Improved-LinearRegression-v0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "256f8b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b721ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us read the data\n",
    "df = pd.read_csv('train.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f32879",
   "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>GarageArea</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>...</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>...</td>\n",
       "      <td>460</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>...</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>...</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>836</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1          60         65.0     8450            7            5       2003   \n",
       "1   2          20         80.0     9600            6            8       1976   \n",
       "2   3          60         68.0    11250            7            5       2001   \n",
       "3   4          70         60.0     9550            7            5       1915   \n",
       "4   5          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  GarageArea  WoodDeckSF  \\\n",
       "0          2003       196.0         706  ...         548           0   \n",
       "1          1976         0.0         978  ...         460         298   \n",
       "2          2002       162.0         486  ...         608           0   \n",
       "3          1970         0.0         216  ...         642           0   \n",
       "4          2000       350.0         655  ...         836         192   \n",
       "\n",
       "   OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
       "0           61              0          0            0         0        0   \n",
       "1            0              0          0            0         0        0   \n",
       "2           42              0          0            0         0        0   \n",
       "3           35            272          0            0         0        0   \n",
       "4           84              0          0            0         0        0   \n",
       "\n",
       "   MoSold  YrSold  \n",
       "0       2    2008  \n",
       "1       5    2007  \n",
       "2       9    2008  \n",
       "3       2    2006  \n",
       "4      12    2008  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#choose target as SalePrice\n",
    "y = df.SalePrice\n",
    "#Now set features except target\n",
    "X_possible = df.drop(['SalePrice'], axis=1)\n",
    "#To make simple letus drop colums having srting\n",
    "X = X_possible.select_dtypes(exclude = ['object'])\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f12f898",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>GarageArea</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>1293</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6600</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1892</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>12</td>\n",
       "      <td>2009</td>\n",
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       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>1019</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10784</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1991</td>\n",
       "      <td>1992</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>402</td>\n",
       "      <td>164</td>\n",
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       "      <td>2007</td>\n",
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       "      <td>10246</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>648</td>\n",
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       "      <td>364</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>1431</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>21930</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2006</td>\n",
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       "      <th>810</th>\n",
       "      <td>811</td>\n",
       "      <td>20</td>\n",
       "      <td>78.0</td>\n",
       "      <td>10140</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1974</td>\n",
       "      <td>1999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>663</td>\n",
       "      <td>...</td>\n",
       "      <td>484</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
       "1292  1293          70         60.0     6600            5            4   \n",
       "1018  1019          80          NaN    10784            7            5   \n",
       "1213  1214          80          NaN    10246            4            9   \n",
       "1430  1431          60         60.0    21930            5            5   \n",
       "810    811          20         78.0    10140            6            6   \n",
       "\n",
       "      YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  GarageArea  \\\n",
       "1292       1892          1965         0.0           0  ...         432   \n",
       "1018       1991          1992        76.0           0  ...         402   \n",
       "1213       1965          2001         0.0         648  ...         364   \n",
       "1430       2005          2005         0.0           0  ...         372   \n",
       "810        1974          1999        99.0         663  ...         484   \n",
       "\n",
       "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "1292           0          287              0          0            0   \n",
       "1018         164            0              0          0            0   \n",
       "1213          88            0              0          0            0   \n",
       "1430         100           40              0          0            0   \n",
       "810          265            0              0          0            0   \n",
       "\n",
       "      PoolArea  MiscVal  MoSold  YrSold  \n",
       "1292         0        0      12    2009  \n",
       "1018         0        0       5    2007  \n",
       "1213         0        0       5    2006  \n",
       "1430         0        0       7    2006  \n",
       "810        648        0       1    2006  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X,y, random_state=0)\n",
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97cff3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Many columns has missing values so let us find those column which has missing values\n",
    "X_missing = [ col for col in X_train.columns if X_train[col].isnull().any()]\n",
    "#now drops the colums which has missing value in train and test set\n",
    "X_reduced_train = X_train.drop(X_missing, axis=1)\n",
    "X_reduced_val = X_val.drop(X_missing, axis=1)\n",
    "X_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8285a8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19895.280821917808"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now fit the model\n",
    "model_1 = RandomForestRegressor(n_estimators=10, random_state=0)\n",
    "model_1.fit(X_reduced_train, y_train)\n",
    "predict_1 = model_1.predict(X_reduced_val)\n",
    "mae_1 = mean_absolute_error(predict_1, y_val)\n",
    "mae_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf58507",
   "metadata": {},
   "source": [
    "# Let us use imputation to replace the missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d2c1bbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9533996",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_imputer = SimpleImputer()\n",
    "X_impute_train = pd.DataFrame(my_imputer.fit_transform(X_train))\n",
    "X_impute_val = pd.DataFrame(my_imputer.fit_transform(X_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5d52c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputation removed column names so that need to put them back\n",
    "X_impute_train.columns = X_train.columns\n",
    "X_impute_val.columns = X_val.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fd5e7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18909.105205479453"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now fit the model \n",
    "model_2 = RandomForestRegressor(n_estimators=10, random_state=1)\n",
    "model_2.fit(X_impute_train, y_train)\n",
    "predict_2 = model_2.predict(X_impute_val)\n",
    "mae_2 = mean_absolute_error(predict_2, y_val)\n",
    "mae_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f210c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Another way to impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "acf4625a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First copy data\n",
    "X_train_new = X_train.copy()\n",
    "X_val_new = X_val.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "995d3934",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in X_missing:\n",
    "    X_train_new[col + '_was_missing'] = X_train_new[col].isnull()\n",
    "    X_val_new[col + '_was_missing'] = X_val_new[col].isnull()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a899b3e4",
   "metadata": {},
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>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>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>LotFrontage_was_missing</th>\n",
       "      <th>MasVnrArea_was_missing</th>\n",
       "      <th>GarageYrBlt_was_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>1293</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6600</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1892</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>1019</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10784</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1991</td>\n",
       "      <td>1992</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>1214</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10246</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>1965</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>648</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>1431</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>21930</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>811</td>\n",
       "      <td>20</td>\n",
       "      <td>78.0</td>\n",
       "      <td>10140</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1974</td>\n",
       "      <td>1999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>663</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
       "1292  1293          70         60.0     6600            5            4   \n",
       "1018  1019          80          NaN    10784            7            5   \n",
       "1213  1214          80          NaN    10246            4            9   \n",
       "1430  1431          60         60.0    21930            5            5   \n",
       "810    811          20         78.0    10140            6            6   \n",
       "\n",
       "      YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  EnclosedPorch  \\\n",
       "1292       1892          1965         0.0           0  ...              0   \n",
       "1018       1991          1992        76.0           0  ...              0   \n",
       "1213       1965          2001         0.0         648  ...              0   \n",
       "1430       2005          2005         0.0           0  ...              0   \n",
       "810        1974          1999        99.0         663  ...              0   \n",
       "\n",
       "      3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "1292          0            0         0        0      12    2009   \n",
       "1018          0            0         0        0       5    2007   \n",
       "1213          0            0         0        0       5    2006   \n",
       "1430          0            0         0        0       7    2006   \n",
       "810           0            0       648        0       1    2006   \n",
       "\n",
       "      LotFrontage_was_missing  MasVnrArea_was_missing  GarageYrBlt_was_missing  \n",
       "1292                    False                   False                    False  \n",
       "1018                     True                   False                    False  \n",
       "1213                     True                   False                    False  \n",
       "1430                    False                   False                    False  \n",
       "810                     False                   False                    False  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42453cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_imputer = SimpleImputer()\n",
    "X_impute_train_new = pd.DataFrame(my_imputer.fit_transform(X_train_new))\n",
    "X_impute_val_new = pd.DataFrame(my_imputer.fit_transform(X_val_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c911e43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_impute_train_new.columns = X_train_new.columns\n",
    "X_impute_val_new.columns = X_val_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8015ef52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19125.07698630137"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3 = RandomForestRegressor(n_estimators=10, random_state=1)\n",
    "model_3.fit(X_impute_train_new, y_train)\n",
    "predict_3 = model_3.predict(X_impute_val_new)\n",
    "mae_3 = mean_absolute_error(predict_3, y_val)\n",
    "mae_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "972d14e2",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "e6e439e7",
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   "outputs": [],
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   "cell_type": "code",
   "execution_count": null,
   "id": "3fc3f7de",
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   "outputs": [],
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   "cell_type": "code",
   "execution_count": null,
   "id": "c1247d7b",
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   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd057347",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637ea547",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a6ed48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a19c700b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c20b166",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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