FinalNotebook (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import statements\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 81)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the file\n",
    "pd.set_option('display.max_columns', None)\n",
    "dataset = pd.read_csv(\"House_price.csv\")\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>842.787043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.50000</td>\n",
       "      <td>1460.0000</td>\n",
       "      <td>2189.50000</td>\n",
       "      <td>2919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>57.137718</td>\n",
       "      <td>42.517628</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>2433.0</td>\n",
       "      <td>69.305795</td>\n",
       "      <td>23.344905</td>\n",
       "      <td>21.0</td>\n",
       "      <td>59.00000</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>10168.114080</td>\n",
       "      <td>7886.996359</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7478.00000</td>\n",
       "      <td>9453.0000</td>\n",
       "      <td>11570.00000</td>\n",
       "      <td>215245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>6.089072</td>\n",
       "      <td>1.409947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>5.564577</td>\n",
       "      <td>1.113131</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1971.312778</td>\n",
       "      <td>30.291442</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1953.50000</td>\n",
       "      <td>1973.0000</td>\n",
       "      <td>2001.00000</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1984.264474</td>\n",
       "      <td>20.894344</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1965.00000</td>\n",
       "      <td>1993.0000</td>\n",
       "      <td>2004.00000</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>2896.0</td>\n",
       "      <td>102.201312</td>\n",
       "      <td>179.334253</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>164.00000</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>441.423235</td>\n",
       "      <td>455.610826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>368.5000</td>\n",
       "      <td>733.00000</td>\n",
       "      <td>5644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>49.582248</td>\n",
       "      <td>169.205611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>560.772104</td>\n",
       "      <td>439.543659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220.00000</td>\n",
       "      <td>467.0000</td>\n",
       "      <td>805.50000</td>\n",
       "      <td>2336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>1051.777587</td>\n",
       "      <td>440.766258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>793.00000</td>\n",
       "      <td>989.5000</td>\n",
       "      <td>1302.00000</td>\n",
       "      <td>6110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1stFlrSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1159.581706</td>\n",
       "      <td>392.362079</td>\n",
       "      <td>334.0</td>\n",
       "      <td>876.00000</td>\n",
       "      <td>1082.0000</td>\n",
       "      <td>1387.50000</td>\n",
       "      <td>5095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2ndFlrSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>336.483727</td>\n",
       "      <td>428.701456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>704.00000</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>4.694416</td>\n",
       "      <td>46.396825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1500.759849</td>\n",
       "      <td>506.051045</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1126.00000</td>\n",
       "      <td>1444.0000</td>\n",
       "      <td>1743.50000</td>\n",
       "      <td>5642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.429894</td>\n",
       "      <td>0.524736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.061364</td>\n",
       "      <td>0.245687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1.568003</td>\n",
       "      <td>0.552969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>0.380267</td>\n",
       "      <td>0.502872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>2.860226</td>\n",
       "      <td>0.822693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>1.044536</td>\n",
       "      <td>0.214462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>6.451524</td>\n",
       "      <td>1.569379</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>0.597122</td>\n",
       "      <td>0.646129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>2760.0</td>\n",
       "      <td>1978.113406</td>\n",
       "      <td>25.574285</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1960.00000</td>\n",
       "      <td>1979.0000</td>\n",
       "      <td>2002.00000</td>\n",
       "      <td>2207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>1.766621</td>\n",
       "      <td>0.761624</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>472.874572</td>\n",
       "      <td>215.394815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.00000</td>\n",
       "      <td>480.0000</td>\n",
       "      <td>576.00000</td>\n",
       "      <td>1488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>93.709832</td>\n",
       "      <td>126.526589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>168.00000</td>\n",
       "      <td>1424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>47.486811</td>\n",
       "      <td>67.575493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>23.098321</td>\n",
       "      <td>64.244246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3SsnPorch</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>2.602261</td>\n",
       "      <td>25.188169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>16.062350</td>\n",
       "      <td>56.184365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>2.251799</td>\n",
       "      <td>35.663946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>50.825968</td>\n",
       "      <td>567.402211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>6.213087</td>\n",
       "      <td>2.714762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>2007.792737</td>\n",
       "      <td>1.314964</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2007.00000</td>\n",
       "      <td>2008.0000</td>\n",
       "      <td>2009.00000</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>180052.854648</td>\n",
       "      <td>57381.565721</td>\n",
       "      <td>34900.0</td>\n",
       "      <td>154795.08415</td>\n",
       "      <td>176734.8415</td>\n",
       "      <td>191895.74415</td>\n",
       "      <td>755000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count           mean           std      min           25%  \\\n",
       "Id             2919.0    1460.000000    842.787043      1.0     730.50000   \n",
       "MSSubClass     2919.0      57.137718     42.517628     20.0      20.00000   \n",
       "LotFrontage    2433.0      69.305795     23.344905     21.0      59.00000   \n",
       "LotArea        2919.0   10168.114080   7886.996359   1300.0    7478.00000   \n",
       "OverallQual    2919.0       6.089072      1.409947      1.0       5.00000   \n",
       "OverallCond    2919.0       5.564577      1.113131      1.0       5.00000   \n",
       "YearBuilt      2919.0    1971.312778     30.291442   1872.0    1953.50000   \n",
       "YearRemodAdd   2919.0    1984.264474     20.894344   1950.0    1965.00000   \n",
       "MasVnrArea     2896.0     102.201312    179.334253      0.0       0.00000   \n",
       "BsmtFinSF1     2918.0     441.423235    455.610826      0.0       0.00000   \n",
       "BsmtFinSF2     2918.0      49.582248    169.205611      0.0       0.00000   \n",
       "BsmtUnfSF      2918.0     560.772104    439.543659      0.0     220.00000   \n",
       "TotalBsmtSF    2918.0    1051.777587    440.766258      0.0     793.00000   \n",
       "X1stFlrSF      2919.0    1159.581706    392.362079    334.0     876.00000   \n",
       "X2ndFlrSF      2919.0     336.483727    428.701456      0.0       0.00000   \n",
       "LowQualFinSF   2919.0       4.694416     46.396825      0.0       0.00000   \n",
       "GrLivArea      2919.0    1500.759849    506.051045    334.0    1126.00000   \n",
       "BsmtFullBath   2917.0       0.429894      0.524736      0.0       0.00000   \n",
       "BsmtHalfBath   2917.0       0.061364      0.245687      0.0       0.00000   \n",
       "FullBath       2919.0       1.568003      0.552969      0.0       1.00000   \n",
       "HalfBath       2919.0       0.380267      0.502872      0.0       0.00000   \n",
       "BedroomAbvGr   2919.0       2.860226      0.822693      0.0       2.00000   \n",
       "KitchenAbvGr   2919.0       1.044536      0.214462      0.0       1.00000   \n",
       "TotRmsAbvGrd   2919.0       6.451524      1.569379      2.0       5.00000   \n",
       "Fireplaces     2919.0       0.597122      0.646129      0.0       0.00000   \n",
       "GarageYrBlt    2760.0    1978.113406     25.574285   1895.0    1960.00000   \n",
       "GarageCars     2918.0       1.766621      0.761624      0.0       1.00000   \n",
       "GarageArea     2918.0     472.874572    215.394815      0.0     320.00000   \n",
       "WoodDeckSF     2919.0      93.709832    126.526589      0.0       0.00000   \n",
       "OpenPorchSF    2919.0      47.486811     67.575493      0.0       0.00000   \n",
       "EnclosedPorch  2919.0      23.098321     64.244246      0.0       0.00000   \n",
       "X3SsnPorch     2919.0       2.602261     25.188169      0.0       0.00000   \n",
       "ScreenPorch    2919.0      16.062350     56.184365      0.0       0.00000   \n",
       "PoolArea       2919.0       2.251799     35.663946      0.0       0.00000   \n",
       "MiscVal        2919.0      50.825968    567.402211      0.0       0.00000   \n",
       "MoSold         2919.0       6.213087      2.714762      1.0       4.00000   \n",
       "YrSold         2919.0    2007.792737      1.314964   2006.0    2007.00000   \n",
       "SalePrice      2919.0  180052.854648  57381.565721  34900.0  154795.08415   \n",
       "\n",
       "                       50%           75%       max  \n",
       "Id               1460.0000    2189.50000    2919.0  \n",
       "MSSubClass         50.0000      70.00000     190.0  \n",
       "LotFrontage        68.0000      80.00000     313.0  \n",
       "LotArea          9453.0000   11570.00000  215245.0  \n",
       "OverallQual         6.0000       7.00000      10.0  \n",
       "OverallCond         5.0000       6.00000       9.0  \n",
       "YearBuilt        1973.0000    2001.00000    2010.0  \n",
       "YearRemodAdd     1993.0000    2004.00000    2010.0  \n",
       "MasVnrArea          0.0000     164.00000    1600.0  \n",
       "BsmtFinSF1        368.5000     733.00000    5644.0  \n",
       "BsmtFinSF2          0.0000       0.00000    1526.0  \n",
       "BsmtUnfSF         467.0000     805.50000    2336.0  \n",
       "TotalBsmtSF       989.5000    1302.00000    6110.0  \n",
       "X1stFlrSF        1082.0000    1387.50000    5095.0  \n",
       "X2ndFlrSF           0.0000     704.00000    2065.0  \n",
       "LowQualFinSF        0.0000       0.00000    1064.0  \n",
       "GrLivArea        1444.0000    1743.50000    5642.0  \n",
       "BsmtFullBath        0.0000       1.00000       3.0  \n",
       "BsmtHalfBath        0.0000       0.00000       2.0  \n",
       "FullBath            2.0000       2.00000       4.0  \n",
       "HalfBath            0.0000       1.00000       2.0  \n",
       "BedroomAbvGr        3.0000       3.00000       8.0  \n",
       "KitchenAbvGr        1.0000       1.00000       3.0  \n",
       "TotRmsAbvGrd        6.0000       7.00000      15.0  \n",
       "Fireplaces          1.0000       1.00000       4.0  \n",
       "GarageYrBlt      1979.0000    2002.00000    2207.0  \n",
       "GarageCars          2.0000       2.00000       5.0  \n",
       "GarageArea        480.0000     576.00000    1488.0  \n",
       "WoodDeckSF          0.0000     168.00000    1424.0  \n",
       "OpenPorchSF        26.0000      70.00000     742.0  \n",
       "EnclosedPorch       0.0000       0.00000    1012.0  \n",
       "X3SsnPorch          0.0000       0.00000     508.0  \n",
       "ScreenPorch         0.0000       0.00000     576.0  \n",
       "PoolArea            0.0000       0.00000     800.0  \n",
       "MiscVal             0.0000       0.00000   17000.0  \n",
       "MoSold              6.0000       8.00000      12.0  \n",
       "YrSold           2008.0000    2009.00000    2010.0  \n",
       "SalePrice      176734.8415  191895.74415  755000.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(bins = 20 , figsize= (12,16));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC           99.657417\n",
       "MiscFeature      96.402878\n",
       "Alley            93.216855\n",
       "Fence            80.438506\n",
       "FireplaceQu      48.646797\n",
       "LotFrontage      16.649538\n",
       "GarageCond        5.447071\n",
       "GarageYrBlt       5.447071\n",
       "GarageFinish      5.447071\n",
       "GarageQual        5.447071\n",
       "GarageType        5.378554\n",
       "BsmtCond          2.809181\n",
       "BsmtExposure      2.809181\n",
       "BsmtQual          2.774923\n",
       "BsmtFinType2      2.740665\n",
       "BsmtFinType1      2.706406\n",
       "MasVnrType        0.822199\n",
       "MasVnrArea        0.787941\n",
       "MSZoning          0.137033\n",
       "Utilities         0.068517\n",
       "BsmtHalfBath      0.068517\n",
       "BsmtFullBath      0.068517\n",
       "Functional        0.068517\n",
       "Exterior1st       0.034258\n",
       "TotalBsmtSF       0.034258\n",
       "BsmtUnfSF         0.034258\n",
       "BsmtFinSF2        0.034258\n",
       "GarageArea        0.034258\n",
       "KitchenQual       0.034258\n",
       "GarageCars        0.034258\n",
       "                   ...    \n",
       "MSSubClass        0.000000\n",
       "Neighborhood      0.000000\n",
       "HeatingQC         0.000000\n",
       "ExterQual         0.000000\n",
       "TotRmsAbvGrd      0.000000\n",
       "YrSold            0.000000\n",
       "MoSold            0.000000\n",
       "MiscVal           0.000000\n",
       "PoolArea          0.000000\n",
       "ScreenPorch       0.000000\n",
       "X3SsnPorch        0.000000\n",
       "EnclosedPorch     0.000000\n",
       "OpenPorchSF       0.000000\n",
       "WoodDeckSF        0.000000\n",
       "PavedDrive        0.000000\n",
       "Fireplaces        0.000000\n",
       "KitchenAbvGr      0.000000\n",
       "ExterCond         0.000000\n",
       "BedroomAbvGr      0.000000\n",
       "HalfBath          0.000000\n",
       "FullBath          0.000000\n",
       "GrLivArea         0.000000\n",
       "LowQualFinSF      0.000000\n",
       "X2ndFlrSF         0.000000\n",
       "X1stFlrSF         0.000000\n",
       "CentralAir        0.000000\n",
       "SaleCondition     0.000000\n",
       "Heating           0.000000\n",
       "Foundation        0.000000\n",
       "Id                0.000000\n",
       "Length: 81, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for percentage of null values\n",
    "dataset.isnull().sum().sort_values(ascending = False)/len(dataset)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping all columns with more than 10% NA values\n",
    "dataset = dataset.drop(columns =['PoolQC','MiscFeature','Alley','Fence','FireplaceQu', 'LotFrontage'], axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fetching list of numerical features\n",
    "mylist = list(dataset.select_dtypes(include=['int64']).columns)+list(dataset.select_dtypes(include=['float64']).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageYrBlt      159\n",
       "MasVnrArea        23\n",
       "BsmtHalfBath       2\n",
       "BsmtFullBath       2\n",
       "GarageCars         1\n",
       "TotalBsmtSF        1\n",
       "BsmtUnfSF          1\n",
       "BsmtFinSF2         1\n",
       "BsmtFinSF1         1\n",
       "GarageArea         1\n",
       "SalePrice          0\n",
       "YearRemodAdd       0\n",
       "GrLivArea          0\n",
       "LowQualFinSF       0\n",
       "X2ndFlrSF          0\n",
       "X1stFlrSF          0\n",
       "MSSubClass         0\n",
       "YearBuilt          0\n",
       "OverallCond        0\n",
       "OverallQual        0\n",
       "LotArea            0\n",
       "HalfBath           0\n",
       "FullBath           0\n",
       "OpenPorchSF        0\n",
       "BedroomAbvGr       0\n",
       "KitchenAbvGr       0\n",
       "TotRmsAbvGrd       0\n",
       "Fireplaces         0\n",
       "WoodDeckSF         0\n",
       "EnclosedPorch      0\n",
       "X3SsnPorch         0\n",
       "ScreenPorch        0\n",
       "PoolArea           0\n",
       "MiscVal            0\n",
       "MoSold             0\n",
       "YrSold             0\n",
       "Id                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for NA values in numerical feature\n",
    "dataset[mylist].isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing all NA values with median values\n",
    "for i in mylist:\n",
    "    dataset[i].fillna(value = dataset[i].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice        0\n",
       "WoodDeckSF       0\n",
       "TotRmsAbvGrd     0\n",
       "KitchenAbvGr     0\n",
       "BedroomAbvGr     0\n",
       "HalfBath         0\n",
       "FullBath         0\n",
       "GrLivArea        0\n",
       "LowQualFinSF     0\n",
       "X2ndFlrSF        0\n",
       "X1stFlrSF        0\n",
       "YearRemodAdd     0\n",
       "YearBuilt        0\n",
       "OverallCond      0\n",
       "OverallQual      0\n",
       "LotArea          0\n",
       "MSSubClass       0\n",
       "Fireplaces       0\n",
       "OpenPorchSF      0\n",
       "GarageArea       0\n",
       "EnclosedPorch    0\n",
       "GarageCars       0\n",
       "GarageYrBlt      0\n",
       "BsmtHalfBath     0\n",
       "BsmtFullBath     0\n",
       "TotalBsmtSF      0\n",
       "BsmtUnfSF        0\n",
       "BsmtFinSF2       0\n",
       "BsmtFinSF1       0\n",
       "MasVnrArea       0\n",
       "YrSold           0\n",
       "MoSold           0\n",
       "MiscVal          0\n",
       "PoolArea         0\n",
       "ScreenPorch      0\n",
       "X3SsnPorch       0\n",
       "Id               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying NA values count\n",
    "dataset[mylist].isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb579b6d30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the distribution of salesPrice\n",
    "sns.distplot(dataset['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb574ae828>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G0PM99L6RRFleT6QURQPd3d9W5PGvAl8tW0UiAdjbM0JrLHzKp52bqr0pAsCxIQW6VI9WioqQm+HS1UJu9HDOYuEGmiIhjg6Nl+X1REox9wm3IvPY7Q8cBGB79yBndLacuD1XZsby9hhHh9VDl+pRD10WvEQqzeDYBF1lGm7JW9beqB66VJUCXRa83nh2amFXa3kDfXl7I8cU6FJFCnRZ8HpywyLl7qEvb4txdCiBJn1JtSjQZcHrGU5gwNKWaFlfd3l7I2MTaYYTqbK+rsh0FOiy4PXEEyxpiRIOlffrkD8vqYZdpFoU6LLg9Q4nyj7cAtkeOmS3FRCpBgW6LGgZd3rjibIfEIXJga4eulSHAl0WtOOjE6QyXpEeen7VqXroUi0KdFnQeoazvedKBHpLLExbLKweulSNAl0WtBNTFisw5ALZA6PHhhXoUh0KdFnQeuIJmqMhmstw2rlCVixq1JCLVI0CXRa0ngrNcMlb3qbl/1I9CnRZ0HqGKzPDJW9ZeyPHtFpUqkSBLgvW8dEkI8l0ZXvo7TGS6YzOXCRVoUCXBWtvzwhQuQOiMGkuug6MShUo0GXByp9HtNI9dNBcdKkOBbosWHt74oQajMXN5d2Ua7Jlbbke+qB66FJ5CnRZsPYeG2FpS5RQQ3lOO1fIshM9dAW6VJ4CXRasfT3xig63AMTCITqaIxpDl6ooGuhmdquZHTOzx6Z53MzsK2a2x8y2m9mG8pcpUl7JVIYD/aMVD3TIHhg9MqgxdKm8Unro3wKumuHx1wHrc5cbgK/NvSyRytrXGyed8RMbaFXC7Q8cPHHS6R3PHC/bCahFplM00N39fqB/hibXAP/oWb8FFpvZaeUqUKQSHjl4HIBVHc0Vf6+u1hh98SQZLS6SCivHGPpK4NCk2925+57HzG4wsy1mtqWnp6cMby1yah4+MEBHc6Tsp50rpLM1RirjDGpxkVRYOQK90BSBgl0Rd7/F3Te5+6aurq4yvLXIqXnk0HEuXtOBWeVmuOR15oZ1euIaR5fKKkegdwOrJ91eBTxbhtcVqYjB0Qn2HIuzYc3iqrxfZ2v2fwG9CnSpsHIE+mbg3bnZLi8FBt39cBleV6QitnVnx88vXtNRlfdrjYWJhRsU6FJxRTeBNrPvAFcCnWbWDXweiAC4+83Aj4HXA3uAUeC9lSpWpBwePjCAGVy4ejEH+kYr/n5mRldbjN7hZMXfSxa2ooHu7m8r8rgDHypbRSIV9sih47xgeRutFTqpRSGdrTGe7h2p2vvJwqSVorKgZDLOIwcHqjbcktfZGmVwbIKxZLqq7ysLiwJdFpR9vXGGx1NcXKUDonmduS161UuXSlKgy4Ly8IHsAdENVe+hZwN9X2+8qu8rC4sCXRaUe3YeobM1ypmdLVV93xM99B710KVyFOiyYOztifMfu47xjkvX0lDBLXMLiYYbWNQUYZ+GXKSCFOiyYPzDr54mGm7gnS9dG8j7d7ZGFehSUQp0WRAGRpLcubWbN120sipb5hbS2RpjX08c1yZdUiHVm4grEqBPfX874xMZli9qDGwb287WGMPjKXqGEyzLnTxapJzUQ5e6d3hwjF/t6WX9slZWBBikqzuaANh6YCCwGqS+qYcudWVq7zsxkebr9+8jlXFe96Jgt+k/vaOJxkgDDzzdH3gtUp/UQ5e6lc4433noIMeGx3n7JWsC7Z0DhBsa2Li2gwefnul8MSKnToEudeuenUfYfTTONReuZP3ytqDLAeCSdUt54siQTnYhFaFAl7r06KHj/HJPL5eduZSXnLEk6HJOuOSMJbjDlgPqpUv5KdCl7hweHOOuR7pZu7SZ19fYWPXFaxYTDWXH0UXKTYEudSWZyvCdBw/RGAnx9kvWEKryitBiGiMhLly9SIEuFaFAl7pyz84j9MYTvGXjatoaI0GXU9AlZyzhsWcGGUmkgi5F6owCXerGL57q4Tf7+rj8rKWcvaw16HKmdekZS0lnXPPRpewU6FIXjo8m+cT3ttPVFuP3LlgRdDkz2rC2g3CD8fPdPUGXInVGgS7znrvzyTu30zeS4I82riYSqu1f69ZYmNecv5zvP9zN+ITOYCTlU9u/+SIluO3X+/np40f59OvOY2VueX2te9dlazk+OsHmR58NuhSpIwp0mdcee2aQ//njXbz6vGW874p1QZdTssvOXMr6Za38028OaPdFKZuSAt3MrjKzJ81sj5l9usDj15tZj5lty10+UP5SRU5226/3875vPURjpIGXnrGU7zx4KOiSSmZmvPuytex4ZpBth44HXY7UiaKbc5lZCPg74DVAN/CQmW1298enNL3D3T9cgRpFCvr3J45ybDjB9Zevozk2P/aZm7x5WCrtxMIN/MUPH+cHH7oiwKqkXpTSQ78E2OPu+9w9CXwXuKayZYnMbOuBfn75VC8vWbeEc2pkn5bZikVCbFzbwaOHjmvDLimLUgJ9JTD5/7LdufumerOZbTezO81sdaEXMrMbzGyLmW3p6dGULTk14xNp/vx721ncHOH1L6ztKYrFvOa85SxpifKx7z7C8dFk0OXIPFdKoBdaOz31KM4PgXXu/mLg34HbCr2Qu9/i7pvcfVNXV9fsKhXJuem+p3i6d4Q3XbyKWCQUdDlzEouEuO4la+iJJ/jU97frAKnMSSmB3g1M7nGvAk6aa+Xufe6eyN38BrCxPOWJnGzXkSG+/vN9vHnDqppeDTobKzua+NRV53LPzqN87ed7gy5H5rFSAv0hYL2ZnWFmUeA6YPPkBmY2eUu7q4EnyleiSFYm43zmrh20NYb53O+fF3Q5ZfW+K87gmotO50v/9iQ/eOSZoMuRearo1AB3T5nZh4F7gBBwq7vvNLMvAFvcfTPwETO7GkgB/cD1FaxZFqiv/Xwvjxw8zt+85UKWtESDLqesGhqML137Yo4NJfjEnY+yrC3G5Wd3Bl2WzDMW1Jjdpk2bfMuWLYG8t8w/P3vyGO/91kNcfeHp/O1bL8LMnnf+0Howlkzz9fv3Ek+k+C+vWs8Hrzwr6JKkxpjZVnffVOgxrRSVmnegb4SPfOcRzl3Rzhf/8MWY1dYe5+XUFA3xjkvXkko7dzx0iFQ6E3RJMo8o0KWmHeof5d23PkhDg3HLuzbSFJ3fs1pK0dUW45qLTmd/3whfuW9P0OXIPKJAl5q168gQb/7arxkcm+Afrn8Jq5c0B11S1Vy8poMNazq46b6n+PWe3qDLkXlifqyXlgXnX7cf5jN3bacpGuI9l63jicPDPHF4OOiyqurqC09ncCzJR+/Yxk8++jI6W2NBlyQ1Tj10qSk9wwk+fsc2PnT7w5zR2cKdf3I5y9sbgy4rENFwA199+waGxib4+B3byGS06Ehmph66BGLyDBV358kjwzw7OMZ/PpndEuJjr17Ph155ds2frKLSzjutnc+/8QI+e/cOvvhvu/jM686t64PCMjcKdAnUU8eGuffxo3QPjNHWGOaKs5ayce0SutpifG9Ld9Dl1YS3XbKaJw4Pccv9+5hIZ7jxDecr1KUgBboE5ld7evnXHYdZ3BThzRtWctHqDkINCqrJ8v+TOXdFG5eftZR/+NV+th08zs3v2rhgh6Jkegp0CcSuI0P8eMdhLji9nbduWk14gQ+tFGNm/P6LTiMWDvHz3ce44ov3sWFtBxec3s7aJS1Eww28/dI1QZcpAVOgS9XtOjLEdx86xGmLG3nLRoV5qcyM15y/nI1rO/j57mNs3T/Ag0/302CwrK2R3+zr49wVbbzinC4uOL1dwzILkJb+S1UNj0/whpt+Sf9Ikj+98mwWNUWCLmneGp9Ic7B/lKd7RzgyOM6x4XEGRicAaG8M88pzl3HJuiW846VrA65Uymmmpf/qoUvVuDufu/sxDvWP8v7fOVNhPkeNkRDnLG876YxN8USK3UeG2XpwgH/Z9iz7e0f4g4tX0jJPTtEnc6P/60rVfG9rN5sffZaPv/oczuhsCbqcutQaC7NhbQfv/50zeO35y9nePcgf/v2v6Ysnij9Z5j0FulTFfbuOcuO/PMZlZy7lT195dtDl1L0GM658wTKuv2Id+/tGeOc3H9Qp7hYABbpU3Ld+9TQfuG0LZy9r5Stvu1hTE6to/bI2vvHuTew9Fufdtz7IsaHxgu2eOT7Gof5R4omUToM3j2lgTSrmkYMDfOauHew6Msx5p7Vz7YbV3Pv40aDLWnBefk4XX3vnBj747Yf53S//nM+87jxe/6IVfPOXT3Oof4ytB/o5NDB2ov3apc188vfO5fUvWqGZMvOMZrnI86TSGbY/M8hv9/Wx7eBxjo9NEB9PEQ4Z7Y0ROlqirOpoYnVHM6uXZH8ubo6QTGfoH0ly/+4e7n38KA/tH6ApEuJl6zt5+TldNCgcApGfn/507wifvWsHv9nXd9Ljy9pibFjTQUssxPrlbdz98DM8eXSYTWs7+B9veiHnrmgPomyZxkyzXBTockIm4/xw+7N8+d7dHOgbBaCzNUprLEJjpIGMO+MTGeKJFIOjE6Rn+N05d0Ubf7hhJZFQA7Fw/e9hPl+4O489O8TgaJJFzVE6W6OsaG88qSeecWfr/gF++vgRxicyXPmCLr72zo1EwxqhrQUKdJnR7Q8c5NjwON/b0s0zx8dY0d7IK87p4qxlrbROM90t487Q2AQDoxMMjCQ5//R2ouEGmqMhLj1zKSsXN514bZmfRhIpfrT9WR7tHuSsrhZufOMFvOKcrqDLWvAU6DKjT965nR888gzhUHZ5+YWrF2t4RE7YdWSI+3f3sL9vlJef08W1G1fxu+cuoyUWZnwizZ5jcR7a38+OZwYZSaRIpDKcvriJy89aymVnLmWp9nEvKwW6FDQwkuQLP3qcux95hrVLm7nuJWu02EcKSqUz/HpvH7/a08twIkWowTAgNWmP9vbGME3REOGGBnrjCRKpDAasX97Kxas7+NwbzqO9Ub9fc6VAl5MkUmk2b3uWL/5kF4NjE7xsfRevOneZphNKURl3DvSN8uSRYRynKRJicXOUdUubWdwcPdEunXGePT7GE4eH2HYoe2C9weBFqxZz8erFrFnSzJolzaxekj2w3hzVhLtSzTnQzewq4P8CIeD/ufsXpzweA/4R2Aj0AW919/0zvaYCfXqpdIaB0Qn6R5L0jSQYTaRpb4rQ0RxhcXOUxc0RIqEGJtIZRhNp+kYS9MaTpNIZIuEGmiIhutpiLG2JEg41kMk4R4fH2dE9yANP93PXw90MjE7wopWL+NK1L+aRg8eD/iNLHcv/I7DnWJy9PXGODI6TTGdOatPZGmVVRzbkV3U0saoj/7OJ0xc30RjRgfW8OQW6mYWA3cBrgG7gIeBt7v74pDZ/CrzY3f/EzK4D3uTub53pdcsR6JmMk0xnSKYzjCXT9I8kGRhNMjAywcBokvGJdL6+7M8T9WZ7EGPJNGMTacyyK+saIyE6coG5uOm58FzcHKEpEippTm4m44wkU4wk0owkU8THUxwdGufocIKjg+McGRqnL55gbCLN+ESG8Yk0iVSGxESaiYyTTGUYGpug2D+zoQYjXeSUZPlyJ/8VNxicf1o7LzljCWd1tWqsXKrO3RlJphkYSdI/msz+nHR9cGyCqb/ajZEG2hojrF/WyvL2Rpa1xehqi9HRHCXjTjrjxBMphsZTDI1NMDQ2QTyRnWobDTUQDecuoRCRsBE76b4GouEQsXBD7vue/d53NEdpjYVPfI/y35RkOsPweIrh8Yncz+wlnphgIu20NYZpb4rQ3hhhUVOY9sYI7U0RYuGGsszrn+vmXJcAe9x9X+7FvgtcAzw+qc01wH/LXb8T+KqZmVdgPOffHjvMx+94lGQ6UzTQStEwKfRmejWz7F/o5H8cnvuLfu5vPJnKFHx+/r1aY2FaG8NEQw1Ecpe2xjAdzVFCDdmgbo6GaYmGaImFaYmFiYUbGJtIM5rMX1Kk0k40nH1+SzREa2OYcEMDqUyGZCqT+wXLrvprMKM5GmLl4iZWLGrS9DMJlJllvwexMKuXND/v8ZNmUI0mswGdC9DugewwztB4quD334BYJPu/1Fg4RDoX9ql0hlQmez2d8ZPG/qsl1GDZixkfeNkZ/NlrX1D29ygl0FcChybd7gYuna6Nu6fMbBBYCoV9ZjUAAAahSURBVPRObmRmNwA35G7GzezJUyl6Djqn1jRPqO7qm6+1z9e6Yf7WPuu6/zx3OUXT7odcSqAX+j/C1H/eSmmDu98C3FLCe1aEmW2Z7r8qtUx1V998rX2+1g3zt/ZaqruU/3t3A6sn3V4FPDtdGzMLA4uA/nIUKCIipSkl0B8C1pvZGWYWBa4DNk9psxl4T+76tcB9lRg/FxGR6RUdcsmNiX8YuIfstMVb3X2nmX0B2OLum4FvAv9kZnvI9syvq2TRcxDYcM8cqe7qm6+1z9e6Yf7WXjN1B7awSEREykvz10RE6oQCXUSkTtRFoJvZrWZ2zMwem3TfEjO718yeyv3smOa5aTPblrtMPdhbUdPU/RYz22lmGTObdiqUmV1lZk+a2R4z+3R1Kj7x3nOpe7+Z7ch93lXf+2Ga2v/azHaZ2XYzu9vMFk/z3Fr7zEutuxY/8/+eq3ubmf3UzE6f5rnvyX2HnzKz9xRqUylzrDuYXHH3eX8BXg5sAB6bdN+XgE/nrn8a+KtpnhuvsbrPA14A/AzYNM3zQsBe4EwgCjwKnF/rdefa7Qc6a+wzfy0Qzl3/q0K/KzX6mRetu4Y/8/ZJ1z8C3FzgeUuAfbmfHbnrHbVed+6xQHKlLnro7n4/z5/3fg1wW+76bcAfVLWoEhSq292fcPdiK2hPbMfg7kkgvx1DVcyh7sBNU/tP3T2Vu/lbsmstpqrFz7yUugM3Te1Dk262UHjnjd8D7nX3fncfAO4FrqpYoVPMoe7A1EWgT2O5ux8GyP1cNk27RjPbYma/NbOaC/1pFNqOYWVAtcyWAz81s625rSBqzfuAnxS4v9Y/8+nqhhr9zM3sL83sEPAO4MYCTWryMy+hbggoV+o50Eu1xrPLdt8O/K2ZnRV0QSUoaauFGnWFu28AXgd8yMxeHnRBeWb2OSAFfLvQwwXuq4nPvEjdUKOfubt/zt1Xk637wwWa1ORnXkLdEFCu1HOgHzWz0wByP48VauTuz+Z+7iM7/ntxtQqcg1K2Y6hJkz7vY8DdZIcyApc74PYG4B2eGwSdoiY/8xLqrtnPfJLbgTcXuL8mP/NJpqs7sFyp50CfvB3Be4B/mdrAzDose3IOzKwTuIKTtwWuVaVsx1BzzKzFzNry18ke1Hts5mdVnmVP4PIp4Gp3H52mWc195qXUXcOf+fpJN68GdhVodg/w2tz3tINs7fdUo77plFJ3oLkSxJHYcl+A7wCHgQmy/6q/n+z2vf8BPJX7uSTXdhPZsy4BXA7sIDtjYQfw/hqo+0256wngKHBPru3pwI8nPff1ZE88shf43Hyom+wMkUdzl53VrnuG2veQHavdlrvcPE8+86J11/Bn/n2y/7BsB34IrMy1PfH9zN1+X+7PuQd473yoO8hc0dJ/EZE6Uc9DLiIiC4oCXUSkTijQRUTqhAJdRKROKNBFROqEAl3mNTP7XG6Xx/wOeJfO0PZbZnZtkdf7lpk9nXuth83ssmna/YmZvXuu9YuUU9FT0InUqlzYvgHY4O6J3CKOaBle+hPufqeZvRb4OvDiKe8bdveby/A+ImWlQJf57DSg190TAO7eC2BmNwJvBJqAXwN/7FMWXJjZRuDLQCvQC1zvuc3cJrkfODvX/me517oC2JxbfRl39/9tZmcDNwNdQBp4i7vvNbNPAH8ExIC73f3zZf7zi5xEQy4yn/0UWG1mu83s783sFbn7v+ruL3H3F5IN9TdMfpKZRYCbgGvdfSNwK/CXBV7/jWRX+uUtdvdXuPvfTGn3beDv3P1CsqsED+d69+vJ7ptyEbCxVjbFkvqlHrrMW+4ez/W0Xwa8ErgjdyahYTP7JNBM9uQIO8ku0857AfBC4F4zg+zJKyb3zv/azP4r0EN2uXfeHVNryPXUV7r73bmaxnP3v5bs3iOP5Jq2kg34++fyZxaZiQJd5jV3T5Pdze5nZrYD+GOyY96b3P2Qmf03oHHK0wzY6e4FD3iSG0MvcP9IgfsKbfGav/9/ufvXi/wRRMpGQy4yb5nZC6bsfncRkD9rUq+ZtQKFZrU8CXTlZ7CYWcTMLjiVGjx7Bpvu/EkMzCxmZs1kdwV8X64GzGylmU13khWRslAPXeazVuCm3MmRU2R35LsBOE527Hs/2W1vT+Luydz0xa+Y2SKy34O/JTs0cyreBXzdzL5Adme+t7j7T83sPOA3uWGdOPBOptmXX6QctNuiiEid0JCLiEidUKCLiNQJBbqISJ1QoIuI1AkFuohInVCgi4jUCQW6iEid+P8dh6i6dXGN4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the distribution of the log of sales price\n",
    "x = np.log(dataset['SalePrice'])\n",
    "sns.distplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding new variables for log of sales price and age of the house\n",
    "dataset['logSales'] = np.log(dataset['SalePrice'])\n",
    "dataset['age'] = dataset['age'] = dataset['YrSold'] - dataset['YearBuilt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrmat = dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorting all the columns according to there correlation with logSales\n",
    "sorted_corr = abs(corrmat[\"logSales\"]).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "top20 = sorted_corr[:21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data taking only the top 20 features that are highly correlated with logSales\n",
    "data = dataset[top20.index].drop('SalePrice', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2919 entries, 0 to 2918\n",
      "Data columns (total 20 columns):\n",
      "logSales        2919 non-null float64\n",
      "GrLivArea       2919 non-null int64\n",
      "OverallQual     2919 non-null int64\n",
      "GarageCars      2919 non-null float64\n",
      "GarageArea      2919 non-null float64\n",
      "TotRmsAbvGrd    2919 non-null int64\n",
      "FullBath        2919 non-null int64\n",
      "X1stFlrSF       2919 non-null int64\n",
      "TotalBsmtSF     2919 non-null float64\n",
      "age             2919 non-null int64\n",
      "YearBuilt       2919 non-null int64\n",
      "YearRemodAdd    2919 non-null int64\n",
      "Fireplaces      2919 non-null int64\n",
      "GarageYrBlt     2919 non-null float64\n",
      "MasVnrArea      2919 non-null float64\n",
      "LotArea         2919 non-null int64\n",
      "X2ndFlrSF       2919 non-null int64\n",
      "BedroomAbvGr    2919 non-null int64\n",
      "BsmtFinSF1      2919 non-null float64\n",
      "HalfBath        2919 non-null int64\n",
      "dtypes: float64(7), int64(13)\n",
      "memory usage: 456.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>logSales</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>X1stFlrSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>age</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>X2ndFlrSF</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>HalfBath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logSales</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.578486</td>\n",
       "      <td>0.563275</td>\n",
       "      <td>0.494292</td>\n",
       "      <td>0.481480</td>\n",
       "      <td>0.468042</td>\n",
       "      <td>0.457031</td>\n",
       "      <td>0.453791</td>\n",
       "      <td>0.448304</td>\n",
       "      <td>-0.403951</td>\n",
       "      <td>0.402435</td>\n",
       "      <td>0.382440</td>\n",
       "      <td>0.364122</td>\n",
       "      <td>0.328523</td>\n",
       "      <td>0.316480</td>\n",
       "      <td>0.285116</td>\n",
       "      <td>0.270448</td>\n",
       "      <td>0.265515</td>\n",
       "      <td>0.259284</td>\n",
       "      <td>0.248845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.578486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.575126</td>\n",
       "      <td>0.489958</td>\n",
       "      <td>0.485441</td>\n",
       "      <td>0.808354</td>\n",
       "      <td>0.630286</td>\n",
       "      <td>0.562538</td>\n",
       "      <td>0.445223</td>\n",
       "      <td>-0.243475</td>\n",
       "      <td>0.242666</td>\n",
       "      <td>0.316972</td>\n",
       "      <td>0.459386</td>\n",
       "      <td>0.263064</td>\n",
       "      <td>0.400088</td>\n",
       "      <td>0.284519</td>\n",
       "      <td>0.655085</td>\n",
       "      <td>0.517058</td>\n",
       "      <td>0.211682</td>\n",
       "      <td>0.433548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.563275</td>\n",
       "      <td>0.575126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.600591</td>\n",
       "      <td>0.565056</td>\n",
       "      <td>0.389761</td>\n",
       "      <td>0.528483</td>\n",
       "      <td>0.479152</td>\n",
       "      <td>0.549157</td>\n",
       "      <td>-0.597523</td>\n",
       "      <td>0.597554</td>\n",
       "      <td>0.571532</td>\n",
       "      <td>0.390753</td>\n",
       "      <td>0.542218</td>\n",
       "      <td>0.426528</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.245596</td>\n",
       "      <td>0.073075</td>\n",
       "      <td>0.281784</td>\n",
       "      <td>0.272668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0.494292</td>\n",
       "      <td>0.489958</td>\n",
       "      <td>0.600591</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.889689</td>\n",
       "      <td>0.358012</td>\n",
       "      <td>0.480012</td>\n",
       "      <td>0.440452</td>\n",
       "      <td>0.437900</td>\n",
       "      <td>-0.537866</td>\n",
       "      <td>0.537663</td>\n",
       "      <td>0.426053</td>\n",
       "      <td>0.321533</td>\n",
       "      <td>0.484947</td>\n",
       "      <td>0.357659</td>\n",
       "      <td>0.180415</td>\n",
       "      <td>0.182532</td>\n",
       "      <td>0.092892</td>\n",
       "      <td>0.255510</td>\n",
       "      <td>0.234435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>0.481480</td>\n",
       "      <td>0.485441</td>\n",
       "      <td>0.565056</td>\n",
       "      <td>0.889689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.328679</td>\n",
       "      <td>0.408453</td>\n",
       "      <td>0.491979</td>\n",
       "      <td>0.486021</td>\n",
       "      <td>-0.480382</td>\n",
       "      <td>0.480507</td>\n",
       "      <td>0.376741</td>\n",
       "      <td>0.294713</td>\n",
       "      <td>0.469271</td>\n",
       "      <td>0.370945</td>\n",
       "      <td>0.213249</td>\n",
       "      <td>0.128548</td>\n",
       "      <td>0.073914</td>\n",
       "      <td>0.310454</td>\n",
       "      <td>0.178966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>0.468042</td>\n",
       "      <td>0.808354</td>\n",
       "      <td>0.389761</td>\n",
       "      <td>0.358012</td>\n",
       "      <td>0.328679</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.527337</td>\n",
       "      <td>0.391782</td>\n",
       "      <td>0.282082</td>\n",
       "      <td>-0.115514</td>\n",
       "      <td>0.114280</td>\n",
       "      <td>0.198250</td>\n",
       "      <td>0.311260</td>\n",
       "      <td>0.154603</td>\n",
       "      <td>0.277103</td>\n",
       "      <td>0.213802</td>\n",
       "      <td>0.584586</td>\n",
       "      <td>0.669737</td>\n",
       "      <td>0.052204</td>\n",
       "      <td>0.345572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>0.457031</td>\n",
       "      <td>0.630286</td>\n",
       "      <td>0.528483</td>\n",
       "      <td>0.480012</td>\n",
       "      <td>0.408453</td>\n",
       "      <td>0.527337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373077</td>\n",
       "      <td>0.327751</td>\n",
       "      <td>-0.470686</td>\n",
       "      <td>0.471169</td>\n",
       "      <td>0.457980</td>\n",
       "      <td>0.235927</td>\n",
       "      <td>0.479649</td>\n",
       "      <td>0.254085</td>\n",
       "      <td>0.125826</td>\n",
       "      <td>0.402865</td>\n",
       "      <td>0.356125</td>\n",
       "      <td>0.081566</td>\n",
       "      <td>0.158386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X1stFlrSF</th>\n",
       "      <td>0.453791</td>\n",
       "      <td>0.562538</td>\n",
       "      <td>0.479152</td>\n",
       "      <td>0.440452</td>\n",
       "      <td>0.491979</td>\n",
       "      <td>0.391782</td>\n",
       "      <td>0.373077</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801638</td>\n",
       "      <td>-0.310938</td>\n",
       "      <td>0.310814</td>\n",
       "      <td>0.242245</td>\n",
       "      <td>0.407545</td>\n",
       "      <td>0.252963</td>\n",
       "      <td>0.392367</td>\n",
       "      <td>0.332460</td>\n",
       "      <td>-0.249823</td>\n",
       "      <td>0.108418</td>\n",
       "      <td>0.458091</td>\n",
       "      <td>-0.104141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>0.448304</td>\n",
       "      <td>0.445223</td>\n",
       "      <td>0.549157</td>\n",
       "      <td>0.437900</td>\n",
       "      <td>0.486021</td>\n",
       "      <td>0.282082</td>\n",
       "      <td>0.327751</td>\n",
       "      <td>0.801638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.408387</td>\n",
       "      <td>0.408505</td>\n",
       "      <td>0.298048</td>\n",
       "      <td>0.332948</td>\n",
       "      <td>0.335717</td>\n",
       "      <td>0.393662</td>\n",
       "      <td>0.254150</td>\n",
       "      <td>-0.205605</td>\n",
       "      <td>0.053433</td>\n",
       "      <td>0.536471</td>\n",
       "      <td>-0.055676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.403951</td>\n",
       "      <td>-0.243475</td>\n",
       "      <td>-0.597523</td>\n",
       "      <td>-0.537866</td>\n",
       "      <td>-0.480382</td>\n",
       "      <td>-0.115514</td>\n",
       "      <td>-0.470686</td>\n",
       "      <td>-0.310938</td>\n",
       "      <td>-0.408387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.999060</td>\n",
       "      <td>-0.609892</td>\n",
       "      <td>-0.170721</td>\n",
       "      <td>-0.787278</td>\n",
       "      <td>-0.307831</td>\n",
       "      <td>-0.025143</td>\n",
       "      <td>-0.018395</td>\n",
       "      <td>0.052136</td>\n",
       "      <td>-0.278202</td>\n",
       "      <td>-0.269276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.402435</td>\n",
       "      <td>0.242666</td>\n",
       "      <td>0.597554</td>\n",
       "      <td>0.537663</td>\n",
       "      <td>0.480507</td>\n",
       "      <td>0.114280</td>\n",
       "      <td>0.471169</td>\n",
       "      <td>0.310814</td>\n",
       "      <td>0.408505</td>\n",
       "      <td>-0.999060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.612235</td>\n",
       "      <td>0.170680</td>\n",
       "      <td>0.788253</td>\n",
       "      <td>0.307520</td>\n",
       "      <td>0.024128</td>\n",
       "      <td>0.017588</td>\n",
       "      <td>-0.053101</td>\n",
       "      <td>0.279592</td>\n",
       "      <td>0.269743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>0.382440</td>\n",
       "      <td>0.316972</td>\n",
       "      <td>0.571532</td>\n",
       "      <td>0.426053</td>\n",
       "      <td>0.376741</td>\n",
       "      <td>0.198250</td>\n",
       "      <td>0.457980</td>\n",
       "      <td>0.242245</td>\n",
       "      <td>0.298048</td>\n",
       "      <td>-0.609892</td>\n",
       "      <td>0.612235</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.134157</td>\n",
       "      <td>0.626341</td>\n",
       "      <td>0.191668</td>\n",
       "      <td>0.021612</td>\n",
       "      <td>0.158985</td>\n",
       "      <td>-0.021912</td>\n",
       "      <td>0.152146</td>\n",
       "      <td>0.211430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>0.364122</td>\n",
       "      <td>0.459386</td>\n",
       "      <td>0.390753</td>\n",
       "      <td>0.321533</td>\n",
       "      <td>0.294713</td>\n",
       "      <td>0.311260</td>\n",
       "      <td>0.235927</td>\n",
       "      <td>0.407545</td>\n",
       "      <td>0.332948</td>\n",
       "      <td>-0.170721</td>\n",
       "      <td>0.170680</td>\n",
       "      <td>0.134157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085954</td>\n",
       "      <td>0.273129</td>\n",
       "      <td>0.261185</td>\n",
       "      <td>0.169991</td>\n",
       "      <td>0.086793</td>\n",
       "      <td>0.293095</td>\n",
       "      <td>0.205876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>0.328523</td>\n",
       "      <td>0.263064</td>\n",
       "      <td>0.542218</td>\n",
       "      <td>0.484947</td>\n",
       "      <td>0.469271</td>\n",
       "      <td>0.154603</td>\n",
       "      <td>0.479649</td>\n",
       "      <td>0.252963</td>\n",
       "      <td>0.335717</td>\n",
       "      <td>-0.787278</td>\n",
       "      <td>0.788253</td>\n",
       "      <td>0.626341</td>\n",
       "      <td>0.085954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.240459</td>\n",
       "      <td>-0.009165</td>\n",
       "      <td>0.083850</td>\n",
       "      <td>-0.042312</td>\n",
       "      <td>0.189044</td>\n",
       "      <td>0.218749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.316480</td>\n",
       "      <td>0.400088</td>\n",
       "      <td>0.426528</td>\n",
       "      <td>0.357659</td>\n",
       "      <td>0.370945</td>\n",
       "      <td>0.277103</td>\n",
       "      <td>0.254085</td>\n",
       "      <td>0.392367</td>\n",
       "      <td>0.393662</td>\n",
       "      <td>-0.307831</td>\n",
       "      <td>0.307520</td>\n",
       "      <td>0.191668</td>\n",
       "      <td>0.273129</td>\n",
       "      <td>0.240459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124728</td>\n",
       "      <td>0.119377</td>\n",
       "      <td>0.078213</td>\n",
       "      <td>0.301427</td>\n",
       "      <td>0.187685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>0.285116</td>\n",
       "      <td>0.284519</td>\n",
       "      <td>0.100541</td>\n",
       "      <td>0.180415</td>\n",
       "      <td>0.213249</td>\n",
       "      <td>0.213802</td>\n",
       "      <td>0.125826</td>\n",
       "      <td>0.332460</td>\n",
       "      <td>0.254150</td>\n",
       "      <td>-0.025143</td>\n",
       "      <td>0.024128</td>\n",
       "      <td>0.021612</td>\n",
       "      <td>0.261185</td>\n",
       "      <td>-0.009165</td>\n",
       "      <td>0.124728</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031515</td>\n",
       "      <td>0.132801</td>\n",
       "      <td>0.194050</td>\n",
       "      <td>0.034244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2ndFlrSF</th>\n",
       "      <td>0.270448</td>\n",
       "      <td>0.655085</td>\n",
       "      <td>0.245596</td>\n",
       "      <td>0.182532</td>\n",
       "      <td>0.128548</td>\n",
       "      <td>0.584586</td>\n",
       "      <td>0.402865</td>\n",
       "      <td>-0.249823</td>\n",
       "      <td>-0.205605</td>\n",
       "      <td>-0.018395</td>\n",
       "      <td>0.017588</td>\n",
       "      <td>0.158985</td>\n",
       "      <td>0.169991</td>\n",
       "      <td>0.083850</td>\n",
       "      <td>0.119377</td>\n",
       "      <td>0.031515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503506</td>\n",
       "      <td>-0.162240</td>\n",
       "      <td>0.611362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>0.265515</td>\n",
       "      <td>0.517058</td>\n",
       "      <td>0.073075</td>\n",
       "      <td>0.092892</td>\n",
       "      <td>0.073914</td>\n",
       "      <td>0.669737</td>\n",
       "      <td>0.356125</td>\n",
       "      <td>0.108418</td>\n",
       "      <td>0.053433</td>\n",
       "      <td>0.052136</td>\n",
       "      <td>-0.053101</td>\n",
       "      <td>-0.021912</td>\n",
       "      <td>0.086793</td>\n",
       "      <td>-0.042312</td>\n",
       "      <td>0.078213</td>\n",
       "      <td>0.132801</td>\n",
       "      <td>0.503506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.113468</td>\n",
       "      <td>0.245318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>0.259284</td>\n",
       "      <td>0.211682</td>\n",
       "      <td>0.281784</td>\n",
       "      <td>0.255510</td>\n",
       "      <td>0.310454</td>\n",
       "      <td>0.052204</td>\n",
       "      <td>0.081566</td>\n",
       "      <td>0.458091</td>\n",
       "      <td>0.536471</td>\n",
       "      <td>-0.278202</td>\n",
       "      <td>0.279592</td>\n",
       "      <td>0.152146</td>\n",
       "      <td>0.293095</td>\n",
       "      <td>0.189044</td>\n",
       "      <td>0.301427</td>\n",
       "      <td>0.194050</td>\n",
       "      <td>-0.162240</td>\n",
       "      <td>-0.113468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>0.248845</td>\n",
       "      <td>0.433548</td>\n",
       "      <td>0.272668</td>\n",
       "      <td>0.234435</td>\n",
       "      <td>0.178966</td>\n",
       "      <td>0.345572</td>\n",
       "      <td>0.158386</td>\n",
       "      <td>-0.104141</td>\n",
       "      <td>-0.055676</td>\n",
       "      <td>-0.269276</td>\n",
       "      <td>0.269743</td>\n",
       "      <td>0.211430</td>\n",
       "      <td>0.205876</td>\n",
       "      <td>0.218749</td>\n",
       "      <td>0.187685</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>0.611362</td>\n",
       "      <td>0.245318</td>\n",
       "      <td>-0.007269</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              logSales  GrLivArea  OverallQual  GarageCars  GarageArea  \\\n",
       "logSales      1.000000   0.578486     0.563275    0.494292    0.481480   \n",
       "GrLivArea     0.578486   1.000000     0.575126    0.489958    0.485441   \n",
       "OverallQual   0.563275   0.575126     1.000000    0.600591    0.565056   \n",
       "GarageCars    0.494292   0.489958     0.600591    1.000000    0.889689   \n",
       "GarageArea    0.481480   0.485441     0.565056    0.889689    1.000000   \n",
       "TotRmsAbvGrd  0.468042   0.808354     0.389761    0.358012    0.328679   \n",
       "FullBath      0.457031   0.630286     0.528483    0.480012    0.408453   \n",
       "X1stFlrSF     0.453791   0.562538     0.479152    0.440452    0.491979   \n",
       "TotalBsmtSF   0.448304   0.445223     0.549157    0.437900    0.486021   \n",
       "age          -0.403951  -0.243475    -0.597523   -0.537866   -0.480382   \n",
       "YearBuilt     0.402435   0.242666     0.597554    0.537663    0.480507   \n",
       "YearRemodAdd  0.382440   0.316972     0.571532    0.426053    0.376741   \n",
       "Fireplaces    0.364122   0.459386     0.390753    0.321533    0.294713   \n",
       "GarageYrBlt   0.328523   0.263064     0.542218    0.484947    0.469271   \n",
       "MasVnrArea    0.316480   0.400088     0.426528    0.357659    0.370945   \n",
       "LotArea       0.285116   0.284519     0.100541    0.180415    0.213249   \n",
       "X2ndFlrSF     0.270448   0.655085     0.245596    0.182532    0.128548   \n",
       "BedroomAbvGr  0.265515   0.517058     0.073075    0.092892    0.073914   \n",
       "BsmtFinSF1    0.259284   0.211682     0.281784    0.255510    0.310454   \n",
       "HalfBath      0.248845   0.433548     0.272668    0.234435    0.178966   \n",
       "\n",
       "              TotRmsAbvGrd  FullBath  X1stFlrSF  TotalBsmtSF       age  \\\n",
       "logSales          0.468042  0.457031   0.453791     0.448304 -0.403951   \n",
       "GrLivArea         0.808354  0.630286   0.562538     0.445223 -0.243475   \n",
       "OverallQual       0.389761  0.528483   0.479152     0.549157 -0.597523   \n",
       "GarageCars        0.358012  0.480012   0.440452     0.437900 -0.537866   \n",
       "GarageArea        0.328679  0.408453   0.491979     0.486021 -0.480382   \n",
       "TotRmsAbvGrd      1.000000  0.527337   0.391782     0.282082 -0.115514   \n",
       "FullBath          0.527337  1.000000   0.373077     0.327751 -0.470686   \n",
       "X1stFlrSF         0.391782  0.373077   1.000000     0.801638 -0.310938   \n",
       "TotalBsmtSF       0.282082  0.327751   0.801638     1.000000 -0.408387   \n",
       "age              -0.115514 -0.470686  -0.310938    -0.408387  1.000000   \n",
       "YearBuilt         0.114280  0.471169   0.310814     0.408505 -0.999060   \n",
       "YearRemodAdd      0.198250  0.457980   0.242245     0.298048 -0.609892   \n",
       "Fireplaces        0.311260  0.235927   0.407545     0.332948 -0.170721   \n",
       "GarageYrBlt       0.154603  0.479649   0.252963     0.335717 -0.787278   \n",
       "MasVnrArea        0.277103  0.254085   0.392367     0.393662 -0.307831   \n",
       "LotArea           0.213802  0.125826   0.332460     0.254150 -0.025143   \n",
       "X2ndFlrSF         0.584586  0.402865  -0.249823    -0.205605 -0.018395   \n",
       "BedroomAbvGr      0.669737  0.356125   0.108418     0.053433  0.052136   \n",
       "BsmtFinSF1        0.052204  0.081566   0.458091     0.536471 -0.278202   \n",
       "HalfBath          0.345572  0.158386  -0.104141    -0.055676 -0.269276   \n",
       "\n",
       "              YearBuilt  YearRemodAdd  Fireplaces  GarageYrBlt  MasVnrArea  \\\n",
       "logSales       0.402435      0.382440    0.364122     0.328523    0.316480   \n",
       "GrLivArea      0.242666      0.316972    0.459386     0.263064    0.400088   \n",
       "OverallQual    0.597554      0.571532    0.390753     0.542218    0.426528   \n",
       "GarageCars     0.537663      0.426053    0.321533     0.484947    0.357659   \n",
       "GarageArea     0.480507      0.376741    0.294713     0.469271    0.370945   \n",
       "TotRmsAbvGrd   0.114280      0.198250    0.311260     0.154603    0.277103   \n",
       "FullBath       0.471169      0.457980    0.235927     0.479649    0.254085   \n",
       "X1stFlrSF      0.310814      0.242245    0.407545     0.252963    0.392367   \n",
       "TotalBsmtSF    0.408505      0.298048    0.332948     0.335717    0.393662   \n",
       "age           -0.999060     -0.609892   -0.170721    -0.787278   -0.307831   \n",
       "YearBuilt      1.000000      0.612235    0.170680     0.788253    0.307520   \n",
       "YearRemodAdd   0.612235      1.000000    0.134157     0.626341    0.191668   \n",
       "Fireplaces     0.170680      0.134157    1.000000     0.085954    0.273129   \n",
       "GarageYrBlt    0.788253      0.626341    0.085954     1.000000    0.240459   \n",
       "MasVnrArea     0.307520      0.191668    0.273129     0.240459    1.000000   \n",
       "LotArea        0.024128      0.021612    0.261185    -0.009165    0.124728   \n",
       "X2ndFlrSF      0.017588      0.158985    0.169991     0.083850    0.119377   \n",
       "BedroomAbvGr  -0.053101     -0.021912    0.086793    -0.042312    0.078213   \n",
       "BsmtFinSF1     0.279592      0.152146    0.293095     0.189044    0.301427   \n",
       "HalfBath       0.269743      0.211430    0.205876     0.218749    0.187685   \n",
       "\n",
       "               LotArea  X2ndFlrSF  BedroomAbvGr  BsmtFinSF1  HalfBath  \n",
       "logSales      0.285116   0.270448      0.265515    0.259284  0.248845  \n",
       "GrLivArea     0.284519   0.655085      0.517058    0.211682  0.433548  \n",
       "OverallQual   0.100541   0.245596      0.073075    0.281784  0.272668  \n",
       "GarageCars    0.180415   0.182532      0.092892    0.255510  0.234435  \n",
       "GarageArea    0.213249   0.128548      0.073914    0.310454  0.178966  \n",
       "TotRmsAbvGrd  0.213802   0.584586      0.669737    0.052204  0.345572  \n",
       "FullBath      0.125826   0.402865      0.356125    0.081566  0.158386  \n",
       "X1stFlrSF     0.332460  -0.249823      0.108418    0.458091 -0.104141  \n",
       "TotalBsmtSF   0.254150  -0.205605      0.053433    0.536471 -0.055676  \n",
       "age          -0.025143  -0.018395      0.052136   -0.278202 -0.269276  \n",
       "YearBuilt     0.024128   0.017588     -0.053101    0.279592  0.269743  \n",
       "YearRemodAdd  0.021612   0.158985     -0.021912    0.152146  0.211430  \n",
       "Fireplaces    0.261185   0.169991      0.086793    0.293095  0.205876  \n",
       "GarageYrBlt  -0.009165   0.083850     -0.042312    0.189044  0.218749  \n",
       "MasVnrArea    0.124728   0.119377      0.078213    0.301427  0.187685  \n",
       "LotArea       1.000000   0.031515      0.132801    0.194050  0.034244  \n",
       "X2ndFlrSF     0.031515   1.000000      0.503506   -0.162240  0.611362  \n",
       "BedroomAbvGr  0.132801   0.503506      1.000000   -0.113468  0.245318  \n",
       "BsmtFinSF1    0.194050  -0.162240     -0.113468    1.000000 -0.007269  \n",
       "HalfBath      0.034244   0.611362      0.245318   -0.007269  1.000000  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Variable Correlation')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(data.corr(),cmap = 'coolwarm')\n",
    "plt.title('Variable Correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to get sorted values for the correlation between the features \n",
    "def get_redundant_pairs(df):\n",
    "    '''Get diagonal and lower triangular pairs of correlation matrix'''\n",
    "    pairs_to_drop = set()\n",
    "    cols = df.columns\n",
    "    for i in range(0, df.shape[1]):\n",
    "        for j in range(0, i+1):\n",
    "            pairs_to_drop.add((cols[i], cols[j]))\n",
    "    return pairs_to_drop\n",
    "\n",
    "def get_top_abs_correlations(df):\n",
    "    au_corr = df.corr().abs().stack()\n",
    "    labels_to_drop = get_redundant_pairs(df.corr())\n",
    "    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)\n",
    "    return au_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           YearBuilt       0.999060\n",
       "GarageCars    GarageArea      0.889689\n",
       "GrLivArea     TotRmsAbvGrd    0.808354\n",
       "X1stFlrSF     TotalBsmtSF     0.801638\n",
       "YearBuilt     GarageYrBlt     0.788253\n",
       "age           GarageYrBlt     0.787278\n",
       "TotRmsAbvGrd  BedroomAbvGr    0.669737\n",
       "GrLivArea     X2ndFlrSF       0.655085\n",
       "              FullBath        0.630286\n",
       "YearRemodAdd  GarageYrBlt     0.626341\n",
       "YearBuilt     YearRemodAdd    0.612235\n",
       "X2ndFlrSF     HalfBath        0.611362\n",
       "age           YearRemodAdd    0.609892\n",
       "OverallQual   GarageCars      0.600591\n",
       "              YearBuilt       0.597554\n",
       "              age             0.597523\n",
       "TotRmsAbvGrd  X2ndFlrSF       0.584586\n",
       "logSales      GrLivArea       0.578486\n",
       "GrLivArea     OverallQual     0.575126\n",
       "OverallQual   YearRemodAdd    0.571532\n",
       "              GarageArea      0.565056\n",
       "logSales      OverallQual     0.563275\n",
       "GrLivArea     X1stFlrSF       0.562538\n",
       "OverallQual   TotalBsmtSF     0.549157\n",
       "              GarageYrBlt     0.542218\n",
       "GarageCars    age             0.537866\n",
       "              YearBuilt       0.537663\n",
       "TotalBsmtSF   BsmtFinSF1      0.536471\n",
       "OverallQual   FullBath        0.528483\n",
       "TotRmsAbvGrd  FullBath        0.527337\n",
       "                                ...   \n",
       "              age             0.115514\n",
       "              YearBuilt       0.114280\n",
       "BedroomAbvGr  BsmtFinSF1      0.113468\n",
       "X1stFlrSF     BedroomAbvGr    0.108418\n",
       "              HalfBath        0.104141\n",
       "OverallQual   LotArea         0.100541\n",
       "GarageCars    BedroomAbvGr    0.092892\n",
       "Fireplaces    BedroomAbvGr    0.086793\n",
       "              GarageYrBlt     0.085954\n",
       "GarageYrBlt   X2ndFlrSF       0.083850\n",
       "FullBath      BsmtFinSF1      0.081566\n",
       "MasVnrArea    BedroomAbvGr    0.078213\n",
       "GarageArea    BedroomAbvGr    0.073914\n",
       "OverallQual   BedroomAbvGr    0.073075\n",
       "TotalBsmtSF   HalfBath        0.055676\n",
       "              BedroomAbvGr    0.053433\n",
       "YearBuilt     BedroomAbvGr    0.053101\n",
       "TotRmsAbvGrd  BsmtFinSF1      0.052204\n",
       "age           BedroomAbvGr    0.052136\n",
       "GarageYrBlt   BedroomAbvGr    0.042312\n",
       "LotArea       HalfBath        0.034244\n",
       "              X2ndFlrSF       0.031515\n",
       "age           LotArea         0.025143\n",
       "YearBuilt     LotArea         0.024128\n",
       "YearRemodAdd  BedroomAbvGr    0.021912\n",
       "              LotArea         0.021612\n",
       "age           X2ndFlrSF       0.018395\n",
       "YearBuilt     X2ndFlrSF       0.017588\n",
       "GarageYrBlt   LotArea         0.009165\n",
       "BsmtFinSF1    HalfBath        0.007269\n",
       "Length: 190, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get sorted correlation between all features\n",
    "get_top_abs_correlations(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping 5 variables that are highly correlated with each other\n",
    "drop_list = ['YearBuilt','GarageCars', 'TotRmsAbvGrd', 'GarageYrBlt', 'TotalBsmtSF']\n",
    "data.drop(drop_list, axis =1)\n",
    "#adding an addition column for calculating the theta values\n",
    "data.insert(0,'Intercept', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Intercept</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>X1stFlrSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>age</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>X2ndFlrSF</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>HalfBath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1710</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>856</td>\n",
       "      <td>856.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>854</td>\n",
       "      <td>3</td>\n",
       "      <td>706.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1262</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1786</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>920</td>\n",
       "      <td>920.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>866</td>\n",
       "      <td>3</td>\n",
       "      <td>486.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1717</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>961</td>\n",
       "      <td>756.0</td>\n",
       "      <td>91</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>756</td>\n",
       "      <td>3</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2198</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>1053</td>\n",
       "      <td>4</td>\n",
       "      <td>655.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1362</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>796</td>\n",
       "      <td>796.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1993</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14115</td>\n",
       "      <td>566</td>\n",
       "      <td>1</td>\n",
       "      <td>732.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1694</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1694</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>10084</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2090</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1107</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1973</td>\n",
       "      <td>1973</td>\n",
       "      <td>2</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>10382</td>\n",
       "      <td>983</td>\n",
       "      <td>3</td>\n",
       "      <td>859.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1774</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1022</td>\n",
       "      <td>952.0</td>\n",
       "      <td>77</td>\n",
       "      <td>1931</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>752</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1077</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1077</td>\n",
       "      <td>991.0</td>\n",
       "      <td>69</td>\n",
       "      <td>1939</td>\n",
       "      <td>1950</td>\n",
       "      <td>2</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7420</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>851.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1040</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1040</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>43</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11200</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>906.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2324</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1182</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>11924</td>\n",
       "      <td>1142</td>\n",
       "      <td>4</td>\n",
       "      <td>998.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>912</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>912</td>\n",
       "      <td>912.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1962</td>\n",
       "      <td>1962</td>\n",
       "      <td>0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12968</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>1494</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1494</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>10652</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1253</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253.0</td>\n",
       "      <td>48</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>10920</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>733.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>854</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>854</td>\n",
       "      <td>832.0</td>\n",
       "      <td>78</td>\n",
       "      <td>1929</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>1004</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1004</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>11241</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>578.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1296</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1967</td>\n",
       "      <td>1967</td>\n",
       "      <td>0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10791</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1114</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1114</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13695</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>1339</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1339</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1958</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7560</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>504.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>2376</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1158</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>14215</td>\n",
       "      <td>1218</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1108</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1108</td>\n",
       "      <td>637.0</td>\n",
       "      <td>77</td>\n",
       "      <td>1930</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7449</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1795</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1795</td>\n",
       "      <td>1777.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2002</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>9742</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1060</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1060</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4224</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>840.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1060</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1060</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>42</td>\n",
       "      <td>1968</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8246</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1600</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1600</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>14230</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "      <td>900.0</td>\n",
       "      <td>59</td>\n",
       "      <td>1951</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7200</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1704</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1704</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2007</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>11478</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1600</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1600</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>49</td>\n",
       "      <td>1957</td>\n",
       "      <td>1997</td>\n",
       "      <td>2</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16321</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>520</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>520</td>\n",
       "      <td>520.0</td>\n",
       "      <td>81</td>\n",
       "      <td>1927</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6324</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2889</th>\n",
       "      <td>1</td>\n",
       "      <td>641</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>641</td>\n",
       "      <td>641.0</td>\n",
       "      <td>81</td>\n",
       "      <td>1925</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7030</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2890</th>\n",
       "      <td>1</td>\n",
       "      <td>1638</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>967</td>\n",
       "      <td>967.0</td>\n",
       "      <td>49</td>\n",
       "      <td>1957</td>\n",
       "      <td>1957</td>\n",
       "      <td>0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>9060</td>\n",
       "      <td>671</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2891</th>\n",
       "      <td>1</td>\n",
       "      <td>729</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61</td>\n",
       "      <td>1945</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12366</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>1</td>\n",
       "      <td>1396</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1060</td>\n",
       "      <td>660.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9000</td>\n",
       "      <td>336</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2893</th>\n",
       "      <td>1</td>\n",
       "      <td>936</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>576</td>\n",
       "      <td>216.0</td>\n",
       "      <td>90</td>\n",
       "      <td>1916</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8520</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2894</th>\n",
       "      <td>1</td>\n",
       "      <td>1778</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1778</td>\n",
       "      <td>1573.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>5748</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1573.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>1</td>\n",
       "      <td>1646</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1646</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3842</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2896</th>\n",
       "      <td>1</td>\n",
       "      <td>1625</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1625</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1979</td>\n",
       "      <td>1979</td>\n",
       "      <td>1</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23580</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>776.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2897</th>\n",
       "      <td>1</td>\n",
       "      <td>1664</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1664</td>\n",
       "      <td>1664.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1978</td>\n",
       "      <td>1978</td>\n",
       "      <td>0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8385</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2898</th>\n",
       "      <td>1</td>\n",
       "      <td>1491</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1491</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9116</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2899</th>\n",
       "      <td>1</td>\n",
       "      <td>1210</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1210</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1975</td>\n",
       "      <td>1975</td>\n",
       "      <td>0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>11080</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>576.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2900</th>\n",
       "      <td>1</td>\n",
       "      <td>1650</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1650</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>48</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>2</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50102</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>909.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>1</td>\n",
       "      <td>1403</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1403</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8098</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>1</td>\n",
       "      <td>1960</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1960</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>13618</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2903</th>\n",
       "      <td>1</td>\n",
       "      <td>1838</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1838</td>\n",
       "      <td>1838.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>11577</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2904</th>\n",
       "      <td>1</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1951</td>\n",
       "      <td>1951</td>\n",
       "      <td>0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2905</th>\n",
       "      <td>1</td>\n",
       "      <td>1368</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1368</td>\n",
       "      <td>1288.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1997</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>7020</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>1</td>\n",
       "      <td>1304</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>616</td>\n",
       "      <td>264.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1977</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665</td>\n",
       "      <td>688</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>1</td>\n",
       "      <td>874</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>874</td>\n",
       "      <td>864.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1968</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10172</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>441.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>1</td>\n",
       "      <td>1652</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1652</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11836</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>1</td>\n",
       "      <td>630</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>630</td>\n",
       "      <td>630.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1470</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>522.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>1</td>\n",
       "      <td>1092</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "      <td>546.0</td>\n",
       "      <td>34</td>\n",
       "      <td>1972</td>\n",
       "      <td>1972</td>\n",
       "      <td>0</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1484</td>\n",
       "      <td>546</td>\n",
       "      <td>3</td>\n",
       "      <td>252.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>1</td>\n",
       "      <td>1360</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1360</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>37</td>\n",
       "      <td>1969</td>\n",
       "      <td>1979</td>\n",
       "      <td>1</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>13384</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>119.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>1</td>\n",
       "      <td>1092</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "      <td>546.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1533</td>\n",
       "      <td>546</td>\n",
       "      <td>3</td>\n",
       "      <td>408.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>1</td>\n",
       "      <td>1092</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "      <td>546.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1526</td>\n",
       "      <td>546</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>1</td>\n",
       "      <td>1092</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "      <td>546.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>546</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>1</td>\n",
       "      <td>1092</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "      <td>546.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>546</td>\n",
       "      <td>3</td>\n",
       "      <td>252.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>1</td>\n",
       "      <td>1224</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1224</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1960</td>\n",
       "      <td>1996</td>\n",
       "      <td>1</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>1</td>\n",
       "      <td>970</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>970</td>\n",
       "      <td>912.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>996</td>\n",
       "      <td>996.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1993</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>1004</td>\n",
       "      <td>3</td>\n",
       "      <td>758.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Intercept  GrLivArea  OverallQual  GarageCars  GarageArea  TotRmsAbvGrd  \\\n",
       "0             1       1710            7         2.0       548.0             8   \n",
       "1             1       1262            6         2.0       460.0             6   \n",
       "2             1       1786            7         2.0       608.0             6   \n",
       "3             1       1717            7         3.0       642.0             7   \n",
       "4             1       2198            8         3.0       836.0             9   \n",
       "5             1       1362            5         2.0       480.0             5   \n",
       "6             1       1694            8         2.0       636.0             7   \n",
       "7             1       2090            7         2.0       484.0             7   \n",
       "8             1       1774            7         2.0       468.0             8   \n",
       "9             1       1077            5         1.0       205.0             5   \n",
       "10            1       1040            5         1.0       384.0             5   \n",
       "11            1       2324            9         3.0       736.0            11   \n",
       "12            1        912            5         1.0       352.0             4   \n",
       "13            1       1494            7         3.0       840.0             7   \n",
       "14            1       1253            6         1.0       352.0             5   \n",
       "15            1        854            7         2.0       576.0             5   \n",
       "16            1       1004            6         2.0       480.0             5   \n",
       "17            1       1296            4         2.0       516.0             6   \n",
       "18            1       1114            5         2.0       576.0             6   \n",
       "19            1       1339            5         1.0       294.0             6   \n",
       "20            1       2376            8         3.0       853.0             9   \n",
       "21            1       1108            7         1.0       280.0             6   \n",
       "22            1       1795            8         2.0       534.0             7   \n",
       "23            1       1060            5         2.0       572.0             6   \n",
       "24            1       1060            5         1.0       270.0             6   \n",
       "25            1       1600            8         3.0       890.0             7   \n",
       "26            1        900            5         2.0       576.0             5   \n",
       "27            1       1704            8         3.0       772.0             7   \n",
       "28            1       1600            5         1.0       319.0             6   \n",
       "29            1        520            4         1.0       240.0             4   \n",
       "...         ...        ...          ...         ...         ...           ...   \n",
       "2889          1        641            4         1.0       272.0             4   \n",
       "2890          1       1638            6         1.0       384.0             6   \n",
       "2891          1        729            3         0.0         0.0             5   \n",
       "2892          1       1396            5         0.0         0.0             8   \n",
       "2893          1        936            3         0.0         0.0             6   \n",
       "2894          1       1778            8         2.0       495.0             5   \n",
       "2895          1       1646            8         2.0       525.0             5   \n",
       "2896          1       1625            6         2.0       576.0             6   \n",
       "2897          1       1664            6         2.0       616.0            10   \n",
       "2898          1       1491            8         2.0       490.0             7   \n",
       "2899          1       1210            6         2.0       528.0             6   \n",
       "2900          1       1650            6         2.0       518.0             6   \n",
       "2901          1       1403            6         2.0       470.0             5   \n",
       "2902          1       1960            8         3.0       714.0             8   \n",
       "2903          1       1838            9         3.0       682.0             9   \n",
       "2904          1       1600            1         1.0       270.0             6   \n",
       "2905          1       1368            7         4.0       784.0             8   \n",
       "2906          1       1304            5         1.0       336.0             5   \n",
       "2907          1        874            5         1.0       288.0             5   \n",
       "2908          1       1652            5         3.0       928.0             8   \n",
       "2909          1        630            4         0.0         0.0             3   \n",
       "2910          1       1092            4         1.0       253.0             5   \n",
       "2911          1       1360            5         1.0       336.0             8   \n",
       "2912          1       1092            4         1.0       286.0             5   \n",
       "2913          1       1092            4         0.0         0.0             5   \n",
       "2914          1       1092            4         0.0         0.0             5   \n",
       "2915          1       1092            4         1.0       286.0             6   \n",
       "2916          1       1224            5         2.0       576.0             7   \n",
       "2917          1        970            5         0.0         0.0             6   \n",
       "2918          1       2000            7         3.0       650.0             9   \n",
       "\n",
       "      FullBath  X1stFlrSF  TotalBsmtSF  age  YearBuilt  YearRemodAdd  \\\n",
       "0            2        856        856.0    5       2003          2003   \n",
       "1            2       1262       1262.0   31       1976          1976   \n",
       "2            2        920        920.0    7       2001          2002   \n",
       "3            1        961        756.0   91       1915          1970   \n",
       "4            2       1145       1145.0    8       2000          2000   \n",
       "5            1        796        796.0   16       1993          1995   \n",
       "6            2       1694       1686.0    3       2004          2005   \n",
       "7            2       1107       1107.0   36       1973          1973   \n",
       "8            2       1022        952.0   77       1931          1950   \n",
       "9            1       1077        991.0   69       1939          1950   \n",
       "10           1       1040       1040.0   43       1965          1965   \n",
       "11           3       1182       1175.0    1       2005          2006   \n",
       "12           1        912        912.0   46       1962          1962   \n",
       "13           2       1494       1494.0    1       2006          2007   \n",
       "14           1       1253       1253.0   48       1960          1960   \n",
       "15           1        854        832.0   78       1929          2001   \n",
       "16           1       1004       1004.0   40       1970          1970   \n",
       "17           2       1296          0.0   39       1967          1967   \n",
       "18           1       1114       1114.0    4       2004          2004   \n",
       "19           1       1339       1029.0   51       1958          1965   \n",
       "20           3       1158       1158.0    1       2005          2006   \n",
       "21           1       1108        637.0   77       1930          1950   \n",
       "22           2       1795       1777.0    6       2002          2002   \n",
       "23           1       1060       1040.0   31       1976          1976   \n",
       "24           1       1060       1060.0   42       1968          2001   \n",
       "25           2       1600       1566.0    2       2007          2007   \n",
       "26           1        900        900.0   59       1951          2000   \n",
       "27           2       1704       1704.0    3       2007          2008   \n",
       "28           1       1600       1484.0   49       1957          1997   \n",
       "29           1        520        520.0   81       1927          1950   \n",
       "...        ...        ...          ...  ...        ...           ...   \n",
       "2889         1        641        641.0   81       1925          1950   \n",
       "2890         2        967        967.0   49       1957          1957   \n",
       "2891         1        729          0.0   61       1945          1950   \n",
       "2892         2       1060        660.0   55       1951          1951   \n",
       "2893         1        576        216.0   90       1916          1950   \n",
       "2894         2       1778       1573.0    1       2005          2006   \n",
       "2895         2       1646       1594.0    2       2004          2005   \n",
       "2896         2       1625       1625.0   27       1979          1979   \n",
       "2897         2       1664       1664.0   28       1978          1978   \n",
       "2898         2       1491       1491.0    5       2001          2001   \n",
       "2899         2       1210       1128.0   31       1975          1975   \n",
       "2900         1       1650       1632.0   48       1958          1958   \n",
       "2901         2       1403       1381.0    6       2000          2000   \n",
       "2902         2       1960       1728.0    1       2005          2006   \n",
       "2903         2       1838       1838.0    1       2005          2006   \n",
       "2904         1       1600          0.0   55       1951          1951   \n",
       "2905         2       1368       1288.0    9       1997          1997   \n",
       "2906         1        616        264.0   29       1977          1977   \n",
       "2907         1        874        864.0   38       1968          2003   \n",
       "2908         2       1652       1652.0   36       1970          1970   \n",
       "2909         1        630        630.0   36       1970          1970   \n",
       "2910         1        546        546.0   34       1972          1972   \n",
       "2911         1       1360       1104.0   37       1969          1979   \n",
       "2912         1        546        546.0   36       1970          1970   \n",
       "2913         1        546        546.0   36       1970          1970   \n",
       "2914         1        546        546.0   36       1970          1970   \n",
       "2915         1        546        546.0   36       1970          1970   \n",
       "2916         1       1224       1224.0   46       1960          1996   \n",
       "2917         1        970        912.0   14       1992          1992   \n",
       "2918         2        996        996.0   13       1993          1994   \n",
       "\n",
       "      Fireplaces  GarageYrBlt  MasVnrArea  LotArea  X2ndFlrSF  BedroomAbvGr  \\\n",
       "0              0       2003.0       196.0     8450        854             3   \n",
       "1              1       1976.0         0.0     9600          0             3   \n",
       "2              1       2001.0       162.0    11250        866             3   \n",
       "3              1       1998.0         0.0     9550        756             3   \n",
       "4              1       2000.0       350.0    14260       1053             4   \n",
       "5              0       1993.0         0.0    14115        566             1   \n",
       "6              1       2004.0       186.0    10084          0             3   \n",
       "7              2       1973.0       240.0    10382        983             3   \n",
       "8              2       1931.0         0.0     6120        752             2   \n",
       "9              2       1939.0         0.0     7420          0             2   \n",
       "10             0       1965.0         0.0    11200          0             3   \n",
       "11             2       2005.0       286.0    11924       1142             4   \n",
       "12             0       1962.0         0.0    12968          0             2   \n",
       "13             1       2006.0       306.0    10652          0             3   \n",
       "14             1       1960.0       212.0    10920          0             2   \n",
       "15             0       1991.0         0.0     6120          0             2   \n",
       "16             1       1970.0       180.0    11241          0             2   \n",
       "17             0       1967.0         0.0    10791          0             2   \n",
       "18             0       2004.0         0.0    13695          0             3   \n",
       "19             0       1958.0         0.0     7560          0             3   \n",
       "20             1       2005.0       380.0    14215       1218             4   \n",
       "21             1       1930.0         0.0     7449          0             3   \n",
       "22             1       2002.0       281.0     9742          0             3   \n",
       "23             1       1976.0         0.0     4224          0             3   \n",
       "24             1       1968.0         0.0     8246          0             3   \n",
       "25             1       2007.0       640.0    14230          0             3   \n",
       "26             0       2005.0         0.0     7200          0             3   \n",
       "27             1       2008.0       200.0    11478          0             3   \n",
       "28             2       1957.0         0.0    16321          0             2   \n",
       "29             0       1920.0         0.0     6324          0             1   \n",
       "...          ...          ...         ...      ...        ...           ...   \n",
       "2889           0       1925.0         0.0     7030          0             2   \n",
       "2890           0       1957.0       327.0     9060        671             4   \n",
       "2891           0       1979.0         0.0    12366          0             2   \n",
       "2892           0       1979.0         0.0     9000        336             4   \n",
       "2893           0       1979.0         0.0     8520        360             2   \n",
       "2894           1       2005.0       473.0     5748          0             2   \n",
       "2895           1       2004.0       186.0     3842          0             2   \n",
       "2896           1       1979.0         0.0    23580          0             3   \n",
       "2897           0       1978.0         0.0     8385          0             4   \n",
       "2898           0       2001.0         0.0     9116          0             3   \n",
       "2899           0       1975.0       257.0    11080          0             3   \n",
       "2900           2       1958.0         0.0    50102          0             2   \n",
       "2901           0       2000.0         0.0     8098          0             2   \n",
       "2902           2       2005.0       198.0    13618          0             3   \n",
       "2903           1       2005.0       382.0    11577          0             3   \n",
       "2904           0       1951.0         0.0    31250          0             3   \n",
       "2905           0       1997.0       200.0     7020          0             2   \n",
       "2906           1       1977.0         0.0     2665        688             3   \n",
       "2907           0       1968.0         0.0    10172          0             3   \n",
       "2908           0       1970.0         0.0    11836          0             4   \n",
       "2909           0       1979.0         0.0     1470          0             1   \n",
       "2910           0       1972.0         0.0     1484        546             3   \n",
       "2911           1       1969.0       194.0    13384          0             3   \n",
       "2912           0       1970.0         0.0     1533        546             3   \n",
       "2913           0       1979.0         0.0     1526        546             3   \n",
       "2914           0       1979.0         0.0     1936        546             3   \n",
       "2915           0       1970.0         0.0     1894        546             3   \n",
       "2916           1       1960.0         0.0    20000          0             4   \n",
       "2917           0       1979.0         0.0    10441          0             3   \n",
       "2918           1       1993.0        94.0     9627       1004             3   \n",
       "\n",
       "      BsmtFinSF1  HalfBath  \n",
       "0          706.0         1  \n",
       "1          978.0         0  \n",
       "2          486.0         1  \n",
       "3          216.0         0  \n",
       "4          655.0         1  \n",
       "5          732.0         1  \n",
       "6         1369.0         0  \n",
       "7          859.0         1  \n",
       "8            0.0         0  \n",
       "9          851.0         0  \n",
       "10         906.0         0  \n",
       "11         998.0         0  \n",
       "12         737.0         0  \n",
       "13           0.0         0  \n",
       "14         733.0         1  \n",
       "15           0.0         0  \n",
       "16         578.0         0  \n",
       "17           0.0         0  \n",
       "18         646.0         1  \n",
       "19         504.0         0  \n",
       "20           0.0         1  \n",
       "21           0.0         0  \n",
       "22           0.0         0  \n",
       "23         840.0         0  \n",
       "24         188.0         0  \n",
       "25           0.0         0  \n",
       "26         234.0         0  \n",
       "27        1218.0         0  \n",
       "28        1277.0         0  \n",
       "29           0.0         0  \n",
       "...          ...       ...  \n",
       "2889         0.0         0  \n",
       "2890         0.0         0  \n",
       "2891         0.0         0  \n",
       "2892         0.0         0  \n",
       "2893         0.0         0  \n",
       "2894      1573.0         0  \n",
       "2895      1564.0         0  \n",
       "2896       776.0         0  \n",
       "2897         0.0         0  \n",
       "2898         0.0         0  \n",
       "2899       576.0         0  \n",
       "2900       909.0         0  \n",
       "2901      1136.0         0  \n",
       "2902      1350.0         0  \n",
       "2903      1455.0         0  \n",
       "2904         0.0         1  \n",
       "2905      1243.0         0  \n",
       "2906         0.0         1  \n",
       "2907       441.0         0  \n",
       "2908       149.0         0  \n",
       "2909       522.0         0  \n",
       "2910       252.0         1  \n",
       "2911       119.0         0  \n",
       "2912       408.0         1  \n",
       "2913         0.0         1  \n",
       "2914         0.0         1  \n",
       "2915       252.0         1  \n",
       "2916      1224.0         0  \n",
       "2917       337.0         0  \n",
       "2918       758.0         1  \n",
       "\n",
       "[2919 rows x 20 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking out logSales from the features as the target variable\n",
    "target = data[['logSales']]\n",
    "data.drop('logSales', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data as test and train\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size= 0.33, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to get rmse values\n",
    "def get_rmse(data, target, cmat):\n",
    "    dlength = len(data)\n",
    "    loss = np.dot(data,cmat)- target\n",
    "    return np.sqrt(np.sum(np.power(loss,2))/dlength)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to get the cost\n",
    "def get_cost(data, target, cmat):\n",
    "    dlength = len(data)\n",
    "    loss = np.dot(data,cmat)- target\n",
    "    return np.sum(np.power(loss,2))/dlength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to perform the gradient descent\n",
    "def get_theta_matrix(alpha, iterations, data, target, cmat):\n",
    "    dlength = len(data)\n",
    "    for i in range (0, iterations):\n",
    "        loss = np.dot(data,cmat)- target\n",
    "        gradient = pd.DataFrame(np.dot(data.transpose(),loss)).divide(dlength)\n",
    "        cmat = cmat - (gradient.multiply(alpha))\n",
    "    return cmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to run the linear regression for varies learning rates and iteration.\n",
    "def run_linear_regress(data, target, cmat):\n",
    "    alpha_values = [0.75,0.5,0.25,0.1]\n",
    "    iteration = [500,1000,2500,5000, 7500, 10000]\n",
    "    theta_matrixes = {}\n",
    "    rmse_values = {}\n",
    "    cost_values = {}\n",
    "    for i in alpha_values:\n",
    "        for j in iteration:\n",
    "            theta_matrixes[str(i),str(j)] = get_theta_matrix(i,j,data,target,cmat)\n",
    "            rmse_values[str(i),str(j)] = get_rmse(data, target, theta_matrixes[str(i),str(j)])\n",
    "            cost_values[str(i),str(j)] = get_cost(data, target, theta_matrixes[str(i),str(j)])\n",
    "    return theta_matrixes,rmse_values,cost_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 1)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating an empty coefficient matrix\n",
    "carray = np.zeros(len(X_train.columns))\n",
    "cmat = pd.DataFrame(carray)\n",
    "cmat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X_Scalar_train = scaler.fit_transform(X_train)\n",
    "y_Scalar_train = scaler.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#run linear regression with the train set\n",
    "thetas,rmse_val,cost_val = run_linear_regress(X_Scalar_train,y_Scalar_train, cmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('0.75', '500'): 0.012633173639686726,\n",
       " ('0.75', '1000'): 0.003517130817094459,\n",
       " ('0.75', '2500'): 0.0005904082705960501,\n",
       " ('0.75', '5000'): 0.00010771148147463176,\n",
       " ('0.75', '7500'): 2.157034784936232e-05,\n",
       " ('0.75', '10000'): 4.583876606850443e-06,\n",
       " ('0.5', '500'): 0.020678738121557953,\n",
       " ('0.5', '1000'): 0.007933661963950261,\n",
       " ('0.5', '2500'): 0.0012531616025322954,\n",
       " ('0.5', '5000'): 0.0003235184297583739,\n",
       " ('0.5', '7500'): 0.00010774940370781859,\n",
       " ('0.5', '10000'): 3.681294104528197e-05,\n",
       " ('0.25', '500'): 0.034563174232062496,\n",
       " ('0.25', '1000'): 0.020689623691996197,\n",
       " ('0.25', '2500'): 0.005166320003727442,\n",
       " ('0.25', '5000'): 0.0012537355000891916,\n",
       " ('0.25', '7500'): 0.0005907504763992035,\n",
       " ('0.25', '10000'): 0.0003236080568059169,\n",
       " ('0.1', '500'): 0.047860714465372864,\n",
       " ('0.1', '1000'): 0.03842294720377577,\n",
       " ('0.1', '2500'): 0.020696151109467296,\n",
       " ('0.1', '5000'): 0.007944854806220737,\n",
       " ('0.1', '7500'): 0.003525611999766853,\n",
       " ('0.1', '10000'): 0.0019271872825478435}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print rmse values for the regression\n",
    "rmse_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.00015959707621047558,\n",
       " 1.2370209184555537e-05,\n",
       " 3.4858192598821874e-07,\n",
       " 1.1601763241459941e-08,\n",
       " 4.6527990634248964e-10,\n",
       " 2.1011924746830724e-11]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the cost function for alpha 0.75 for all iteration\n",
    "iteration = [500,1000,2500,5000, 7500, 10000]\n",
    "cost = []\n",
    "for i in cost_val.keys():\n",
    "    if(not ('0.75') in i):\n",
    "        break;\n",
    "    cost.append(cost_val.get(i))\n",
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting number of iteration vs cost for 0.75 alpha\n",
    "plt.plot(iteration, cost) \n",
    "plt.xlabel(\"Number of iterations\") \n",
    "plt.ylabel(\"Cost\") \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting rmse values vs learning rates\n",
    "rmse_plot = [2.1011924746830724e-11, 1.3551926284034056e-09, 1.0472217442970156e-07, 3.7140508220141415e-06]\n",
    "alpha_plot = [0.75, 0.5,0.25, 0.1]\n",
    "plt.plot(alpha_plot, rmse_plot) \n",
    "plt.xlabel(\"Alpha values\") \n",
    "plt.ylabel(\"RMSE values\") \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to predict logSales\n",
    "def predict(testData, coeffMatrix):\n",
    "    predictedTarget = np.dot(testData,coeffMatrix);\n",
    "    return predictedTarget;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [3.79929705e-01],\n",
       "       [5.61931575e-01],\n",
       "       [5.11685213e-01],\n",
       "       [4.15951946e-01],\n",
       "       [5.26539902e-01],\n",
       "       [5.34615286e-01],\n",
       "       [3.26716239e-01],\n",
       "       [4.92112328e-01],\n",
       "       [2.90357929e-01],\n",
       "       [6.52688017e-01],\n",
       "       [2.95784324e-01],\n",
       "       [4.19730685e-01],\n",
       "       [5.18551237e-01],\n",
       "       [6.50411366e-01],\n",
       "       [5.17621917e-01],\n",
       "       [5.33668242e-01],\n",
       "       [4.97496202e-01],\n",
       "       [5.19823683e-01],\n",
       "       [5.10129729e-01],\n",
       "       [6.48486777e-01],\n",
       "       [5.21027857e-01],\n",
       "       [4.99591923e-01],\n",
       "       [4.33539758e-01],\n",
       "       [5.50347685e-01],\n",
       "       [2.32723883e-01],\n",
       "       [5.15690616e-01],\n",
       "       [5.17609441e-01],\n",
       "       [5.13131112e-01],\n",
       "       [5.23184503e-01],\n",
       "       [4.83339286e-01],\n",
       "       [4.94447603e-01],\n",
       "       [3.55108683e-01],\n",
       "       [4.59500332e-01],\n",
       "       [5.32706785e-01],\n",
       "       [4.24702513e-01],\n",
       "       [5.49738041e-01],\n",
       "       [5.11750191e-01],\n",
       "       [7.31524295e-01],\n",
       "       [5.10506829e-01]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling test features and predicting logSales\n",
    "X_Scalar_test = scaler.fit_transform(X_test)\n",
    "y_Scalar_test = scaler.fit_transform(y_test)\n",
    "predict(X_Scalar_test, coeff75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get best theta values for all alpha values\n",
    "coeff75 = thetas.get(('0.75', '10000'))\n",
    "coeff5 = thetas.get(('0.5', '10000'))\n",
    "coeff25 = thetas.get(('0.25', '10000'))\n",
    "coeff1 = thetas.get(('0.1', '10000'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the rmse values for the test set\n",
    "test_rmse = []\n",
    "test_rmse.append(get_rmse(X_Scalar_test,y_Scalar_test, coeff75))\n",
    "test_rmse.append(get_rmse(X_Scalar_test,y_Scalar_test, coeff5))\n",
    "test_rmse.append(get_rmse(X_Scalar_test,y_Scalar_test, coeff25))\n",
    "test_rmse.append(get_rmse(X_Scalar_test,y_Scalar_test, coeff1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.2362917260283245e-05,\n",
       " 7.263401350774937e-05,\n",
       " 0.0007349729641189954,\n",
       " 0.007337450722066172]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting rmse value vs alpha for the test set\n",
    "alpha_plot = [0.75, 0.5,0.25, 0.1]\n",
    "plt.plot(alpha_plot, rmse_plot) \n",
    "plt.xlabel(\"Alpha values\") \n",
    "plt.ylabel(\"RMSE values\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting thetas for alpha 0.75 and different iterations\n",
    "coeff75_500 = thetas.get(('0.75', '500'))\n",
    "coeff75_1000 = thetas.get(('0.75', '1000'))\n",
    "coeff75_2500 = thetas.get(('0.75', '2500'))\n",
    "coeff75_5000 = thetas.get(('0.75', '5000'))\n",
    "coeff75_7500 = thetas.get(('0.75', '7500'))\n",
    "coeff75_10000 = thetas.get(('0.75', '10000'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = [500,1000,2500,5000, 7500, 10000]\n",
    "cost = []\n",
    "cost.append(get_cost(X_Scalar_test,y_Scalar_test, coeff75_500))\n",
    "cost.append(get_cost(X_Scalar_test,y_Scalar_test, coeff75_1000))\n",
    "cost.append(get_cost(X_Scalar_test,y_Scalar_test, coeff75_2500))\n",
    "cost.append(get_cost(X_Scalar_test,y_Scalar_test, coeff75_5000))\n",
    "cost.append(get_cost(X_Scalar_test,y_Scalar_test, coeff75_7500))\n",
    "cost.append(get_cost(X_Scalar_test,y_Scalar_test, coeff75_10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMOSwGSR8YaeZDWcOm0FiwuhqakZU+jCamQ1LDptBQpJvNWBmw5bDZhDxrQbMbLhy2AwiDd6zMbNhymEziNTX1bJpWwsbtu4suitmZv3KYTOIHDxpNAB3LHu54J6YmfUvh80g8vZDJ3HsQXV84WdLeWG9z92Y2fDhsBlEqioruPKMmbS1BZ9a8Ahtbb79g5kNDw6bQeagCaP5/J/N4LdPr+H6e58pujtmZv3CYTMIndHYwLsO34+v3Lac5S9tKro7ZmZ7zWEzCEniy6cdxb41VXxi3sNsb2ktuktmZnvFYTNITdxnJJefdjRPvLSJK3/5ZNHdMTPbKw6bQezEw/fjrOMO5NpfP819T68pujtmZnvMYTPI/d2fHs5B42v5mwWPsHGbL/Y0s6HJYTPIjR5ZxZVnzuKljdv4wsKlRXfHzGyPOGyGgGMOrOOCdxzCTQ+9wC2Pvlh0d8zM+sxhM0R8/J2HMLN+LJ/7r0d5eeO2ortjZtYnDpshYkRlBVeeOYttO1v59I1LiPDsAmY2dDhshpDXT9qHvz31cH795GpuuO+5ortjZtZrDpsh5i+OP4i3HzqJf/754zS/8lrR3TEz6xWHzRAjiStOP5ra6ko+OX8xO1vbiu6SmVmPHDZD0Ov2HcW/fOAoHn1hA9+886miu2Nm1iOHzRB18pGTOf3Yeq6+u5lFz60rujtmZt1y2Axh//BnMzhgXA2XLFjM5u0tRXfHzKxLDpshbMyoEVx5xiyeX7uFL968rOjumJl1yWEzxB03bTx/9SevZ96DK/jlspeL7o6ZWaccNsPAJe8+lMMn78ulP1nC6k3bi+6OmdkfcNgMA9VVFXxjziw2bW9h7k2eXcDMBh+HzTBx6H5j+OzJh3HH468w78EVRXfHzGw3uYaNpJMlLZfULOnSTpaPlDQ/Lb9f0tSSZXNT+XJJJ/XUpqRpqY2nUpvVqfwcSaslLU6P8/Lc5iJ99I+n8pZDJvDFm5fx7Kubi+6OmdkuuYWNpErgauAUYAZwlqQZHaqdC6yLiEOAq4DL07ozgDnAEcDJwDWSKnto83LgqoiYDqxLbbebHxGz0uN7OWzuoFBRIb76wZlUVYhPLlhMi2cXMLNBIs89m+OA5oh4OiJ2APOA2R3qzAZ+kJ7fCJwoSal8XkRsj4hngObUXqdtpnXemdogtfn+HLdt0Jo8toYvvv9IHn5+Pd++53dFd8fMDMg3bKYApScPVqayTutERAuwAZjQzbpdlU8A1qc2Onuv0yQtkXSjpIbOOivpfElNkppWr17d+60chGbPmsL7Zh7AN+58iiUr1xfdHTOzXMNGnZR1HCbVVZ3+Kgf4GTA1Io4G7uD3e1K7V464NiIaI6Jx0qRJnVUZUr44+0gmjRnJxfMXs3VHa9HdMbMyl2fYrARK9yLqgVVd1ZFUBYwF1nazblflrwLjUhu7vVdErImI9otP/g04dq+2aogYWzuCr35wJk+v3sy/3Pp40d0xszKXZ9g8CExPo8SqyU74L+xQZyFwdnp+OnBXZBeJLATmpNFq04DpwANdtZnWuTu1QWrzpwCSJpe83/uAsvnN+5ZDJnLuW6fxw98+xz3LXym6O2ZWxnILm3T+5ELgdrJf8AsiYqmkyyS9L1W7DpggqRm4BLg0rbsUWAAsA24DLoiI1q7aTG19FrgktTUhtQ1wkaSlkh4BLgLOyWubB6NPn/QGDt1vHz594xLWbd5RdHfMrEzJV5v/ocbGxmhqaiq6G/1m6aoNvP/qe3nX4ftxzYeOIRu8Z2bWvyQtiojGzpZ5BoEycMQBY7nk3W/g1sde4qaHXii6O2ZWhhw2ZeL8PzmY46aO5x8WLmXF2i1Fd8fMyozDpkxUVoivnTETgL9Z8AitbT58amYDx2FTRhrG1/KF9x3BA8+u5d9+83TR3TGzMuKwKTOnHTOFk4/Yn6/9YjlLV20oujtmViYcNmVGEl/6wFGMq63mk/MXs22nZxcws/w5bMrQ+NHVXHH60Tz58mtccfvyortjZmXAYVOmTnjD6/jw8Qdx3f88w73NrxbdHTMb5hw2Zexzpx7OwRNH86kfP8KGLTuL7o6ZDWMOmzJWU13JVWfOYvWm7Xx+4WNFd8fMhjGHTZmb2TCOi06czk8Xr2LhIx0n5TYz6x8OG+NjJ7yeNx44jr/7r0d5ccPWortjZsOQw8aoqqzgqjNmsbM1+NSPH6HNswuYWT9z2BgAUyeO5u/fO4N7m9fw/f/3bNHdMbNhxmFju5x1XAMnHvY6Lr/tCZ58eVPR3TGzYcRhY7tI4sunHc2YkVVcPG8xO1raiu6SmQ0TDhvbzaQxI/nyaUez7MWNXHXHk0V3x8yGCYeN/YF3z9iPOW9q4Du/+h0PPLO26O6Y2TDgsLFO/d17Z9BQV8slCxazaZtnFzCzveOwsU7tM7KKq86cyar1W/nHny0rujtmNsQ5bKxLxx40no+dcAg3LlrJbY+9WHR3zGwIc9hYtz7xrukcNWUsc296lFc2biu6O2Y2RDlsrFsjKiu46syZbNnRymd+soQIzy5gZn3nsLEeHfK6MXzu1MO5Z/lq/u/9zxfdHTMbghw21isfPv4g3jZ9Iv/882X8bvVrRXfHzIYYh431SkWF+OoHZzKyqpJL5i9mZ6tnFzCz3nPYWK/tt+8ovvTnR/HIyg18667mortjZkOIw8b65E+PnswH3jiFq+9u5qHn1xXdHTMbIhw21mdfmH0E++87ikvmL2bz9paiu2NmQ4DDxvps31Ej+NoZM3lu7Rb++ZbHi+6OmQ0BDhvbI8cfPIHz33Yw/3H/89z5+MtFd8fMBjmHje2xS95zKIftP4bP/mQJr762vejumNkg5rCxPTayqpKvz5nFxq0tzL3pUc8uYGZdctjYXjls/3359Elv4JfLXmZB04qiu2Nmg5TDxvbauW+dxpsPnsA//mwZz63ZXHR3zGwQctjYXquoEF89YyaVFeLi+Yv5xdKX+J+nXmXRc+t4/MWNPLdmM69s2sbm7S20tflQm1k5qiq6AzY8TBlXwz+9/0gunr+Y829Y1G3dUSMqqK2uomZEJbXV2aOmupLR1VXUVLeXpecjKlNZFaNHVqZ1SuuVrDuikooKDdAWm1lf5Bo2kk4GvgFUAt+LiC93WD4S+CFwLLAGODMink3L5gLnAq3ARRFxe3dtSpoGzAPGAw8BH46IHd29h/Wv2bOmcNy08ax5bQebt7ewZWcrW3e0smVHK1t3tLCl/fnOVjZvb9m1LKvXwksbt+0q27wjW97Sxz2hPIKstrqKWgeZ2V7JLWwkVQJXA+8GVgIPSloYEaX3GD4XWBcRh0iaA1wOnClpBjAHOAI4ALhD0qFpna7avBy4KiLmSfpOavvbXb1HXttd7iaPrWHy2Jp+a29HS1sWQDtbUmjtHkadBdmWHS1s2d51kG1J9fsryGqrq3a9dpCZdS7PPZvjgOaIeBpA0jxgNlAaNrOBL6TnNwL/KkmpfF5EbAeekdSc2qOzNiU9DrwT+F+pzg9Su9/u6j3C43SHhOqqCqqrKhjLiH5vu6sg25KCbHNXQZbqti9/uUOQbd3Zys7WPQ+yil6eSRW9Cyj1Msd6G3fqZYO9js+C+medm/OmBs5728H93m6eYTMFKB0LuxL4o67qRESLpA3AhFR+X4d1p6TnnbU5AVgfES2d1O/qPV4t7Yik84HzAQ488MC+bKcNUUUGWekeVmmQbd3R1qvrlXobZb39m6r37fWyXq/b69/+9b6idWXiPiNzaTfPsOnsz4uOPwpd1emqvLO/+bqr39t+EBHXAtcCNDY2+kfW9kqeQWY2FOU59Hkl0FDyuh5Y1VUdSVXAWGBtN+t2Vf4qMC610fG9unoPMzMbIHmGzYPAdEnTJFWTnfBf2KHOQuDs9Px04K50LmUhMEfSyDTKbDrwQFdtpnXuTm2Q2vxpD+9hZmYDJLfDaOn8yIXA7WTDlK+PiKWSLgOaImIhcB1wQxoAsJYsPEj1FpANJmgBLoiIVoDO2kxv+VlgnqR/Ah5ObdPVe5iZ2cCR/8j/Q42NjdHU1FR0N8zMhhRJiyKisbNlnq7GzMxy57AxM7PcOWzMzCx3DhszM8udBwh0QtJq4Lmi+1GQiXSYXaEMlftn4O339u/p9h8UEZM6W+Cwsd1IaupqNEm5KPfPwNvv7c9j+30YzczMcuewMTOz3DlsrKNri+7AIFDun4G3v7zlsv0+Z2NmZrnzno2ZmeXOYWNmZrlz2JQBSQ2S7pb0uKSlkj6RysdL+qWkp9K/dalckr4pqVnSEknHlLR1dqr/lKSzu3rPwUZSpaSHJd2cXk+TdH/ajvnplhWk21rMT9t+v6SpJW3MTeXLJZ1UzJbsGUnjJN0o6Yn0c/DmMvv+P5l+9h+T9J+SRg3nnwFJ10t6RdJjJWX99n1LOlbSo2mdb0q9uBd3RPgxzB/AZOCY9HwM8CQwA/gKcGkqvxS4PD0/FbiV7C6nxwP3p/LxwNPp37r0vK7o7evlZ3AJ8B/Azen1AmBOev4d4K/T848B30nP5wDz0/MZwCPASGAa8Dugsujt6sP2/wA4Lz2vBsaVy/dPdmv4Z4Caku/+nOH8MwD8CXAM8FhJWb9932T3F3tzWudW4JQe+1T0h+LHwD/Ibiz3bmA5MDmVTQaWp+ffBc4qqb88LT8L+G5J+W71BuuD7M6tdwLvBG5O/0FeBarS8jcDt6fntwNvTs+rUj0Bc4G5JW3uqjfYH8C+6ZetOpSXy/c/BViRfmlWpZ+Bk4b7zwAwtUPY9Mv3nZY9UVK+W72uHj6MVmbSIYE3AvcD+0XEiwDp39elau3/OdutTGVdlQ92Xwc+A7Sl1xOA9RHRkl6XbseubUzLN6T6Q3XbAQ4GVgPfT4cSvydpNGXy/UfEC8BXgeeBF8m+00WU188A9N/3PSU971jeLYdNGZG0D/AT4OKI2Nhd1U7KopvyQUvSe4FXImJRaXEnVaOHZUNu20tUkR1S+XZEvBHYTHYYpSvD6jNI5yZmkx36OgAYDZzSSdXh/DPQnb5u7x59Dg6bMiFpBFnQ/CgibkrFL0uanJZPBl5J5SuBhpLV64FV3ZQPZm8B3ifpWWAe2aG0rwPjJLXfFr10O3ZtY1o+lux24kNx29utBFZGxP3p9Y1k4VMO3z/Au4BnImJ1ROwEbgL+mPL6GYD++75Xpucdy7vlsCkDaaTIdcDjEXFlyaKFQPsIk7PJzuW0l38kjVI5HtiQdrtvB94jqS79tfieVDZoRcTciKiPiKlkJ3vviogPAXcDp6dqHbe9/TM5PdWPVD4njVSaBkwnO0k66EXES8AKSW9IRScCyyiD7z95HjheUm36v9C+/WXzM5D0y/edlm2SdHz6PD9S0lbXij6J5Uf+D+CtZLu5S4DF6XEq2XHoO4Gn0r/jU30BV5ONtnkUaCxp6y+B5vT4aNHb1sfP4QR+PxrtYLJfFM3Aj4GRqXxUet2clh9csv7fps9kOb0YfTOYHsAsoCn9DPw32eiisvn+gX8EngAeA24gG1E2bH8GgP8kOz+1k2xP5Nz+/L6BxvRZ/g74VzoMPuns4elqzMwsdz6MZmZmuXPYmJlZ7hw2ZmaWO4eNmZnlzmFjZma5c9hYWZAUkr5W8vpTkr7QT23/u6TTe6651+/zwTRj890dyg+QdGN6PkvSqf34nuMkfayz9zLrC4eNlYvtwAckTSy6I6UkVfah+rnAxyLiHaWFEbEqItrDbhbZNVR96UNVN4vHkc2C3Nl7mfWaw8bKRQvZvdU/2XFBxz0TSa+lf0+Q9CtJCyQ9KenLkj4k6YF0L4/XlzTzLkm/SfXem9avlHSFpAfTfUL+qqTduyX9B9lFdB37c1Zq/zFJl6eyz5NdnPsdSVd0qD811a0GLgPOlLRY0pmSRiu7t8mDaRLO2WmdcyT9WNLPgF9I2kfSnZIeSu89OzX/ZeD1qb0r2t8rtTFK0vdT/YclvaOk7Zsk3absPihfKfk8/j319VFJf/Bd2PDV3V80ZsPN1cCS9l9+vTQTOJxsbqynge9FxHHKbkD3ceDiVG8q8Hbg9cDdkg4hm8ZjQ0S8SdJI4F5Jv0j1jwOOjIhnSt9M0gHA5cCxwDqyIHh/RFwm6Z3ApyKiqXk76+4AAAKMSURBVLOORsSOFEqNEXFhau9LZNOt/KWkccADku5Iq7wZODoi1qa9mz+PiI1p7+8+SQvJJuw8MiJmpfamlrzlBel9j5J0WOrroWnZLLLZxbcDyyV9i2yW4SkRcWRqa1z3H70NJ96zsbIR2UzXPwQu6sNqD0bEixGxnWxqjvaweJQsYNotiIi2iHiKLJQOI5tL6iOSFpPd0mEC2XxaAA90DJrkTcA9kU0a2QL8iOxGWHvqPcClqQ/3kE3FcmBa9suIWJueC/iSpCXAHWRTxu/XQ9tvJZv6hYh4AngOaA+bOyNiQ0RsI5uH7CCyz+VgSd+SdDLQ3czjNsx4z8bKzdeBh4Dvl5S1kP7wShMLVpcs217yvK3kdRu7///pOO9T+1TsH4+I3SarlHQC2TT/nen59rp9I+C0iFjeoQ9/1KEPHwImAcdGxE5ls2SP6kXbXSn93FrJblK2TtJMshuXXQCcQTb3lpUB79lYWUl/yS8gO9ne7lmyw1aQ3fdkxB40/UFJFek8zsFkEzXeDvy1sts7IOlQZTct6879wNslTUyDB84CftWHfmwiu/V3u9uBj6cQRdIbu1hvLNl9f3amcy8HddFeqV+ThRTp8NmBZNvdqXR4riIifgL8PdltDqxMOGysHH0NKB2V9m9kv+AfADr+xd9by8lC4Vbg/6TDR98jO4T0UDqp/l16OJoQ2fTtc8mmv38EeCgiep6+/ffuBma0DxAAvkgWnktSH77YxXo/AholNZEFyBOpP2vIzjU91nFgAnANUCnpUWA+cE463NiVKcA96ZDev6fttDLhWZ/NzCx33rMxM7PcOWzMzCx3DhszM8udw8bMzHLnsDEzs9w5bMzMLHcOGzMzy93/B+NnkLsv1DqlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting cost vs iteration for alpha 0.75\n",
    "plt.plot(iteration, cost) \n",
    "plt.xlabel(\"Number of iterations\") \n",
    "plt.ylabel(\"Cost\") \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}