House Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
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       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
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       "      <th>...</th>\n",
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       "      <td>60</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>8</td>\n",
       "      <td>2007</td>\n",
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       "      <td>175000</td>\n",
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       "      <td>1457</td>\n",
       "      <td>20</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
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       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
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       "      <td>Pave</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
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       "      <td>5</td>\n",
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       "      <td>4</td>\n",
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       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
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       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
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       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
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       "<p>1460 rows × 81 columns</p>\n",
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      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv('train.csv')\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
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       "      <th>1</th>\n",
       "      <td>1462</td>\n",
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       "      <td>81.0</td>\n",
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       "      <td>Gar2</td>\n",
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       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "      <th>3</th>\n",
       "      <td>1464</td>\n",
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       "      <td>RL</td>\n",
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       "      <th>4</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0     1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1     1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2     1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3     1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4     1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub  ...         120        0    NaN  MnPrv   \n",
       "1            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "2            Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "3            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "4            HLS    AllPub  ...         144        0    NaN    NaN   \n",
       "...          ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1455         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1457         Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "1458         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0      6    2010        WD         Normal  \n",
       "1           Gar2   12500      6    2010        WD         Normal  \n",
       "2            NaN       0      3    2010        WD         Normal  \n",
       "3            NaN       0      6    2010        WD         Normal  \n",
       "4            NaN       0      1    2010        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1454         NaN       0      6    2006        WD         Normal  \n",
       "1455         NaN       0      4    2006        WD        Abnorml  \n",
       "1456         NaN       0      9    2006        WD        Abnorml  \n",
       "1457        Shed     700      7    2006        WD         Normal  \n",
       "1458         NaN       0     11    2006        WD         Normal  \n",
       "\n",
       "[1459 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.read_csv('test.csv')\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id                  0\n",
      "MSSubClass          0\n",
      "MSZoning            0\n",
      "LotFrontage       259\n",
      "LotArea             0\n",
      "Street              0\n",
      "Alley            1369\n",
      "LotShape            0\n",
      "LandContour         0\n",
      "Utilities           0\n",
      "LotConfig           0\n",
      "LandSlope           0\n",
      "Neighborhood        0\n",
      "Condition1          0\n",
      "Condition2          0\n",
      "BldgType            0\n",
      "HouseStyle          0\n",
      "OverallQual         0\n",
      "OverallCond         0\n",
      "YearBuilt           0\n",
      "YearRemodAdd        0\n",
      "RoofStyle           0\n",
      "RoofMatl            0\n",
      "Exterior1st         0\n",
      "Exterior2nd         0\n",
      "MasVnrType          8\n",
      "MasVnrArea          8\n",
      "ExterQual           0\n",
      "ExterCond           0\n",
      "Foundation          0\n",
      "BsmtQual           37\n",
      "BsmtCond           37\n",
      "BsmtExposure       38\n",
      "BsmtFinType1       37\n",
      "BsmtFinSF1          0\n",
      "BsmtFinType2       38\n",
      "BsmtFinSF2          0\n",
      "BsmtUnfSF           0\n",
      "TotalBsmtSF         0\n",
      "Heating             0\n",
      "HeatingQC           0\n",
      "CentralAir          0\n",
      "Electrical          1\n",
      "1stFlrSF            0\n",
      "2ndFlrSF            0\n",
      "LowQualFinSF        0\n",
      "GrLivArea           0\n",
      "BsmtFullBath        0\n",
      "BsmtHalfBath        0\n",
      "FullBath            0\n",
      "HalfBath            0\n",
      "BedroomAbvGr        0\n",
      "KitchenAbvGr        0\n",
      "KitchenQual         0\n",
      "TotRmsAbvGrd        0\n",
      "Functional          0\n",
      "Fireplaces          0\n",
      "FireplaceQu       690\n",
      "GarageType         81\n",
      "GarageYrBlt        81\n",
      "GarageFinish       81\n",
      "GarageCars          0\n",
      "GarageArea          0\n",
      "GarageQual         81\n",
      "GarageCond         81\n",
      "PavedDrive          0\n",
      "WoodDeckSF          0\n",
      "OpenPorchSF         0\n",
      "EnclosedPorch       0\n",
      "3SsnPorch           0\n",
      "ScreenPorch         0\n",
      "PoolArea            0\n",
      "PoolQC           1453\n",
      "Fence            1179\n",
      "MiscFeature      1406\n",
      "MiscVal             0\n",
      "MoSold              0\n",
      "YrSold              0\n",
      "SaleType            0\n",
      "SaleCondition       0\n",
      "SalePrice           0\n"
     ]
    }
   ],
   "source": [
    "nandata=train.isnull().sum()\n",
    "print(nandata.to_string())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id                  0\n",
      "MSSubClass          0\n",
      "MSZoning            4\n",
      "LotFrontage       227\n",
      "LotArea             0\n",
      "Street              0\n",
      "Alley            1352\n",
      "LotShape            0\n",
      "LandContour         0\n",
      "Utilities           2\n",
      "LotConfig           0\n",
      "LandSlope           0\n",
      "Neighborhood        0\n",
      "Condition1          0\n",
      "Condition2          0\n",
      "BldgType            0\n",
      "HouseStyle          0\n",
      "OverallQual         0\n",
      "OverallCond         0\n",
      "YearBuilt           0\n",
      "YearRemodAdd        0\n",
      "RoofStyle           0\n",
      "RoofMatl            0\n",
      "Exterior1st         1\n",
      "Exterior2nd         1\n",
      "MasVnrType         16\n",
      "MasVnrArea         15\n",
      "ExterQual           0\n",
      "ExterCond           0\n",
      "Foundation          0\n",
      "BsmtQual           44\n",
      "BsmtCond           45\n",
      "BsmtExposure       44\n",
      "BsmtFinType1       42\n",
      "BsmtFinSF1          1\n",
      "BsmtFinType2       42\n",
      "BsmtFinSF2          1\n",
      "BsmtUnfSF           1\n",
      "TotalBsmtSF         1\n",
      "Heating             0\n",
      "HeatingQC           0\n",
      "CentralAir          0\n",
      "Electrical          0\n",
      "1stFlrSF            0\n",
      "2ndFlrSF            0\n",
      "LowQualFinSF        0\n",
      "GrLivArea           0\n",
      "BsmtFullBath        2\n",
      "BsmtHalfBath        2\n",
      "FullBath            0\n",
      "HalfBath            0\n",
      "BedroomAbvGr        0\n",
      "KitchenAbvGr        0\n",
      "KitchenQual         1\n",
      "TotRmsAbvGrd        0\n",
      "Functional          2\n",
      "Fireplaces          0\n",
      "FireplaceQu       730\n",
      "GarageType         76\n",
      "GarageYrBlt        78\n",
      "GarageFinish       78\n",
      "GarageCars          1\n",
      "GarageArea          1\n",
      "GarageQual         78\n",
      "GarageCond         78\n",
      "PavedDrive          0\n",
      "WoodDeckSF          0\n",
      "OpenPorchSF         0\n",
      "EnclosedPorch       0\n",
      "3SsnPorch           0\n",
      "ScreenPorch         0\n",
      "PoolArea            0\n",
      "PoolQC           1456\n",
      "Fence            1169\n",
      "MiscFeature      1408\n",
      "MiscVal             0\n",
      "MoSold              0\n",
      "YrSold              0\n",
      "SaleType            1\n",
      "SaleCondition       0\n"
     ]
    }
   ],
   "source": [
    "nandata=test.isnull().sum()\n",
    "print(nandata.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.hist(figsize=[20,20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "2            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "3            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "4            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "...          ...       ...  ...         ...      ...    ...    ...   \n",
       "1455         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "1457         Lvl    AllPub  ...           0        0    NaN  GdPrv   \n",
       "1458         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1459         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0      2    2008        WD         Normal  \n",
       "1            NaN       0      5    2007        WD         Normal  \n",
       "2            NaN       0      9    2008        WD         Normal  \n",
       "3            NaN       0      2    2006        WD        Abnorml  \n",
       "4            NaN       0     12    2008        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1455         NaN       0      8    2007        WD         Normal  \n",
       "1456         NaN       0      2    2010        WD         Normal  \n",
       "1457        Shed    2500      5    2010        WD         Normal  \n",
       "1458         NaN       0      4    2010        WD         Normal  \n",
       "1459         NaN       0      6    2008        WD         Normal  \n",
       "\n",
       "[1460 rows x 80 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.DataFrame(train['SalePrice'])\n",
    "train.drop(['SalePrice'], axis = 1, inplace = True)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SalePrice', ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "sns.distplot(np.log(y['SalePrice']), kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.DataFrame(np.log(y['SalePrice']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "2            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "3            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "4            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "...          ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1455         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1457         Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "1458         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0      2    2008        WD         Normal  \n",
       "1            NaN       0      5    2007        WD         Normal  \n",
       "2            NaN       0      9    2008        WD         Normal  \n",
       "3            NaN       0      2    2006        WD        Abnorml  \n",
       "4            NaN       0     12    2008        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1454         NaN       0      6    2006        WD         Normal  \n",
       "1455         NaN       0      4    2006        WD        Abnorml  \n",
       "1456         NaN       0      9    2006        WD        Abnorml  \n",
       "1457        Shed     700      7    2006        WD         Normal  \n",
       "1458         NaN       0     11    2006        WD         Normal  \n",
       "\n",
       "[2919 rows x 80 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.concat([train,test])\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</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>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>count</th>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2896.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>57.137718</td>\n",
       "      <td>69.305795</td>\n",
       "      <td>10168.114080</td>\n",
       "      <td>6.089072</td>\n",
       "      <td>5.564577</td>\n",
       "      <td>1971.312778</td>\n",
       "      <td>1984.264474</td>\n",
       "      <td>102.201312</td>\n",
       "      <td>441.423235</td>\n",
       "      <td>...</td>\n",
       "      <td>472.874572</td>\n",
       "      <td>93.709832</td>\n",
       "      <td>47.486811</td>\n",
       "      <td>23.098321</td>\n",
       "      <td>2.602261</td>\n",
       "      <td>16.062350</td>\n",
       "      <td>2.251799</td>\n",
       "      <td>50.825968</td>\n",
       "      <td>6.213087</td>\n",
       "      <td>2007.792737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>842.787043</td>\n",
       "      <td>42.517628</td>\n",
       "      <td>23.344905</td>\n",
       "      <td>7886.996359</td>\n",
       "      <td>1.409947</td>\n",
       "      <td>1.113131</td>\n",
       "      <td>30.291442</td>\n",
       "      <td>20.894344</td>\n",
       "      <td>179.334253</td>\n",
       "      <td>455.610826</td>\n",
       "      <td>...</td>\n",
       "      <td>215.394815</td>\n",
       "      <td>126.526589</td>\n",
       "      <td>67.575493</td>\n",
       "      <td>64.244246</td>\n",
       "      <td>25.188169</td>\n",
       "      <td>56.184365</td>\n",
       "      <td>35.663946</td>\n",
       "      <td>567.402211</td>\n",
       "      <td>2.714762</td>\n",
       "      <td>1.314964</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7478.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1953.500000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>320.000000</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>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>9453.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2189.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11570.000000</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>733.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2919.000000</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>1488.000000</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>508.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  2919.000000  2919.000000  2433.000000    2919.000000  2919.000000   \n",
       "mean   1460.000000    57.137718    69.305795   10168.114080     6.089072   \n",
       "std     842.787043    42.517628    23.344905    7886.996359     1.409947   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     730.500000    20.000000    59.000000    7478.000000     5.000000   \n",
       "50%    1460.000000    50.000000    68.000000    9453.000000     6.000000   \n",
       "75%    2189.500000    70.000000    80.000000   11570.000000     7.000000   \n",
       "max    2919.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
       "count  2919.000000  2919.000000   2919.000000  2896.000000  2918.000000  ...   \n",
       "mean      5.564577  1971.312778   1984.264474   102.201312   441.423235  ...   \n",
       "std       1.113131    30.291442     20.894344   179.334253   455.610826  ...   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
       "25%       5.000000  1953.500000   1965.000000     0.000000     0.000000  ...   \n",
       "50%       5.000000  1973.000000   1993.000000     0.000000   368.500000  ...   \n",
       "75%       6.000000  2001.000000   2004.000000   164.000000   733.000000  ...   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
       "\n",
       "        GarageArea   WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count  2918.000000  2919.000000  2919.000000    2919.000000  2919.000000   \n",
       "mean    472.874572    93.709832    47.486811      23.098321     2.602261   \n",
       "std     215.394815   126.526589    67.575493      64.244246    25.188169   \n",
       "min       0.000000     0.000000     0.000000       0.000000     0.000000   \n",
       "25%     320.000000     0.000000     0.000000       0.000000     0.000000   \n",
       "50%     480.000000     0.000000    26.000000       0.000000     0.000000   \n",
       "75%     576.000000   168.000000    70.000000       0.000000     0.000000   \n",
       "max    1488.000000  1424.000000   742.000000    1012.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \n",
       "count  2919.000000  2919.000000   2919.000000  2919.000000  2919.000000  \n",
       "mean     16.062350     2.251799     50.825968     6.213087  2007.792737  \n",
       "std      56.184365    35.663946    567.402211     2.714762     1.314964  \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000  \n",
       "25%       0.000000     0.000000      0.000000     4.000000  2007.000000  \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000  \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000  \n",
       "max     576.000000   800.000000  17000.000000    12.000000  2010.000000  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2919 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             2919 non-null   int64  \n",
      " 1   MSSubClass     2919 non-null   int64  \n",
      " 2   MSZoning       2915 non-null   object \n",
      " 3   LotFrontage    2433 non-null   float64\n",
      " 4   LotArea        2919 non-null   int64  \n",
      " 5   Street         2919 non-null   object \n",
      " 6   Alley          198 non-null    object \n",
      " 7   LotShape       2919 non-null   object \n",
      " 8   LandContour    2919 non-null   object \n",
      " 9   Utilities      2917 non-null   object \n",
      " 10  LotConfig      2919 non-null   object \n",
      " 11  LandSlope      2919 non-null   object \n",
      " 12  Neighborhood   2919 non-null   object \n",
      " 13  Condition1     2919 non-null   object \n",
      " 14  Condition2     2919 non-null   object \n",
      " 15  BldgType       2919 non-null   object \n",
      " 16  HouseStyle     2919 non-null   object \n",
      " 17  OverallQual    2919 non-null   int64  \n",
      " 18  OverallCond    2919 non-null   int64  \n",
      " 19  YearBuilt      2919 non-null   int64  \n",
      " 20  YearRemodAdd   2919 non-null   int64  \n",
      " 21  RoofStyle      2919 non-null   object \n",
      " 22  RoofMatl       2919 non-null   object \n",
      " 23  Exterior1st    2918 non-null   object \n",
      " 24  Exterior2nd    2918 non-null   object \n",
      " 25  MasVnrType     2895 non-null   object \n",
      " 26  MasVnrArea     2896 non-null   float64\n",
      " 27  ExterQual      2919 non-null   object \n",
      " 28  ExterCond      2919 non-null   object \n",
      " 29  Foundation     2919 non-null   object \n",
      " 30  BsmtQual       2838 non-null   object \n",
      " 31  BsmtCond       2837 non-null   object \n",
      " 32  BsmtExposure   2837 non-null   object \n",
      " 33  BsmtFinType1   2840 non-null   object \n",
      " 34  BsmtFinSF1     2918 non-null   float64\n",
      " 35  BsmtFinType2   2839 non-null   object \n",
      " 36  BsmtFinSF2     2918 non-null   float64\n",
      " 37  BsmtUnfSF      2918 non-null   float64\n",
      " 38  TotalBsmtSF    2918 non-null   float64\n",
      " 39  Heating        2919 non-null   object \n",
      " 40  HeatingQC      2919 non-null   object \n",
      " 41  CentralAir     2919 non-null   object \n",
      " 42  Electrical     2918 non-null   object \n",
      " 43  1stFlrSF       2919 non-null   int64  \n",
      " 44  2ndFlrSF       2919 non-null   int64  \n",
      " 45  LowQualFinSF   2919 non-null   int64  \n",
      " 46  GrLivArea      2919 non-null   int64  \n",
      " 47  BsmtFullBath   2917 non-null   float64\n",
      " 48  BsmtHalfBath   2917 non-null   float64\n",
      " 49  FullBath       2919 non-null   int64  \n",
      " 50  HalfBath       2919 non-null   int64  \n",
      " 51  BedroomAbvGr   2919 non-null   int64  \n",
      " 52  KitchenAbvGr   2919 non-null   int64  \n",
      " 53  KitchenQual    2918 non-null   object \n",
      " 54  TotRmsAbvGrd   2919 non-null   int64  \n",
      " 55  Functional     2917 non-null   object \n",
      " 56  Fireplaces     2919 non-null   int64  \n",
      " 57  FireplaceQu    1499 non-null   object \n",
      " 58  GarageType     2762 non-null   object \n",
      " 59  GarageYrBlt    2760 non-null   float64\n",
      " 60  GarageFinish   2760 non-null   object \n",
      " 61  GarageCars     2918 non-null   float64\n",
      " 62  GarageArea     2918 non-null   float64\n",
      " 63  GarageQual     2760 non-null   object \n",
      " 64  GarageCond     2760 non-null   object \n",
      " 65  PavedDrive     2919 non-null   object \n",
      " 66  WoodDeckSF     2919 non-null   int64  \n",
      " 67  OpenPorchSF    2919 non-null   int64  \n",
      " 68  EnclosedPorch  2919 non-null   int64  \n",
      " 69  3SsnPorch      2919 non-null   int64  \n",
      " 70  ScreenPorch    2919 non-null   int64  \n",
      " 71  PoolArea       2919 non-null   int64  \n",
      " 72  PoolQC         10 non-null     object \n",
      " 73  Fence          571 non-null    object \n",
      " 74  MiscFeature    105 non-null    object \n",
      " 75  MiscVal        2919 non-null   int64  \n",
      " 76  MoSold         2919 non-null   int64  \n",
      " 77  YrSold         2919 non-null   int64  \n",
      " 78  SaleType       2918 non-null   object \n",
      " 79  SaleCondition  2919 non-null   object \n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id                0.000000\n",
      "MSSubClass        0.000000\n",
      "MSZoning          0.137033\n",
      "LotFrontage      16.649538\n",
      "LotArea           0.000000\n",
      "Street            0.000000\n",
      "Alley            93.216855\n",
      "LotShape          0.000000\n",
      "LandContour       0.000000\n",
      "Utilities         0.068517\n",
      "LotConfig         0.000000\n",
      "LandSlope         0.000000\n",
      "Neighborhood      0.000000\n",
      "Condition1        0.000000\n",
      "Condition2        0.000000\n",
      "BldgType          0.000000\n",
      "HouseStyle        0.000000\n",
      "OverallQual       0.000000\n",
      "OverallCond       0.000000\n",
      "YearBuilt         0.000000\n",
      "YearRemodAdd      0.000000\n",
      "RoofStyle         0.000000\n",
      "RoofMatl          0.000000\n",
      "Exterior1st       0.034258\n",
      "Exterior2nd       0.034258\n",
      "MasVnrType        0.822199\n",
      "MasVnrArea        0.787941\n",
      "ExterQual         0.000000\n",
      "ExterCond         0.000000\n",
      "Foundation        0.000000\n",
      "BsmtQual          2.774923\n",
      "BsmtCond          2.809181\n",
      "BsmtExposure      2.809181\n",
      "BsmtFinType1      2.706406\n",
      "BsmtFinSF1        0.034258\n",
      "BsmtFinType2      2.740665\n",
      "BsmtFinSF2        0.034258\n",
      "BsmtUnfSF         0.034258\n",
      "TotalBsmtSF       0.034258\n",
      "Heating           0.000000\n",
      "HeatingQC         0.000000\n",
      "CentralAir        0.000000\n",
      "Electrical        0.034258\n",
      "1stFlrSF          0.000000\n",
      "2ndFlrSF          0.000000\n",
      "LowQualFinSF      0.000000\n",
      "GrLivArea         0.000000\n",
      "BsmtFullBath      0.068517\n",
      "BsmtHalfBath      0.068517\n",
      "FullBath          0.000000\n",
      "HalfBath          0.000000\n",
      "BedroomAbvGr      0.000000\n",
      "KitchenAbvGr      0.000000\n",
      "KitchenQual       0.034258\n",
      "TotRmsAbvGrd      0.000000\n",
      "Functional        0.068517\n",
      "Fireplaces        0.000000\n",
      "FireplaceQu      48.646797\n",
      "GarageType        5.378554\n",
      "GarageYrBlt       5.447071\n",
      "GarageFinish      5.447071\n",
      "GarageCars        0.034258\n",
      "GarageArea        0.034258\n",
      "GarageQual        5.447071\n",
      "GarageCond        5.447071\n",
      "PavedDrive        0.000000\n",
      "WoodDeckSF        0.000000\n",
      "OpenPorchSF       0.000000\n",
      "EnclosedPorch     0.000000\n",
      "3SsnPorch         0.000000\n",
      "ScreenPorch       0.000000\n",
      "PoolArea          0.000000\n",
      "PoolQC           99.657417\n",
      "Fence            80.438506\n",
      "MiscFeature      96.402878\n",
      "MiscVal           0.000000\n",
      "MoSold            0.000000\n",
      "YrSold            0.000000\n",
      "SaleType          0.034258\n",
      "SaleCondition     0.000000\n"
     ]
    }
   ],
   "source": [
    "percent_missing = (dataset.isnull().sum()*100/len(dataset))\n",
    "print(percent_missing.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave  None      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave  None      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave  None      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave  None      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave  None      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1454  2915         160       RM         21.0     1936   Pave  None      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave  None      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave  None      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave  None      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave  None      Reg   \n",
       "\n",
       "     LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub  ...           0        0   None   None   \n",
       "1            Lvl    AllPub  ...           0        0   None   None   \n",
       "2            Lvl    AllPub  ...           0        0   None   None   \n",
       "3            Lvl    AllPub  ...           0        0   None   None   \n",
       "4            Lvl    AllPub  ...           0        0   None   None   \n",
       "...          ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub  ...           0        0   None   None   \n",
       "1455         Lvl    AllPub  ...           0        0   None   None   \n",
       "1456         Lvl    AllPub  ...           0        0   None   None   \n",
       "1457         Lvl    AllPub  ...           0        0   None  MnPrv   \n",
       "1458         Lvl    AllPub  ...           0        0   None   None   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0           None       0      2    2008        WD         Normal  \n",
       "1           None       0      5    2007        WD         Normal  \n",
       "2           None       0      9    2008        WD         Normal  \n",
       "3           None       0      2    2006        WD        Abnorml  \n",
       "4           None       0     12    2008        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1454        None       0      6    2006        WD         Normal  \n",
       "1455        None       0      4    2006        WD        Abnorml  \n",
       "1456        None       0      9    2006        WD        Abnorml  \n",
       "1457        Shed     700      7    2006        WD         Normal  \n",
       "1458        None       0     11    2006        WD         Normal  \n",
       "\n",
       "[2919 rows x 80 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['PoolQC'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['Alley'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['Fence'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['MiscFeature'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['FireplaceQu'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['GarageType'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['GarageFinish'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['GarageQual'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['GarageCond'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['BsmtQual'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['BsmtCond'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['BsmtExposure'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['BsmtFinType1'].replace(np.nan, 'None', inplace=True)\n",
    "dataset['BsmtFinType2'].replace(np.nan, 'None', inplace=True)\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"LotFrontage\"] = dataset[\"LotFrontage\"].replace(np.NaN, dataset[\"LotFrontage\"].mean())\n",
    "dataset[\"MasVnrArea\"] = dataset[\"MasVnrArea\"].replace(np.NaN, dataset[\"MasVnrArea\"].mean())\n",
    "dataset[\"BsmtFullBath\"] = dataset[\"BsmtFullBath\"].replace(np.NaN, dataset[\"BsmtFullBath\"].mean())\n",
    "dataset[\"BsmtHalfBath\"] = dataset[\"BsmtHalfBath\"].replace(np.NaN, dataset[\"BsmtHalfBath\"].mean())\n",
    "dataset[\"MasVnrArea\"] = dataset[\"MasVnrArea\"].replace(np.NaN, dataset[\"MasVnrArea\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"SaleType\"] = dataset[\"SaleType\"].replace(np.NaN, dataset['SaleType'].mode()[0][:])\n",
    "dataset[\"Functional\"] = dataset[\"Functional\"].replace(np.NaN, dataset['Functional'].mode()[0][:])\n",
    "dataset[\"KitchenQual\"] = dataset[\"KitchenQual\"].replace(np.NaN, dataset['KitchenQual'].mode()[0][:])\n",
    "dataset[\"Electrical\"] = dataset[\"Electrical\"].replace(np.NaN, dataset['Electrical'].mode()[0][:])\n",
    "dataset[\"SaleType\"] = dataset[\"SaleType\"].replace(np.NaN, dataset['SaleType'].mode()[0][:])\n",
    "dataset[\"MasVnrType\"] = dataset[\"MasVnrType\"].replace(np.NaN, dataset['MasVnrType'].mode()[0][:])\n",
    "dataset[\"Exterior1st\"] = dataset[\"Exterior1st\"].replace(np.NaN, dataset['Exterior1st'].mode()[0][:])\n",
    "dataset[\"Exterior2nd\"] = dataset[\"Exterior2nd\"].replace(np.NaN, dataset['Exterior2nd'].mode()[0][:])\n",
    "dataset[\"MSZoning\"] = dataset[\"MSZoning\"].replace(np.NaN, dataset['MSZoning'].mode()[0][:])\n",
    "dataset[\"Utilities\"] = dataset[\"Utilities\"].replace(np.NaN, dataset['Utilities'].mode()[0][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['BsmtFinSF1'].replace(np.nan, 0, regex=True, inplace=True)\n",
    "dataset['BsmtFinSF2'].replace(np.nan, 0, regex=True, inplace=True)\n",
    "dataset['TotalBsmtSF'].replace(np.nan, 0, regex=True, inplace=True)\n",
    "dataset['BsmtUnfSF'].replace(np.nan, 0, regex=True, inplace=True)\n",
    "dataset['GarageYrBlt'].replace(np.nan, 0, regex=True, inplace=True)\n",
    "dataset['GarageCars'].replace(np.nan, 0, regex=True, inplace=True)\n",
    "dataset['GarageArea'].replace(np.nan, 0, regex=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2919 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             2919 non-null   int64  \n",
      " 1   MSSubClass     2919 non-null   int64  \n",
      " 2   MSZoning       2919 non-null   object \n",
      " 3   LotFrontage    2919 non-null   float64\n",
      " 4   LotArea        2919 non-null   int64  \n",
      " 5   Street         2919 non-null   object \n",
      " 6   Alley          2919 non-null   object \n",
      " 7   LotShape       2919 non-null   object \n",
      " 8   LandContour    2919 non-null   object \n",
      " 9   Utilities      2919 non-null   object \n",
      " 10  LotConfig      2919 non-null   object \n",
      " 11  LandSlope      2919 non-null   object \n",
      " 12  Neighborhood   2919 non-null   object \n",
      " 13  Condition1     2919 non-null   object \n",
      " 14  Condition2     2919 non-null   object \n",
      " 15  BldgType       2919 non-null   object \n",
      " 16  HouseStyle     2919 non-null   object \n",
      " 17  OverallQual    2919 non-null   int64  \n",
      " 18  OverallCond    2919 non-null   int64  \n",
      " 19  YearBuilt      2919 non-null   int64  \n",
      " 20  YearRemodAdd   2919 non-null   int64  \n",
      " 21  RoofStyle      2919 non-null   object \n",
      " 22  RoofMatl       2919 non-null   object \n",
      " 23  Exterior1st    2919 non-null   object \n",
      " 24  Exterior2nd    2919 non-null   object \n",
      " 25  MasVnrType     2919 non-null   object \n",
      " 26  MasVnrArea     2919 non-null   float64\n",
      " 27  ExterQual      2919 non-null   object \n",
      " 28  ExterCond      2919 non-null   object \n",
      " 29  Foundation     2919 non-null   object \n",
      " 30  BsmtQual       2919 non-null   object \n",
      " 31  BsmtCond       2919 non-null   object \n",
      " 32  BsmtExposure   2919 non-null   object \n",
      " 33  BsmtFinType1   2919 non-null   object \n",
      " 34  BsmtFinSF1     2919 non-null   float64\n",
      " 35  BsmtFinType2   2919 non-null   object \n",
      " 36  BsmtFinSF2     2919 non-null   float64\n",
      " 37  BsmtUnfSF      2919 non-null   float64\n",
      " 38  TotalBsmtSF    2919 non-null   float64\n",
      " 39  Heating        2919 non-null   object \n",
      " 40  HeatingQC      2919 non-null   object \n",
      " 41  CentralAir     2919 non-null   object \n",
      " 42  Electrical     2919 non-null   object \n",
      " 43  1stFlrSF       2919 non-null   int64  \n",
      " 44  2ndFlrSF       2919 non-null   int64  \n",
      " 45  LowQualFinSF   2919 non-null   int64  \n",
      " 46  GrLivArea      2919 non-null   int64  \n",
      " 47  BsmtFullBath   2919 non-null   float64\n",
      " 48  BsmtHalfBath   2919 non-null   float64\n",
      " 49  FullBath       2919 non-null   int64  \n",
      " 50  HalfBath       2919 non-null   int64  \n",
      " 51  BedroomAbvGr   2919 non-null   int64  \n",
      " 52  KitchenAbvGr   2919 non-null   int64  \n",
      " 53  KitchenQual    2919 non-null   object \n",
      " 54  TotRmsAbvGrd   2919 non-null   int64  \n",
      " 55  Functional     2919 non-null   object \n",
      " 56  Fireplaces     2919 non-null   int64  \n",
      " 57  FireplaceQu    2919 non-null   object \n",
      " 58  GarageType     2919 non-null   object \n",
      " 59  GarageYrBlt    2919 non-null   float64\n",
      " 60  GarageFinish   2919 non-null   object \n",
      " 61  GarageCars     2919 non-null   float64\n",
      " 62  GarageArea     2919 non-null   float64\n",
      " 63  GarageQual     2919 non-null   object \n",
      " 64  GarageCond     2919 non-null   object \n",
      " 65  PavedDrive     2919 non-null   object \n",
      " 66  WoodDeckSF     2919 non-null   int64  \n",
      " 67  OpenPorchSF    2919 non-null   int64  \n",
      " 68  EnclosedPorch  2919 non-null   int64  \n",
      " 69  3SsnPorch      2919 non-null   int64  \n",
      " 70  ScreenPorch    2919 non-null   int64  \n",
      " 71  PoolArea       2919 non-null   int64  \n",
      " 72  PoolQC         2919 non-null   object \n",
      " 73  Fence          2919 non-null   object \n",
      " 74  MiscFeature    2919 non-null   object \n",
      " 75  MiscVal        2919 non-null   int64  \n",
      " 76  MoSold         2919 non-null   int64  \n",
      " 77  YrSold         2919 non-null   int64  \n",
      " 78  SaleType       2919 non-null   object \n",
      " 79  SaleCondition  2919 non-null   object \n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_features = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n",
    "                'HeatingQC', 'KitchenQual', 'GarageQual', 'GarageCond']\n",
    "\n",
    "ordinal_map={\n",
    "    'Ex': 5,\n",
    "    'Gd': 4,\n",
    "    'TA': 3,\n",
    "    'Fa': 2,\n",
    "    'Po': 1,\n",
    "    'None' :0,\n",
    "}\n",
    "\n",
    "for feature in ordinal_features:\n",
    "    dataset[feature] = dataset[feature].map(ordinal_map)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_map1={\n",
    "    'Gd': 4,\n",
    "    'Av': 3,\n",
    "    'Mn': 2,\n",
    "    'No': 1,\n",
    "    'None' :0,\n",
    "}\n",
    "\n",
    "dataset['BsmtExposure'] = dataset['BsmtExposure'].map(ordinal_map1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0             60       RL         65.0     8450   Pave  None      Reg   \n",
       "1             20       RL         80.0     9600   Pave  None      Reg   \n",
       "2             60       RL         68.0    11250   Pave  None      IR1   \n",
       "3             70       RL         60.0     9550   Pave  None      IR1   \n",
       "4             60       RL         84.0    14260   Pave  None      IR1   \n",
       "...          ...      ...          ...      ...    ...   ...      ...   \n",
       "1454         160       RM         21.0     1936   Pave  None      Reg   \n",
       "1455         160       RM         21.0     1894   Pave  None      Reg   \n",
       "1456          20       RL        160.0    20000   Pave  None      Reg   \n",
       "1457          85       RL         62.0    10441   Pave  None      Reg   \n",
       "1458          60       RL         74.0     9627   Pave  None      Reg   \n",
       "\n",
       "     LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub    Inside  ...           0        0   None   None   \n",
       "1            Lvl    AllPub       FR2  ...           0        0   None   None   \n",
       "2            Lvl    AllPub    Inside  ...           0        0   None   None   \n",
       "3            Lvl    AllPub    Corner  ...           0        0   None   None   \n",
       "4            Lvl    AllPub       FR2  ...           0        0   None   None   \n",
       "...          ...       ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub    Inside  ...           0        0   None   None   \n",
       "1455         Lvl    AllPub    Inside  ...           0        0   None   None   \n",
       "1456         Lvl    AllPub    Inside  ...           0        0   None   None   \n",
       "1457         Lvl    AllPub    Inside  ...           0        0   None  MnPrv   \n",
       "1458         Lvl    AllPub    Inside  ...           0        0   None   None   \n",
       "\n",
       "     MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0           None       0       2    2008        WD         Normal  \n",
       "1           None       0       5    2007        WD         Normal  \n",
       "2           None       0       9    2008        WD         Normal  \n",
       "3           None       0       2    2006        WD        Abnorml  \n",
       "4           None       0      12    2008        WD         Normal  \n",
       "...          ...     ...     ...     ...       ...            ...  \n",
       "1454        None       0       6    2006        WD         Normal  \n",
       "1455        None       0       4    2006        WD        Abnorml  \n",
       "1456        None       0       9    2006        WD        Abnorml  \n",
       "1457        Shed     700       7    2006        WD         Normal  \n",
       "1458        None       0      11    2006        WD         Normal  \n",
       "\n",
       "[2919 rows x 79 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop([\"Id\"], axis=1, inplace=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>TotalPorch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>84</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0             60       RL         65.0     8450   Pave  None      Reg   \n",
       "1             20       RL         80.0     9600   Pave  None      Reg   \n",
       "2             60       RL         68.0    11250   Pave  None      IR1   \n",
       "3             70       RL         60.0     9550   Pave  None      IR1   \n",
       "4             60       RL         84.0    14260   Pave  None      IR1   \n",
       "...          ...      ...          ...      ...    ...   ...      ...   \n",
       "1454         160       RM         21.0     1936   Pave  None      Reg   \n",
       "1455         160       RM         21.0     1894   Pave  None      Reg   \n",
       "1456          20       RL        160.0    20000   Pave  None      Reg   \n",
       "1457          85       RL         62.0    10441   Pave  None      Reg   \n",
       "1458          60       RL         74.0     9627   Pave  None      Reg   \n",
       "\n",
       "     LandContour Utilities LotConfig  ... PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0            Lvl    AllPub    Inside  ...        0   None   None        None   \n",
       "1            Lvl    AllPub       FR2  ...        0   None   None        None   \n",
       "2            Lvl    AllPub    Inside  ...        0   None   None        None   \n",
       "3            Lvl    AllPub    Corner  ...        0   None   None        None   \n",
       "4            Lvl    AllPub       FR2  ...        0   None   None        None   \n",
       "...          ...       ...       ...  ...      ...    ...    ...         ...   \n",
       "1454         Lvl    AllPub    Inside  ...        0   None   None        None   \n",
       "1455         Lvl    AllPub    Inside  ...        0   None   None        None   \n",
       "1456         Lvl    AllPub    Inside  ...        0   None   None        None   \n",
       "1457         Lvl    AllPub    Inside  ...        0   None  MnPrv        Shed   \n",
       "1458         Lvl    AllPub    Inside  ...        0   None   None        None   \n",
       "\n",
       "     MiscVal MoSold  YrSold  SaleType  SaleCondition  TotalPorch  \n",
       "0          0      2    2008        WD         Normal          61  \n",
       "1          0      5    2007        WD         Normal           0  \n",
       "2          0      9    2008        WD         Normal          42  \n",
       "3          0      2    2006        WD        Abnorml         307  \n",
       "4          0     12    2008        WD         Normal          84  \n",
       "...      ...    ...     ...       ...            ...         ...  \n",
       "1454       0      6    2006        WD         Normal           0  \n",
       "1455       0      4    2006        WD        Abnorml          24  \n",
       "1456       0      9    2006        WD        Abnorml           0  \n",
       "1457     700      7    2006        WD         Normal          32  \n",
       "1458       0     11    2006        WD         Normal          48  \n",
       "\n",
       "[2919 rows x 76 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['TotalPorch'] =  dataset['OpenPorchSF'] + dataset['3SsnPorch']+ dataset['ScreenPorch'] + dataset['EnclosedPorch']\n",
    "dataset.drop(['OpenPorchSF', '3SsnPorch', 'ScreenPorch', 'EnclosedPorch'], axis=1, inplace=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Age'] = dataset['YrSold'] - dataset['YearBuilt']\n",
    "dataset.drop(['YrSold', 'YearBuilt'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['TotalSqFeet'] = dataset['1stFlrSF'] + dataset['2ndFlrSF']\n",
    "dataset.drop(['1stFlrSF', '2ndFlrSF'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['TotalBath'] = dataset['FullBath'] + (0.5 * dataset['HalfBath'])\n",
    "dataset['TotalBsmtBath'] = dataset['BsmtFullBath'] + (0.5 * dataset['BsmtHalfBath'])\n",
    "dataset.drop(['FullBath', 'HalfBath', 'BsmtFullBath', 'BsmtHalfBath'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearRemodAdd', 'MasVnrArea', 'ExterQual', 'ExterCond', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',\n",
       "       'TotalBsmtSF', 'HeatingQC', 'LowQualFinSF', 'GrLivArea', 'BedroomAbvGr',\n",
       "       'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond',\n",
       "       'WoodDeckSF', 'PoolArea', 'MiscVal', 'MoSold', 'TotalPorch', 'Age',\n",
       "       'TotalSqFeet', 'TotalBath', 'TotalBsmtBath'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = dataset._get_numeric_data().columns \n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alley', 'BldgType', 'BsmtFinType1', 'BsmtFinType2', 'CentralAir',\n",
       "       'Condition1', 'Condition2', 'Electrical', 'Exterior1st', 'Exterior2nd',\n",
       "       'Fence', 'FireplaceQu', 'Foundation', 'Functional', 'GarageFinish',\n",
       "       'GarageType', 'Heating', 'HouseStyle', 'LandContour', 'LandSlope',\n",
       "       'LotConfig', 'LotShape', 'MSZoning', 'MasVnrType', 'MiscFeature',\n",
       "       'Neighborhood', 'PavedDrive', 'PoolQC', 'RoofMatl', 'RoofStyle',\n",
       "       'SaleCondition', 'SaleType', 'Street', 'Utilities'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = dataset.columns.difference(num_cols)\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2919, 38)\n",
      "(2919, 34)\n"
     ]
    }
   ],
   "source": [
    "num_data = dataset.loc[:, num_cols]\n",
    "cat_data = dataset.loc[:, cat_cols]\n",
    "print(num_data.shape)\n",
    "print(cat_data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "sc = StandardScaler()\n",
    "num_data = pd.DataFrame(sc.fit_transform(num_data),columns = num_data.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "cat_data = cat_data.astype(str).apply(le.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>...</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>Street</th>\n",
       "      <th>Utilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>-0.202068</td>\n",
       "      <td>-0.217879</td>\n",
       "      <td>0.646183</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.896833</td>\n",
       "      <td>0.525202</td>\n",
       "      <td>1.039805</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>0.501870</td>\n",
       "      <td>-0.072044</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>2.188279</td>\n",
       "      <td>-0.395604</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>-0.061280</td>\n",
       "      <td>0.137197</td>\n",
       "      <td>0.646183</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.848965</td>\n",
       "      <td>0.334828</td>\n",
       "      <td>1.039805</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.302568</td>\n",
       "      <td>-0.436714</td>\n",
       "      <td>-0.078385</td>\n",
       "      <td>0.646183</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>-0.682812</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.689587</td>\n",
       "      <td>0.518903</td>\n",
       "      <td>1.355551</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.753229</td>\n",
       "      <td>1.387486</td>\n",
       "      <td>1.039805</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2.419700</td>\n",
       "      <td>-2.266952</td>\n",
       "      <td>-1.043937</td>\n",
       "      <td>-1.481920</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>-0.682812</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2.419700</td>\n",
       "      <td>-2.266952</td>\n",
       "      <td>-1.049263</td>\n",
       "      <td>-1.481920</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>-0.682812</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>4.256207</td>\n",
       "      <td>1.246808</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>0.561757</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>0.655424</td>\n",
       "      <td>-0.342855</td>\n",
       "      <td>0.034605</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.370284</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.220295</td>\n",
       "      <td>-0.068620</td>\n",
       "      <td>0.646183</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.466021</td>\n",
       "      <td>-0.045921</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2919 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "0       0.067331    -0.202068 -0.217879     0.646183    -0.507284   \n",
       "1      -0.873616     0.501870 -0.072044    -0.063185     2.188279   \n",
       "2       0.067331    -0.061280  0.137197     0.646183    -0.507284   \n",
       "3       0.302568    -0.436714 -0.078385     0.646183    -0.507284   \n",
       "4       0.067331     0.689587  0.518903     1.355551    -0.507284   \n",
       "...          ...          ...       ...          ...          ...   \n",
       "2914    2.419700    -2.266952 -1.043937    -1.481920     1.289758   \n",
       "2915    2.419700    -2.266952 -1.049263    -1.481920    -0.507284   \n",
       "2916   -0.873616     4.256207  1.246808    -0.772552     1.289758   \n",
       "2917    0.655424    -0.342855  0.034605    -0.772552    -0.507284   \n",
       "2918    0.067331     0.220295 -0.068620     0.646183    -0.507284   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  ExterQual  ExterCond  BsmtQual  ...  \\\n",
       "0         0.896833    0.525202   1.039805  -0.230047  0.577094  ...   \n",
       "1        -0.395604   -0.572250  -0.683756  -0.230047  0.577094  ...   \n",
       "2         0.848965    0.334828   1.039805  -0.230047  0.577094  ...   \n",
       "3        -0.682812   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "4         0.753229    1.387486   1.039805  -0.230047  0.577094  ...   \n",
       "...            ...         ...        ...        ...       ...  ...   \n",
       "2914     -0.682812   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "2915     -0.682812   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "2916      0.561757   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "2917      0.370284   -0.572250  -0.683756  -0.230047  0.577094  ...   \n",
       "2918      0.466021   -0.045921  -0.683756  -0.230047  0.577094  ...   \n",
       "\n",
       "      MiscFeature  Neighborhood  PavedDrive  PoolQC  RoofMatl  RoofStyle  \\\n",
       "0               1             5           2       3         1          1   \n",
       "1               1            24           2       3         1          1   \n",
       "2               1             5           2       3         1          1   \n",
       "3               1             6           2       3         1          1   \n",
       "4               1            15           2       3         1          1   \n",
       "...           ...           ...         ...     ...       ...        ...   \n",
       "2914            1            10           2       3         1          1   \n",
       "2915            1            10           2       3         1          1   \n",
       "2916            1            11           2       3         1          1   \n",
       "2917            3            11           2       3         1          1   \n",
       "2918            1            11           2       3         1          1   \n",
       "\n",
       "      SaleCondition  SaleType  Street  Utilities  \n",
       "0                 4         8       1          0  \n",
       "1                 4         8       1          0  \n",
       "2                 4         8       1          0  \n",
       "3                 0         8       1          0  \n",
       "4                 4         8       1          0  \n",
       "...             ...       ...     ...        ...  \n",
       "2914              4         8       1          0  \n",
       "2915              0         8       1          0  \n",
       "2916              0         8       1          0  \n",
       "2917              4         8       1          0  \n",
       "2918              4         8       1          0  \n",
       "\n",
       "[2919 rows x 72 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_data.reset_index(drop=True, inplace=True)\n",
    "cat_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "final_dataset = pd.concat([num_data, cat_data], axis = 1)\n",
    "\n",
    "final_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 72)\n",
      "(1459, 72)\n"
     ]
    }
   ],
   "source": [
    "final_train = final_dataset.loc[:1459,]\n",
    "final_test = final_dataset.loc[1460:,]\n",
    "print(final_train.shape)\n",
    "print(final_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
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       "      <th>BsmtQual</th>\n",
       "      <th>...</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>PavedDrive</th>\n",
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       "      <th>RoofMatl</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>Street</th>\n",
       "      <th>Utilities</th>\n",
       "    </tr>\n",
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       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.873616</td>\n",
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       "      <td>-0.072044</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.302568</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.689587</td>\n",
       "      <td>0.518903</td>\n",
       "      <td>1.355551</td>\n",
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       "      <td>0.577094</td>\n",
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       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>0.736516</td>\n",
       "      <td>0.381311</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>0.391237</td>\n",
       "      <td>0.178812</td>\n",
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       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1458</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>-0.061280</td>\n",
       "      <td>-0.057207</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>0.391237</td>\n",
       "      <td>0.561757</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>0.267224</td>\n",
       "      <td>-0.029308</td>\n",
       "      <td>-0.772552</td>\n",
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       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "0       0.067331    -0.202068 -0.217879     0.646183    -0.507284   \n",
       "1      -0.873616     0.501870 -0.072044    -0.063185     2.188279   \n",
       "2       0.067331    -0.061280  0.137197     0.646183    -0.507284   \n",
       "3       0.302568    -0.436714 -0.078385     0.646183    -0.507284   \n",
       "4       0.067331     0.689587  0.518903     1.355551    -0.507284   \n",
       "...          ...          ...       ...          ...          ...   \n",
       "1455    0.067331    -0.342855 -0.285470    -0.063185    -0.507284   \n",
       "1456   -0.873616     0.736516  0.381311    -0.063185     0.391237   \n",
       "1457    0.302568    -0.155138 -0.142806     0.646183     3.086800   \n",
       "1458   -0.873616    -0.061280 -0.057207    -0.772552     0.391237   \n",
       "1459   -0.873616     0.267224 -0.029308    -0.772552     0.391237   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  ExterQual  ExterCond  BsmtQual  ...  \\\n",
       "0         0.896833    0.525202   1.039805  -0.230047  0.577094  ...   \n",
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       "...            ...         ...        ...        ...       ...  ...   \n",
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       "1456      0.178812    0.094060  -0.683756  -0.230047  0.577094  ...   \n",
       "1457      1.040437   -0.572250   2.763367   2.455979 -0.527521  ...   \n",
       "1458      0.561757   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "1459     -0.922153   -0.572250   1.039805  -0.230047 -0.527521  ...   \n",
       "\n",
       "      MiscFeature  Neighborhood  PavedDrive  PoolQC  RoofMatl  RoofStyle  \\\n",
       "0               1             5           2       3         1          1   \n",
       "1               1            24           2       3         1          1   \n",
       "2               1             5           2       3         1          1   \n",
       "3               1             6           2       3         1          1   \n",
       "4               1            15           2       3         1          1   \n",
       "...           ...           ...         ...     ...       ...        ...   \n",
       "1455            1             8           2       3         1          1   \n",
       "1456            1            14           2       3         1          1   \n",
       "1457            3             6           2       3         1          1   \n",
       "1458            1            12           2       3         1          3   \n",
       "1459            1             7           2       3         1          1   \n",
       "\n",
       "      SaleCondition  SaleType  Street  Utilities  \n",
       "0                 4         8       1          0  \n",
       "1                 4         8       1          0  \n",
       "2                 4         8       1          0  \n",
       "3                 0         8       1          0  \n",
       "4                 4         8       1          0  \n",
       "...             ...       ...     ...        ...  \n",
       "1455              4         8       1          0  \n",
       "1456              4         8       1          0  \n",
       "1457              4         8       1          0  \n",
       "1458              4         8       1          0  \n",
       "1459              4         8       1          0  \n",
       "\n",
       "[1460 rows x 72 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
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       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>0.548800</td>\n",
       "      <td>0.519791</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>0.391237</td>\n",
       "      <td>-1.257229</td>\n",
       "      <td>0.032468</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.220295</td>\n",
       "      <td>0.464374</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.657493</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.408012</td>\n",
       "      <td>-0.024109</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>0.391237</td>\n",
       "      <td>0.657493</td>\n",
       "      <td>-0.460265</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>1.478753</td>\n",
       "      <td>-1.234510</td>\n",
       "      <td>-0.654748</td>\n",
       "      <td>1.355551</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.370284</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>1.039805</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914</th>\n",
       "      <td>2.419700</td>\n",
       "      <td>-2.266952</td>\n",
       "      <td>-1.043937</td>\n",
       "      <td>-1.481920</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>-0.682812</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2.419700</td>\n",
       "      <td>-2.266952</td>\n",
       "      <td>-1.049263</td>\n",
       "      <td>-1.481920</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>-0.682812</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>4.256207</td>\n",
       "      <td>1.246808</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>0.561757</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>0.655424</td>\n",
       "      <td>-0.342855</td>\n",
       "      <td>0.034605</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.370284</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.220295</td>\n",
       "      <td>-0.068620</td>\n",
       "      <td>0.646183</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.466021</td>\n",
       "      <td>-0.045921</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "1460   -0.873616     0.501870  0.184371    -0.772552     0.391237   \n",
       "1461   -0.873616     0.548800  0.519791    -0.063185     0.391237   \n",
       "1462    0.067331     0.220295  0.464374    -0.772552    -0.507284   \n",
       "1463    0.067331     0.408012 -0.024109    -0.063185     0.391237   \n",
       "1464    1.478753    -1.234510 -0.654748     1.355551    -0.507284   \n",
       "...          ...          ...       ...          ...          ...   \n",
       "2914    2.419700    -2.266952 -1.043937    -1.481920     1.289758   \n",
       "2915    2.419700    -2.266952 -1.049263    -1.481920    -0.507284   \n",
       "2916   -0.873616     4.256207  1.246808    -0.772552     1.289758   \n",
       "2917    0.655424    -0.342855  0.034605    -0.772552    -0.507284   \n",
       "2918    0.067331     0.220295 -0.068620     0.646183    -0.507284   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  ExterQual  ExterCond  BsmtQual  ...  \\\n",
       "1460     -1.113625   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "1461     -1.257229    0.032468  -0.683756  -0.230047 -0.527521  ...   \n",
       "1462      0.657493   -0.572250  -0.683756  -0.230047  0.577094  ...   \n",
       "1463      0.657493   -0.460265  -0.683756  -0.230047 -0.527521  ...   \n",
       "1464      0.370284   -0.572250   1.039805  -0.230047  0.577094  ...   \n",
       "...            ...         ...        ...        ...       ...  ...   \n",
       "2914     -0.682812   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "2915     -0.682812   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "2916      0.561757   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "2917      0.370284   -0.572250  -0.683756  -0.230047  0.577094  ...   \n",
       "2918      0.466021   -0.045921  -0.683756  -0.230047  0.577094  ...   \n",
       "\n",
       "      MiscFeature  Neighborhood  PavedDrive  PoolQC  RoofMatl  RoofStyle  \\\n",
       "1460            1            12           2       3         1          1   \n",
       "1461            0            12           2       3         1          3   \n",
       "1462            1             8           2       3         1          1   \n",
       "1463            1             8           2       3         1          1   \n",
       "1464            1            22           2       3         1          1   \n",
       "...           ...           ...         ...     ...       ...        ...   \n",
       "2914            1            10           2       3         1          1   \n",
       "2915            1            10           2       3         1          1   \n",
       "2916            1            11           2       3         1          1   \n",
       "2917            3            11           2       3         1          1   \n",
       "2918            1            11           2       3         1          1   \n",
       "\n",
       "      SaleCondition  SaleType  Street  Utilities  \n",
       "1460              4         8       1          0  \n",
       "1461              4         8       1          0  \n",
       "1462              4         8       1          0  \n",
       "1463              4         8       1          0  \n",
       "1464              4         8       1          0  \n",
       "...             ...       ...     ...        ...  \n",
       "2914              4         8       1          0  \n",
       "2915              0         8       1          0  \n",
       "2916              0         8       1          0  \n",
       "2917              4         8       1          0  \n",
       "2918              4         8       1          0  \n",
       "\n",
       "[1459 rows x 72 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(final_train, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>...</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>Street</th>\n",
       "      <th>Utilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.853271</td>\n",
       "      <td>-0.063185</td>\n",
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       "      <td>-0.443472</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>-0.167905</td>\n",
       "      <td>0.454941</td>\n",
       "      <td>-0.085993</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>-1.640173</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>-0.167905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.399855</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>-1.405805</td>\n",
       "      <td>-1.640173</td>\n",
       "      <td>0.329228</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.642658</td>\n",
       "      <td>-0.020685</td>\n",
       "      <td>0.646183</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>-0.347736</td>\n",
       "      <td>1.101924</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2.419700</td>\n",
       "      <td>-2.266952</td>\n",
       "      <td>-1.076401</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>-0.634944</td>\n",
       "      <td>1.561062</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>326</th>\n",
       "      <td>1.478753</td>\n",
       "      <td>-1.750731</td>\n",
       "      <td>0.085965</td>\n",
       "      <td>1.355551</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>0.418153</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>1.039805</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>0.577094</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.873616</td>\n",
       "      <td>1.675100</td>\n",
       "      <td>0.667401</td>\n",
       "      <td>2.774287</td>\n",
       "      <td>-0.507284</td>\n",
       "      <td>1.136173</td>\n",
       "      <td>0.547599</td>\n",
       "      <td>2.763367</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>1.681709</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>-0.167905</td>\n",
       "      <td>-0.436714</td>\n",
       "      <td>-0.209002</td>\n",
       "      <td>-0.063185</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>-1.640173</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>2.455979</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>-0.638379</td>\n",
       "      <td>-0.906006</td>\n",
       "      <td>-0.613534</td>\n",
       "      <td>-1.481920</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>-1.640173</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-2.407318</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.420187</td>\n",
       "      <td>-0.436714</td>\n",
       "      <td>-0.376395</td>\n",
       "      <td>-0.772552</td>\n",
       "      <td>1.289758</td>\n",
       "      <td>0.561757</td>\n",
       "      <td>-0.572250</td>\n",
       "      <td>-0.683756</td>\n",
       "      <td>-0.230047</td>\n",
       "      <td>-0.527521</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "529    -0.873616     0.000000  2.853271    -0.063185    -2.304326   \n",
       "491    -0.167905     0.454941 -0.085993    -0.063185     1.289758   \n",
       "459    -0.167905     0.000000 -0.399855    -0.772552    -1.405805   \n",
       "279     0.067331     0.642658 -0.020685     0.646183    -0.507284   \n",
       "655     2.419700    -2.266952 -1.076401    -0.063185    -0.507284   \n",
       "...          ...          ...       ...          ...          ...   \n",
       "326     1.478753    -1.750731  0.085965     1.355551    -0.507284   \n",
       "440    -0.873616     1.675100  0.667401     2.774287    -0.507284   \n",
       "1387   -0.167905    -0.436714 -0.209002    -0.063185     1.289758   \n",
       "1323   -0.638379    -0.906006 -0.613534    -1.481920     1.289758   \n",
       "61      0.420187    -0.436714 -0.376395    -0.772552     1.289758   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  ExterQual  ExterCond  BsmtQual  ...  \\\n",
       "529      -0.443472    0.000000   1.039805  -0.230047 -0.527521  ...   \n",
       "491      -1.640173   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "459      -1.640173    0.329228  -0.683756  -0.230047 -0.527521  ...   \n",
       "279      -0.347736    1.101924  -0.683756  -0.230047  0.577094  ...   \n",
       "655      -0.634944    1.561062  -0.683756  -0.230047 -0.527521  ...   \n",
       "...            ...         ...        ...        ...       ...  ...   \n",
       "326       0.418153   -0.572250   1.039805  -0.230047  0.577094  ...   \n",
       "440       1.136173    0.547599   2.763367  -0.230047  1.681709  ...   \n",
       "1387     -1.640173   -0.572250  -0.683756   2.455979 -0.527521  ...   \n",
       "1323     -1.640173   -0.572250  -2.407318  -0.230047 -0.527521  ...   \n",
       "61        0.561757   -0.572250  -0.683756  -0.230047 -0.527521  ...   \n",
       "\n",
       "      MiscFeature  Neighborhood  PavedDrive  PoolQC  RoofMatl  RoofStyle  \\\n",
       "529             1             6           2       3         1          3   \n",
       "491             1            12           2       3         1          1   \n",
       "459             1             3           2       3         1          1   \n",
       "279             1             4           2       3         1          3   \n",
       "655             1             2           2       3         1          1   \n",
       "...           ...           ...         ...     ...       ...        ...   \n",
       "326             1            24           2       3         1          1   \n",
       "440             1            16           2       3         1          3   \n",
       "1387            1            17           2       3         1          1   \n",
       "1323            1             3           2       3         1          3   \n",
       "61              1             9           0       3         1          1   \n",
       "\n",
       "      SaleCondition  SaleType  Street  Utilities  \n",
       "529               2         8       1          0  \n",
       "491               4         8       1          0  \n",
       "459               4         8       1          0  \n",
       "279               4         8       1          0  \n",
       "655               3         8       1          0  \n",
       "...             ...       ...     ...        ...  \n",
       "326               4         2       1          0  \n",
       "440               4         8       1          0  \n",
       "1387              3         1       1          0  \n",
       "1323              4         8       1          0  \n",
       "61                4         8       1          0  \n",
       "\n",
       "[292 rows x 72 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def clf_perf(clf, model_name):\\n    print(model_name)\\n    print(\\'Best Score: \\',clf.best_score_)\\n    print(\\'Best Params: \\',clf.best_params_ )\\n\\nfrom sklearn.model_selection import GridSearchCV\\nimport xgboost as xgb\\n    \\nxgbr = xgb.XGBRegressor()\\n\\nparam_grid =  {\\'eta\\': [0.2,0.3,0.4,0.5],\\n               \\'gamma\\':[0,1,2,3,4],\\n               \\'max_depth\\':[5,6,8,10,12],\\n               \\'min_child_weight\\': [1,3,5,7],\\n               \\'lambda\\':[1,3,5,7],\\n               \\'alpha\\':[0,1,3,5,7],\\n               \"colasample_bytree\" : [0.2, 0.3, 0.4, 0.5]\\n              }\\n                                  \\nclf_rf = GridSearchCV(xgbr, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\\nbest_clf_rf = clf_rf.fit(X_train,y_train)\\nclf_perf(best_clf_rf,\\'XGB Regressor\\')'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clf_perf(clf, model_name):\n",
    "    print(model_name)\n",
    "    print('Best Score: ',clf.best_score_)\n",
    "    print('Best Params: ',clf.best_params_ )\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import xgboost as xgb\n",
    "    \n",
    "xgbr = xgb.XGBRegressor()\n",
    "\n",
    "param_grid =  {'eta': [0.2,0.3,0.4,0.5],\n",
    "               'gamma':[0,1,2,3,4],\n",
    "               'max_depth':[5,6,8,10,12],\n",
    "               'min_child_weight': [1,3,5,7],\n",
    "               'lambda':[1,3,5,7],\n",
    "               'alpha':[0,1,3,5,7],\n",
    "               \"colasample_bytree\" : [0.2, 0.3, 0.4, 0.5]\n",
    "              }\n",
    "                                  \n",
    "clf_rf = GridSearchCV(xgbr, param_grid = param_grid, cv = 3, verbose = True, n_jobs = -1)\n",
    "best_clf_rf = clf_rf.fit(X_train,y_train)\n",
    "clf_perf(best_clf_rf,'XGB Regressor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE:  0.007208369317842471\n",
      "Test RMSE:  0.1364005413616177\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "xgbr = xgb.XGBRegressor()\n",
    "\n",
    "xgbr.fit(X_train,y_train)\n",
    "\n",
    "xgbr_train_pred = xgbr.predict(X_train)\n",
    "xgbr_test_pred = xgbr.predict(X_test)\n",
    "\n",
    "xgbr_train_rmse = mean_squared_error(y_train, xgbr_train_pred, squared=False)\n",
    "xgbr_test_rmse = mean_squared_error(y_test, xgbr_test_pred, squared=False)\n",
    "\n",
    "print(\"Train RMSE: \", xgbr_train_rmse)\n",
    "print(\"Test RMSE: \", xgbr_test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.040124\n",
      "0:\tlearn: 0.3916557\ttotal: 148ms\tremaining: 2m 27s\n",
      "1:\tlearn: 0.3817788\ttotal: 155ms\tremaining: 1m 17s\n",
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      "999:\tlearn: 0.0325162\ttotal: 3.07s\tremaining: 0us\n",
      "Train RMSE:  0.03251624302073899\n",
      "Test RMSE:  0.1284663104702012\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "\n",
    "cat = CatBoostRegressor()\n",
    "\n",
    "cat.fit(X_train,y_train)\n",
    "\n",
    "cat_train_pred = cat.predict(X_train)\n",
    "cat_test_pred = cat.predict(X_test)\n",
    "\n",
    "cat_train_rmse = mean_squared_error(y_train, cat_train_pred, squared=False)\n",
    "cat_test_rmse = mean_squared_error(y_test, cat_test_pred, squared=False)\n",
    "\n",
    "print(\"Train RMSE: \", cat_train_rmse)\n",
    "print(\"Test RMSE: \", cat_test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE:  0.1250989650664144\n",
      "Test RMSE:  0.2024919439720385\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "las = LassoCV()\n",
    "\n",
    "las.fit(X_train,y_train)\n",
    "\n",
    "las_train_pred = las.predict(X_train)\n",
    "las_test_pred = las.predict(X_test)\n",
    "\n",
    "las_train_rmse = mean_squared_error(y_train, las_train_pred, squared=False)\n",
    "las_test_rmse = mean_squared_error(y_test, las_test_pred, squared=False)\n",
    "\n",
    "print(\"Train RMSE: \", las_train_rmse)\n",
    "print(\"Test RMSE: \", las_test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1461</td>\n",
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       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>167821.281250</td>\n",
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       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>170508.468750</td>\n",
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       "      <th>3</th>\n",
       "      <td>1464</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>73156.031250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>138998.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
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       "      <td>112006.398438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>238940.140625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  117301.351562\n",
       "1     1462  167821.281250\n",
       "2     1463  170508.468750\n",
       "3     1464  187207.109375\n",
       "4     1465  180561.125000\n",
       "...    ...            ...\n",
       "1454  2915   72709.765625\n",
       "1455  2916   73156.031250\n",
       "1456  2917  138998.625000\n",
       "1457  2918  112006.398438\n",
       "1458  2919  238940.140625\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbr_saleprice = xgbr.predict(final_test)\n",
    "\n",
    "xgbr_saleprice_df = pd.DataFrame(test[['Id']])\n",
    "xgbr_saleprice_df['SalePrice'] = np.exp(xgbr_saleprice)\n",
    "xgbr_saleprice_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgbr_saleprice_df.to_csv(\"xgb_submission.csv\", index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Id</th>\n",
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       "      <td>194388.946250</td>\n",
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       "      <td>82396.262034</td>\n",
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       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>82881.055813</td>\n",
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       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>168781.832318</td>\n",
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       "    <tr>\n",
       "      <th>1457</th>\n",
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       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  123260.849106\n",
       "1     1462  164991.369885\n",
       "2     1463  190950.234679\n",
       "3     1464  194388.946250\n",
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       "...    ...            ...\n",
       "1454  2915   82396.262034\n",
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       "1456  2917  168781.832318\n",
       "1457  2918  108714.097314\n",
       "1458  2919  226789.313399\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_saleprice = cat.predict(final_test)\n",
    "\n",
    "cat_saleprice_df = pd.DataFrame(test[['Id']])\n",
    "cat_saleprice_df['SalePrice'] = np.exp(cat_saleprice)\n",
    "cat_saleprice_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_saleprice_df.to_csv(\"cat_submission.csv\", index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        Id      SalePrice\n",
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       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las_saleprice = las.predict(final_test)\n",
    "\n",
    "las_saleprice_df = pd.DataFrame(test[['Id']])\n",
    "las_saleprice_df['SalePrice'] = np.exp(las_saleprice)\n",
    "las_saleprice_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  120016.470893\n",
       "1     1462  165192.664779\n",
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       "1458  2919  231962.979610\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_submission = pd.DataFrame()\n",
    "avg_submission['Id'] = test['Id']\n",
    "avg_submission['SalePrice'] = (cat_saleprice_df['SalePrice'] + xgbr_saleprice_df['SalePrice'] + las_saleprice_df['SalePrice'])/3\n",
    "avg_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_submission.to_csv(\"submission2.csv\", index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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