0.260\n",
      "grade_Better      1.657e+05   6966.911     23.787      0.000    1.52e+05    1.79e+05\n",
      "grade_Excellent   6.123e+05   1.62e+04     37.684      0.000     5.8e+05    6.44e+05\n",
      "grade_Fair       -3.526e+04   1.76e+04     -2.001      0.045   -6.98e+04    -717.338\n",
      "grade_Good        5.298e+04   4704.858     11.261      0.000    4.38e+04    6.22e+04\n",
      "grade_Low        -5.031e+04   5.62e+04     -0.895      0.371   -1.61e+05    5.99e+04\n",
      "grade_LowAverage -1.902e+04   6648.721     -2.860      0.004    -3.2e+04   -5984.145\n",
      "grade_Luxury       1.12e+06   3.12e+04     35.947      0.000    1.06e+06    1.18e+06\n",
      "grade_Mansion     2.224e+06   7.41e+04     30.028      0.000    2.08e+06    2.37e+06\n",
      "grade_Poor        2.827e+04   2.38e+05      0.119      0.905   -4.37e+05    4.94e+05\n",
      "grade_VeryGood    3.521e+05   1.03e+04     34.337      0.000    3.32e+05    3.72e+05\n",
      "==============================================================================\n",
      "Omnibus:                    11442.709   Durbin-Watson:                   1.968\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           404144.429\n",
      "Skew:                           2.589   Prob(JB):                         0.00\n",
      "Kurtosis:                      25.827   Cond. No.                     5.96e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.96e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X_model5 = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot','grade']]\n",
    "X_five = pd.get_dummies(X_model5, columns=['grade'], drop_first=True)\n",
    "X_five.columns = X_five.columns.str.replace(' ', '')\n",
    "\n",
    "model5 = sm.OLS(y, sm.add_constant(X_five))\n",
    "results5 = model5.fit()\n",
    "print(results5.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the model results\n",
    "The model explained 58.3%  of the variance in price which is lower as compared to the previous model.\n",
    "\n",
    "The Model is statistically significant  with an F-statistic well below a significance level of 0.05\n",
    "Most of the coefficients are statistically significant apart from `grade_Poor` and `grade_low` which have a pvalue of 0.905 and 0.371 respectively.\n",
    "\n",
    "This clearly shows that all features included in the Model 4 are of great importance to the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residual Plot of Model 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'residuals')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(10,7), sharey=True)\n",
    "\n",
    "y_predicted5 = results5.predict(sm.add_constant(X_five))\n",
    "\n",
    "weight_ax = axes\n",
    "weight_ax.scatter(y_predicted5, results5.resid)\n",
    "weight_ax.axhline(y=0, color=\"black\")\n",
    "weight_ax.set_xlabel(\"Predicted Values\")\n",
    "weight_ax.set_ylabel(\"residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237429.7629204154"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE = ((results5.resid ** 2).sum() / len(y)) ** 0.5\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error(RMSE) Interpretation\n",
    "For this RMSE value in our model, it means that our model is off by a price of about 208713 in a given prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommendation of the Model\n",
    "From the five models that we built, we have the following summarissed results for all the models\n",
    "* Baseline Model:    R-squared = 49.4 $\\;\\;\\;\\;\\;$ Root Mean Squared Error = 9090492497284\n",
    "* Model1:    R-squared = 58.8% $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ Root Mean Squared Error = 235926\n",
    "* Model2:    R-squared = 58.9% $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ Root Mean Squared Error = 235598\n",
    "* Model3:    R-squared = 66.9% $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ Root Mean Squared Error = 211466\n",
    "* Model4:    R-squared = 67.8% $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ Root Mean Squared Error = 208713 \n",
    "* Model5:    R-squared = 58.3% $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ Root Mean Squared Error = 208713"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results it will be in our best interest to pick the Model offerring the lowest error and the Highest R-squared value.<br>\n",
    "We have chosen **Model 4** as our best model since it has the best R-squared value and the least error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (learn-env)",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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