PRODIGY_ML_01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "931da6c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a842141e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('train.csv')\n",
    "test_df = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86ebc7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc744841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0178fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
       "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
       "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
       "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
       "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
       "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
       "\n",
       "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
       "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
       "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
       "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
       "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
       "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
       "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c09a2efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1232.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1444.000000</td>\n",
       "      <td>1458.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "      <td>1459.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2190.000000</td>\n",
       "      <td>57.378341</td>\n",
       "      <td>68.580357</td>\n",
       "      <td>9819.161069</td>\n",
       "      <td>6.078821</td>\n",
       "      <td>5.553804</td>\n",
       "      <td>1971.357779</td>\n",
       "      <td>1983.662783</td>\n",
       "      <td>100.709141</td>\n",
       "      <td>439.203704</td>\n",
       "      <td>...</td>\n",
       "      <td>93.174777</td>\n",
       "      <td>48.313914</td>\n",
       "      <td>24.243317</td>\n",
       "      <td>1.794380</td>\n",
       "      <td>17.064428</td>\n",
       "      <td>1.744345</td>\n",
       "      <td>58.167923</td>\n",
       "      <td>6.104181</td>\n",
       "      <td>2007.769705</td>\n",
       "      <td>179183.918243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.321334</td>\n",
       "      <td>42.746880</td>\n",
       "      <td>22.376841</td>\n",
       "      <td>4955.517327</td>\n",
       "      <td>1.436812</td>\n",
       "      <td>1.113740</td>\n",
       "      <td>30.390071</td>\n",
       "      <td>21.130467</td>\n",
       "      <td>177.625900</td>\n",
       "      <td>455.268042</td>\n",
       "      <td>...</td>\n",
       "      <td>127.744882</td>\n",
       "      <td>68.883364</td>\n",
       "      <td>67.227765</td>\n",
       "      <td>20.207842</td>\n",
       "      <td>56.609763</td>\n",
       "      <td>30.491646</td>\n",
       "      <td>630.806978</td>\n",
       "      <td>2.722432</td>\n",
       "      <td>1.301740</td>\n",
       "      <td>16518.303051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1461.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>135751.318900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1825.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7391.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1953.000000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>168703.011200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2190.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>9399.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>350.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>179208.665700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2554.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11517.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>753.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>186789.409400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>56600.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1290.000000</td>\n",
       "      <td>4010.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>281643.976100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage       LotArea  OverallQual  \\\n",
       "count  1459.000000  1459.000000  1232.000000   1459.000000  1459.000000   \n",
       "mean   2190.000000    57.378341    68.580357   9819.161069     6.078821   \n",
       "std     421.321334    42.746880    22.376841   4955.517327     1.436812   \n",
       "min    1461.000000    20.000000    21.000000   1470.000000     1.000000   \n",
       "25%    1825.500000    20.000000    58.000000   7391.000000     5.000000   \n",
       "50%    2190.000000    50.000000    67.000000   9399.000000     6.000000   \n",
       "75%    2554.500000    70.000000    80.000000  11517.500000     7.000000   \n",
       "max    2919.000000   190.000000   200.000000  56600.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
       "count  1459.000000  1459.000000   1459.000000  1444.000000  1458.000000  ...   \n",
       "mean      5.553804  1971.357779   1983.662783   100.709141   439.203704  ...   \n",
       "std       1.113740    30.390071     21.130467   177.625900   455.268042  ...   \n",
       "min       1.000000  1879.000000   1950.000000     0.000000     0.000000  ...   \n",
       "25%       5.000000  1953.000000   1963.000000     0.000000     0.000000  ...   \n",
       "50%       5.000000  1973.000000   1992.000000     0.000000   350.500000  ...   \n",
       "75%       6.000000  2001.000000   2004.000000   164.000000   753.500000  ...   \n",
       "max       9.000000  2010.000000   2010.000000  1290.000000  4010.000000  ...   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
       "count  1459.000000  1459.000000    1459.000000  1459.000000  1459.000000   \n",
       "mean     93.174777    48.313914      24.243317     1.794380    17.064428   \n",
       "std     127.744882    68.883364      67.227765    20.207842    56.609763   \n",
       "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "50%       0.000000    28.000000       0.000000     0.000000     0.000000   \n",
       "75%     168.000000    72.000000       0.000000     0.000000     0.000000   \n",
       "max    1424.000000   742.000000    1012.000000   360.000000   576.000000   \n",
       "\n",
       "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
       "count  1459.000000   1459.000000  1459.000000  1459.000000    1459.000000  \n",
       "mean      1.744345     58.167923     6.104181  2007.769705  179183.918243  \n",
       "std      30.491646    630.806978     2.722432     1.301740   16518.303051  \n",
       "min       0.000000      0.000000     1.000000  2006.000000  135751.318900  \n",
       "25%       0.000000      0.000000     4.000000  2007.000000  168703.011200  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  179208.665700  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  186789.409400  \n",
       "max     800.000000  17000.000000    12.000000  2010.000000  281643.976100  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d5151de",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df[['GrLivArea', 'BedroomAbvGr', 'FullBath', 'BsmtFullBath', 'HalfBath', 'BsmtHalfBath', 'SalePrice']]\n",
    "test_df = test_df[['GrLivArea' ,'BedroomAbvGr', 'FullBath', 'BsmtFullBath', 'HalfBath', 'BsmtHalfBath', 'SalePrice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0e82f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1710</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1262</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1786</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1717</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2198</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GrLivArea  BedroomAbvGr  FullBath  BsmtFullBath  HalfBath  BsmtHalfBath  \\\n",
       "0       1710             3         2             1         1             0   \n",
       "1       1262             3         2             0         0             1   \n",
       "2       1786             3         2             1         1             0   \n",
       "3       1717             3         1             1         0             0   \n",
       "4       2198             4         2             1         1             0   \n",
       "\n",
       "   SalePrice  \n",
       "0     208500  \n",
       "1     181500  \n",
       "2     223500  \n",
       "3     140000  \n",
       "4     250000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37fc1443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>896</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169277.0525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1329</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187758.3940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1629</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183583.6836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1604</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179317.4775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1280</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150730.0800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GrLivArea  BedroomAbvGr  FullBath  BsmtFullBath  HalfBath  BsmtHalfBath  \\\n",
       "0        896             2         1           0.0         0           0.0   \n",
       "1       1329             3         1           0.0         1           0.0   \n",
       "2       1629             3         2           0.0         1           0.0   \n",
       "3       1604             3         2           0.0         1           0.0   \n",
       "4       1280             2         2           0.0         0           0.0   \n",
       "\n",
       "     SalePrice  \n",
       "0  169277.0525  \n",
       "1  187758.3940  \n",
       "2  183583.6836  \n",
       "3  179317.4775  \n",
       "4  150730.0800  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d4f76e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GrLivArea       0\n",
       "BedroomAbvGr    0\n",
       "FullBath        0\n",
       "BsmtFullBath    0\n",
       "HalfBath        0\n",
       "BsmtHalfBath    0\n",
       "SalePrice       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.dropna(axis=0, inplace=True)\n",
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b1ee4ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GrLivArea       0\n",
       "BedroomAbvGr    0\n",
       "FullBath        0\n",
       "BsmtFullBath    0\n",
       "HalfBath        0\n",
       "BsmtHalfBath    0\n",
       "SalePrice       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8ad5d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GrLivArea       1460\n",
       "BedroomAbvGr    1460\n",
       "FullBath        1460\n",
       "BsmtFullBath    1460\n",
       "HalfBath        1460\n",
       "BsmtHalfBath    1460\n",
       "SalePrice       1460\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87f94575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GrLivArea       1457\n",
       "BedroomAbvGr    1457\n",
       "FullBath        1457\n",
       "BsmtFullBath    1457\n",
       "HalfBath        1457\n",
       "BsmtHalfBath    1457\n",
       "SalePrice       1457\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "705c42ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_df.drop('SalePrice', axis=1), test_df.drop('SalePrice', axis=1), train_df['SalePrice'], test_df['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "136d883f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
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       "   GrLivArea  BedroomAbvGr  FullBath  BsmtFullBath  HalfBath  BsmtHalfBath\n",
       "0       1710             3         2             1         1             0\n",
       "1       1262             3         2             0         0             1\n",
       "2       1786             3         2             1         1             0\n",
       "3       1717             3         1             1         0             0\n",
       "4       2198             4         2             1         1             0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd4cbc71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   GrLivArea  BedroomAbvGr  FullBath  BsmtFullBath  HalfBath  BsmtHalfBath\n",
       "0        896             2         1           0.0         0           0.0\n",
       "1       1329             3         1           0.0         1           0.0\n",
       "2       1629             3         2           0.0         1           0.0\n",
       "3       1604             3         2           0.0         1           0.0\n",
       "4       1280             2         2           0.0         0           0.0"
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   "id": "c31e48d6",
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   "outputs": [
    {
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       "0    208500\n",
       "1    181500\n",
       "2    223500\n",
       "3    140000\n",
       "4    250000\n",
       "Name: SalePrice, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y_train.head()"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf231d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    169277.0525\n",
       "1    187758.3940\n",
       "2    183583.6836\n",
       "3    179317.4775\n",
       "4    150730.0800\n",
       "Name: SalePrice, dtype: float64"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8101a19c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   GrLivArea  BedroomAbvGr  FullBath  BsmtFullBath  HalfBath  BsmtHalfBath  \\\n",
       "0       1710             3         2             1         1             0   \n",
       "1       1262             3         2             0         0             1   \n",
       "2       1786             3         2             1         1             0   \n",
       "3       1717             3         1             1         0             0   \n",
       "4       2198             4         2             1         1             0   \n",
       "\n",
       "   Bathrooms  \n",
       "0          4  \n",
       "1          3  \n",
       "2          4  \n",
       "3          2  \n",
       "4          4  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Bathrooms'] = X_train['FullBath'] + X_train['HalfBath'] + X_train['BsmtFullBath'] + X_train['BsmtHalfBath']\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1992e4e",
   "metadata": {},
   "outputs": [
    {
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       "   GrLivArea  BedroomAbvGr  FullBath  BsmtFullBath  HalfBath  BsmtHalfBath  \\\n",
       "0        896             2         1           0.0         0           0.0   \n",
       "1       1329             3         1           0.0         1           0.0   \n",
       "2       1629             3         2           0.0         1           0.0   \n",
       "3       1604             3         2           0.0         1           0.0   \n",
       "4       1280             2         2           0.0         0           0.0   \n",
       "\n",
       "   Bathrooms  \n",
       "0          4  \n",
       "1          3  \n",
       "2          4  \n",
       "3          2  \n",
       "4          4  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
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    }
   ],
   "source": [
    "X_test['Bathrooms'] = X_train['FullBath'] + X_train['HalfBath'] + X_train['BsmtFullBath'] + X_train['BsmtHalfBath']\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf6fff51",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.drop(columns=['FullBath', 'HalfBath', 'BsmtFullBath', 'BsmtHalfBath'], axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62016eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.drop(columns=['FullBath', 'HalfBath', 'BsmtFullBath', 'BsmtHalfBath'], axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9438e46",
   "metadata": {},
   "outputs": [
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       "   GrLivArea  BedroomAbvGr  Bathrooms\n",
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       "2       1786             3          4\n",
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      ]
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e773076f",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   GrLivArea  BedroomAbvGr  Bathrooms\n",
       "0        896             2          4\n",
       "1       1329             3          3\n",
       "2       1629             3          4\n",
       "3       1604             3          2\n",
       "4       1280             2          4"
      ]
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     "execution_count": 24,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4142918",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91a34850",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(X_train, y_train)\n",
    "y_pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e9779f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2131534915.4344862"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f855a020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.027899646529351"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "adb8e67f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validated R2 Score: 0.59\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(lr, X_train, y_train, cv=5, scoring='r2')\n",
    "print(f'Cross-validated R2 Score: {cv_scores.mean():.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0b32e62d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maina\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([237751.06452235])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[2000, 4, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e53b3c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maina\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([240101.22062962])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[1780, 3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e66758ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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