Kaggle house rate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aware-commonwealth",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "shaped-laptop",
   "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>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",
       "      <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>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14115</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>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10084</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>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10382</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>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</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>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>190</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7420</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>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 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",
       "5   6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6   7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8   9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9  10         190       RL         50.0     7420   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",
       "5         Lvl    AllPub  ...        0    NaN  MnPrv        Shed     700   \n",
       "6         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "7         Lvl    AllPub  ...        0    NaN    NaN        Shed     350   \n",
       "8         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "9         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",
       "5     10   2009        WD         Normal     143000  \n",
       "6      8   2007        WD         Normal     307000  \n",
       "7     11   2009        WD         Normal     200000  \n",
       "8      4   2008        WD        Abnorml     129900  \n",
       "9      1   2008        WD         Normal     118000  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../Data/Hakerearth House rate/train.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "blind-laundry",
   "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>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</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>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</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>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</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>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "4       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"../Data/Hakerearth House rate/test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "frank-story",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "significant-stevens",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1459 non-null   int64  \n",
      " 1   MSSubClass     1459 non-null   int64  \n",
      " 2   MSZoning       1455 non-null   object \n",
      " 3   LotFrontage    1232 non-null   float64\n",
      " 4   LotArea        1459 non-null   int64  \n",
      " 5   Street         1459 non-null   object \n",
      " 6   Alley          107 non-null    object \n",
      " 7   LotShape       1459 non-null   object \n",
      " 8   LandContour    1459 non-null   object \n",
      " 9   Utilities      1457 non-null   object \n",
      " 10  LotConfig      1459 non-null   object \n",
      " 11  LandSlope      1459 non-null   object \n",
      " 12  Neighborhood   1459 non-null   object \n",
      " 13  Condition1     1459 non-null   object \n",
      " 14  Condition2     1459 non-null   object \n",
      " 15  BldgType       1459 non-null   object \n",
      " 16  HouseStyle     1459 non-null   object \n",
      " 17  OverallQual    1459 non-null   int64  \n",
      " 18  OverallCond    1459 non-null   int64  \n",
      " 19  YearBuilt      1459 non-null   int64  \n",
      " 20  YearRemodAdd   1459 non-null   int64  \n",
      " 21  RoofStyle      1459 non-null   object \n",
      " 22  RoofMatl       1459 non-null   object \n",
      " 23  Exterior1st    1458 non-null   object \n",
      " 24  Exterior2nd    1458 non-null   object \n",
      " 25  MasVnrType     1443 non-null   object \n",
      " 26  MasVnrArea     1444 non-null   float64\n",
      " 27  ExterQual      1459 non-null   object \n",
      " 28  ExterCond      1459 non-null   object \n",
      " 29  Foundation     1459 non-null   object \n",
      " 30  BsmtQual       1415 non-null   object \n",
      " 31  BsmtCond       1414 non-null   object \n",
      " 32  BsmtExposure   1415 non-null   object \n",
      " 33  BsmtFinType1   1417 non-null   object \n",
      " 34  BsmtFinSF1     1458 non-null   float64\n",
      " 35  BsmtFinType2   1417 non-null   object \n",
      " 36  BsmtFinSF2     1458 non-null   float64\n",
      " 37  BsmtUnfSF      1458 non-null   float64\n",
      " 38  TotalBsmtSF    1458 non-null   float64\n",
      " 39  Heating        1459 non-null   object \n",
      " 40  HeatingQC      1459 non-null   object \n",
      " 41  CentralAir     1459 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1459 non-null   int64  \n",
      " 44  2ndFlrSF       1459 non-null   int64  \n",
      " 45  LowQualFinSF   1459 non-null   int64  \n",
      " 46  GrLivArea      1459 non-null   int64  \n",
      " 47  BsmtFullBath   1457 non-null   float64\n",
      " 48  BsmtHalfBath   1457 non-null   float64\n",
      " 49  FullBath       1459 non-null   int64  \n",
      " 50  HalfBath       1459 non-null   int64  \n",
      " 51  BedroomAbvGr   1459 non-null   int64  \n",
      " 52  KitchenAbvGr   1459 non-null   int64  \n",
      " 53  KitchenQual    1458 non-null   object \n",
      " 54  TotRmsAbvGrd   1459 non-null   int64  \n",
      " 55  Functional     1457 non-null   object \n",
      " 56  Fireplaces     1459 non-null   int64  \n",
      " 57  FireplaceQu    729 non-null    object \n",
      " 58  GarageType     1383 non-null   object \n",
      " 59  GarageYrBlt    1381 non-null   float64\n",
      " 60  GarageFinish   1381 non-null   object \n",
      " 61  GarageCars     1458 non-null   float64\n",
      " 62  GarageArea     1458 non-null   float64\n",
      " 63  GarageQual     1381 non-null   object \n",
      " 64  GarageCond     1381 non-null   object \n",
      " 65  PavedDrive     1459 non-null   object \n",
      " 66  WoodDeckSF     1459 non-null   int64  \n",
      " 67  OpenPorchSF    1459 non-null   int64  \n",
      " 68  EnclosedPorch  1459 non-null   int64  \n",
      " 69  3SsnPorch      1459 non-null   int64  \n",
      " 70  ScreenPorch    1459 non-null   int64  \n",
      " 71  PoolArea       1459 non-null   int64  \n",
      " 72  PoolQC         3 non-null      object \n",
      " 73  Fence          290 non-null    object \n",
      " 74  MiscFeature    51 non-null     object \n",
      " 75  MiscVal        1459 non-null   int64  \n",
      " 76  MoSold         1459 non-null   int64  \n",
      " 77  YrSold         1459 non-null   int64  \n",
      " 78  SaleType       1458 non-null   object \n",
      " 79  SaleCondition  1459 non-null   object \n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 912.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "multiple-cassette",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 259 float64\n",
      "Alley 1369 object\n",
      "MasVnrType 8 object\n",
      "MasVnrArea 8 float64\n",
      "BsmtQual 37 object\n",
      "BsmtCond 37 object\n",
      "BsmtExposure 38 object\n",
      "BsmtFinType1 37 object\n",
      "BsmtFinType2 38 object\n",
      "Electrical 1 object\n",
      "FireplaceQu 690 object\n",
      "GarageType 81 object\n",
      "GarageYrBlt 81 float64\n",
      "GarageFinish 81 object\n",
      "GarageQual 81 object\n",
      "GarageCond 81 object\n",
      "PoolQC 1453 object\n",
      "Fence 1179 object\n",
      "MiscFeature 1406 object\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        print(i , df[i].isna().sum(), df[i].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "stable-individual",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning 4 object\n",
      "LotFrontage 227 float64\n",
      "Alley 1352 object\n",
      "Utilities 2 object\n",
      "Exterior1st 1 object\n",
      "Exterior2nd 1 object\n",
      "MasVnrType 16 object\n",
      "MasVnrArea 15 float64\n",
      "BsmtQual 44 object\n",
      "BsmtCond 45 object\n",
      "BsmtExposure 44 object\n",
      "BsmtFinType1 42 object\n",
      "BsmtFinSF1 1 float64\n",
      "BsmtFinType2 42 object\n",
      "BsmtFinSF2 1 float64\n",
      "BsmtUnfSF 1 float64\n",
      "TotalBsmtSF 1 float64\n",
      "BsmtFullBath 2 float64\n",
      "BsmtHalfBath 2 float64\n",
      "KitchenQual 1 object\n",
      "Functional 2 object\n",
      "FireplaceQu 730 object\n",
      "GarageType 76 object\n",
      "GarageYrBlt 78 float64\n",
      "GarageFinish 78 object\n",
      "GarageCars 1 float64\n",
      "GarageArea 1 float64\n",
      "GarageQual 78 object\n",
      "GarageCond 78 object\n",
      "PoolQC 1456 object\n",
      "Fence 1169 object\n",
      "MiscFeature 1408 object\n",
      "SaleType 1 object\n"
     ]
    }
   ],
   "source": [
    "for i in test.columns:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i , test[i].isna().sum(), test[i].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "regular-leone",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    if len(df[i].unique()) == 1460:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fiscal-morris",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Id\"] , axis = 1 , inplace = True)\n",
    "ids = test[\"Id\"]\n",
    "test.drop([\"Id\"] , axis = 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "apparent-atlas",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
       "       'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_df = df.select_dtypes(include=\"object\").columns\n",
    "cats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "juvenile-kinase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints_df = df.select_dtypes(exclude = \"object\").columns\n",
    "ints_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "hybrid-server",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns) == len(ints_df) + len(cats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "monetary-objective",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints_test = test.select_dtypes(exclude = \"object\").columns\n",
    "ints_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "powerful-lloyd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
       "       'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_test = test.select_dtypes(include=\"object\").columns\n",
    "cats_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "impaired-incentive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alley 1369\n",
      "MasVnrType 8\n",
      "BsmtQual 37\n",
      "BsmtCond 37\n",
      "BsmtExposure 38\n",
      "BsmtFinType1 37\n",
      "BsmtFinType2 38\n",
      "Electrical 1\n",
      "FireplaceQu 690\n",
      "GarageType 81\n",
      "GarageFinish 81\n",
      "GarageQual 81\n",
      "GarageCond 81\n",
      "PoolQC 1453\n",
      "Fence 1179\n",
      "MiscFeature 1406\n"
     ]
    }
   ],
   "source": [
    "for i in cats_df:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        print(i , df[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "instrumental-prototype",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning 4\n",
      "Alley 1352\n",
      "Utilities 2\n",
      "Exterior1st 1\n",
      "Exterior2nd 1\n",
      "MasVnrType 16\n",
      "BsmtQual 44\n",
      "BsmtCond 45\n",
      "BsmtExposure 44\n",
      "BsmtFinType1 42\n",
      "BsmtFinType2 42\n",
      "KitchenQual 1\n",
      "Functional 2\n",
      "FireplaceQu 730\n",
      "GarageType 76\n",
      "GarageFinish 78\n",
      "GarageQual 78\n",
      "GarageCond 78\n",
      "PoolQC 1456\n",
      "Fence 1169\n",
      "MiscFeature 1408\n",
      "SaleType 1\n"
     ]
    }
   ],
   "source": [
    "for i in cats_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i , test[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "driven-spotlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 1459)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df) , len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fluid-premises",
   "metadata": {
    "scrolled": true
   },
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ex</th>\n",
       "      <td>337712.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fa</th>\n",
       "      <td>167298.484848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gd</th>\n",
       "      <td>226351.415789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Po</th>\n",
       "      <td>129764.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA</th>\n",
       "      <td>205723.488818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SalePrice\n",
       "FireplaceQu               \n",
       "Ex           337712.500000\n",
       "Fa           167298.484848\n",
       "Gd           226351.415789\n",
       "Po           129764.150000\n",
       "TA           205723.488818"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"FireplaceQu\" , \"SalePrice\"]].groupby([\"FireplaceQu\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "behavioral-lease",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"MiscFeature\" , \"PoolQC\" , \"Alley\" , \"Fence\"] , axis = 1 , inplace = True)\n",
    "test.drop([\"MiscFeature\" , \"PoolQC\" , \"Alley\" , \"Fence\"] , axis = 1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "positive-least",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_df = df.select_dtypes(include=\"object\").columns\n",
    "cats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "through-pittsburgh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_test = test.select_dtypes(include=\"object\").columns\n",
    "cats_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "introductory-token",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MasVnrType\n",
      "\n",
      "\n",
      "\n",
      "['BrkFace' 'None' 'Stone' 'BrkCmn' nan]\n",
      "\n",
      "\n",
      "\n",
      "None       864\n",
      "BrkFace    445\n",
      "Stone      128\n",
      "BrkCmn      15\n",
      "Name: MasVnrType, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtQual\n",
      "\n",
      "\n",
      "\n",
      "['Gd' 'TA' 'Ex' nan 'Fa']\n",
      "\n",
      "\n",
      "\n",
      "TA    649\n",
      "Gd    618\n",
      "Ex    121\n",
      "Fa     35\n",
      "Name: BsmtQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtCond\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Gd' nan 'Fa' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "TA    1311\n",
      "Gd      65\n",
      "Fa      45\n",
      "Po       2\n",
      "Name: BsmtCond, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtExposure\n",
      "\n",
      "\n",
      "\n",
      "['No' 'Gd' 'Mn' 'Av' nan]\n",
      "\n",
      "\n",
      "\n",
      "No    953\n",
      "Av    221\n",
      "Gd    134\n",
      "Mn    114\n",
      "Name: BsmtExposure, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType1\n",
      "\n",
      "\n",
      "\n",
      "['GLQ' 'ALQ' 'Unf' 'Rec' 'BLQ' nan 'LwQ']\n",
      "\n",
      "\n",
      "\n",
      "Unf    430\n",
      "GLQ    418\n",
      "ALQ    220\n",
      "BLQ    148\n",
      "Rec    133\n",
      "LwQ     74\n",
      "Name: BsmtFinType1, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType2\n",
      "\n",
      "\n",
      "\n",
      "['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ']\n",
      "\n",
      "\n",
      "\n",
      "Unf    1256\n",
      "Rec      54\n",
      "LwQ      46\n",
      "BLQ      33\n",
      "ALQ      19\n",
      "GLQ      14\n",
      "Name: BsmtFinType2, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Electrical\n",
      "\n",
      "\n",
      "\n",
      "['SBrkr' 'FuseF' 'FuseA' 'FuseP' 'Mix' nan]\n",
      "\n",
      "\n",
      "\n",
      "SBrkr    1334\n",
      "FuseA      94\n",
      "FuseF      27\n",
      "FuseP       3\n",
      "Mix         1\n",
      "Name: Electrical, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "FireplaceQu\n",
      "\n",
      "\n",
      "\n",
      "[nan 'TA' 'Gd' 'Fa' 'Ex' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "Gd    380\n",
      "TA    313\n",
      "Fa     33\n",
      "Ex     24\n",
      "Po     20\n",
      "Name: FireplaceQu, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageType\n",
      "\n",
      "\n",
      "\n",
      "['Attchd' 'Detchd' 'BuiltIn' 'CarPort' nan 'Basment' '2Types']\n",
      "\n",
      "\n",
      "\n",
      "Attchd     870\n",
      "Detchd     387\n",
      "BuiltIn     88\n",
      "Basment     19\n",
      "CarPort      9\n",
      "2Types       6\n",
      "Name: GarageType, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageFinish\n",
      "\n",
      "\n",
      "\n",
      "['RFn' 'Unf' 'Fin' nan]\n",
      "\n",
      "\n",
      "\n",
      "Unf    605\n",
      "RFn    422\n",
      "Fin    352\n",
      "Name: GarageFinish, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageQual\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Fa' 'Gd' nan 'Ex' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "TA    1311\n",
      "Fa      48\n",
      "Gd      14\n",
      "Po       3\n",
      "Ex       3\n",
      "Name: GarageQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageCond\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Fa' nan 'Gd' 'Po' 'Ex']\n",
      "\n",
      "\n",
      "\n",
      "TA    1326\n",
      "Fa      35\n",
      "Gd       9\n",
      "Po       7\n",
      "Ex       2\n",
      "Name: GarageCond, dtype: int64\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in cats_df:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        print(i)\n",
    "        print(\"\\n\\n\")\n",
    "        print(df[i].unique())\n",
    "        print(\"\\n\\n\")\n",
    "        print(df[i].value_counts())\n",
    "        print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "inner-wells",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning\n",
      "\n",
      "\n",
      "\n",
      "['RH' 'RL' 'RM' 'FV' 'C (all)' nan]\n",
      "\n",
      "\n",
      "\n",
      "RL         1114\n",
      "RM          242\n",
      "FV           74\n",
      "C (all)      15\n",
      "RH           10\n",
      "Name: MSZoning, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Utilities\n",
      "\n",
      "\n",
      "\n",
      "['AllPub' nan]\n",
      "\n",
      "\n",
      "\n",
      "AllPub    1457\n",
      "Name: Utilities, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Exterior1st\n",
      "\n",
      "\n",
      "\n",
      "['VinylSd' 'Wd Sdng' 'HdBoard' 'Plywood' 'MetalSd' 'CemntBd' 'WdShing'\n",
      " 'BrkFace' 'AsbShng' 'BrkComm' 'Stucco' 'AsphShn' nan 'CBlock']\n",
      "\n",
      "\n",
      "\n",
      "VinylSd    510\n",
      "MetalSd    230\n",
      "HdBoard    220\n",
      "Wd Sdng    205\n",
      "Plywood    113\n",
      "CemntBd     65\n",
      "BrkFace     37\n",
      "WdShing     30\n",
      "AsbShng     24\n",
      "Stucco      18\n",
      "BrkComm      4\n",
      "AsphShn      1\n",
      "CBlock       1\n",
      "Name: Exterior1st, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Exterior2nd\n",
      "\n",
      "\n",
      "\n",
      "['VinylSd' 'Wd Sdng' 'HdBoard' 'Plywood' 'MetalSd' 'Brk Cmn' 'CmentBd'\n",
      " 'ImStucc' 'Wd Shng' 'AsbShng' 'Stucco' 'CBlock' 'BrkFace' 'AsphShn' nan\n",
      " 'Stone']\n",
      "\n",
      "\n",
      "\n",
      "VinylSd    510\n",
      "MetalSd    233\n",
      "HdBoard    199\n",
      "Wd Sdng    194\n",
      "Plywood    128\n",
      "CmentBd     66\n",
      "Wd Shng     43\n",
      "BrkFace     22\n",
      "Stucco      21\n",
      "AsbShng     18\n",
      "Brk Cmn     15\n",
      "ImStucc      5\n",
      "CBlock       2\n",
      "Stone        1\n",
      "AsphShn      1\n",
      "Name: Exterior2nd, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "MasVnrType\n",
      "\n",
      "\n",
      "\n",
      "['None' 'BrkFace' 'Stone' 'BrkCmn' nan]\n",
      "\n",
      "\n",
      "\n",
      "None       878\n",
      "BrkFace    434\n",
      "Stone      121\n",
      "BrkCmn      10\n",
      "Name: MasVnrType, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtQual\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Gd' 'Ex' 'Fa' nan]\n",
      "\n",
      "\n",
      "\n",
      "TA    634\n",
      "Gd    591\n",
      "Ex    137\n",
      "Fa     53\n",
      "Name: BsmtQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtCond\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Po' 'Fa' 'Gd' nan]\n",
      "\n",
      "\n",
      "\n",
      "TA    1295\n",
      "Fa      59\n",
      "Gd      57\n",
      "Po       3\n",
      "Name: BsmtCond, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtExposure\n",
      "\n",
      "\n",
      "\n",
      "['No' 'Gd' 'Mn' 'Av' nan]\n",
      "\n",
      "\n",
      "\n",
      "No    951\n",
      "Av    197\n",
      "Gd    142\n",
      "Mn    125\n",
      "Name: BsmtExposure, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType1\n",
      "\n",
      "\n",
      "\n",
      "['Rec' 'ALQ' 'GLQ' 'Unf' 'BLQ' 'LwQ' nan]\n",
      "\n",
      "\n",
      "\n",
      "GLQ    431\n",
      "Unf    421\n",
      "ALQ    209\n",
      "Rec    155\n",
      "BLQ    121\n",
      "LwQ     80\n",
      "Name: BsmtFinType1, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType2\n",
      "\n",
      "\n",
      "\n",
      "['LwQ' 'Unf' 'Rec' 'BLQ' 'GLQ' 'ALQ' nan]\n",
      "\n",
      "\n",
      "\n",
      "Unf    1237\n",
      "Rec      51\n",
      "LwQ      41\n",
      "BLQ      35\n",
      "ALQ      33\n",
      "GLQ      20\n",
      "Name: BsmtFinType2, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "KitchenQual\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Gd' 'Ex' 'Fa' nan]\n",
      "\n",
      "\n",
      "\n",
      "TA    757\n",
      "Gd    565\n",
      "Ex    105\n",
      "Fa     31\n",
      "Name: KitchenQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Functional\n",
      "\n",
      "\n",
      "\n",
      "['Typ' 'Min2' 'Min1' 'Mod' 'Maj1' 'Sev' 'Maj2' nan]\n",
      "\n",
      "\n",
      "\n",
      "Typ     1357\n",
      "Min2      36\n",
      "Min1      34\n",
      "Mod       20\n",
      "Maj1       5\n",
      "Maj2       4\n",
      "Sev        1\n",
      "Name: Functional, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "FireplaceQu\n",
      "\n",
      "\n",
      "\n",
      "[nan 'TA' 'Gd' 'Po' 'Fa' 'Ex']\n",
      "\n",
      "\n",
      "\n",
      "Gd    364\n",
      "TA    279\n",
      "Fa     41\n",
      "Po     26\n",
      "Ex     19\n",
      "Name: FireplaceQu, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageType\n",
      "\n",
      "\n",
      "\n",
      "['Attchd' 'Detchd' 'BuiltIn' nan 'Basment' '2Types' 'CarPort']\n",
      "\n",
      "\n",
      "\n",
      "Attchd     853\n",
      "Detchd     392\n",
      "BuiltIn     98\n",
      "2Types      17\n",
      "Basment     17\n",
      "CarPort      6\n",
      "Name: GarageType, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageFinish\n",
      "\n",
      "\n",
      "\n",
      "['Unf' 'Fin' 'RFn' nan]\n",
      "\n",
      "\n",
      "\n",
      "Unf    625\n",
      "RFn    389\n",
      "Fin    367\n",
      "Name: GarageFinish, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageQual\n",
      "\n",
      "\n",
      "\n",
      "['TA' nan 'Fa' 'Gd' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "TA    1293\n",
      "Fa      76\n",
      "Gd      10\n",
      "Po       2\n",
      "Name: GarageQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageCond\n",
      "\n",
      "\n",
      "\n",
      "['TA' nan 'Fa' 'Gd' 'Po' 'Ex']\n",
      "\n",
      "\n",
      "\n",
      "TA    1328\n",
      "Fa      39\n",
      "Po       7\n",
      "Gd       6\n",
      "Ex       1\n",
      "Name: GarageCond, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "SaleType\n",
      "\n",
      "\n",
      "\n",
      "['WD' 'COD' 'New' 'ConLD' 'Oth' 'Con' 'ConLw' 'ConLI' 'CWD' nan]\n",
      "\n",
      "\n",
      "\n",
      "WD       1258\n",
      "New       117\n",
      "COD        44\n",
      "ConLD      17\n",
      "CWD         8\n",
      "ConLI       4\n",
      "Oth         4\n",
      "ConLw       3\n",
      "Con         3\n",
      "Name: SaleType, dtype: int64\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in cats_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i)\n",
    "        print(\"\\n\\n\")\n",
    "        print(test[i].unique())\n",
    "        print(\"\\n\\n\")\n",
    "        print(test[i].value_counts())\n",
    "        print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "corresponding-diameter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning\n",
      "\n",
      "\n",
      "\n",
      "['RL' 'RM' 'C (all)' 'FV' 'RH']\n",
      "\n",
      "\n",
      "\n",
      "RL         1151\n",
      "RM          218\n",
      "FV           65\n",
      "RH           16\n",
      "C (all)      10\n",
      "Name: MSZoning, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Utilities\n",
      "\n",
      "\n",
      "\n",
      "['AllPub' 'NoSeWa']\n",
      "\n",
      "\n",
      "\n",
      "AllPub    1459\n",
      "NoSeWa       1\n",
      "Name: Utilities, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Exterior1st\n",
      "\n",
      "\n",
      "\n",
      "['VinylSd' 'MetalSd' 'Wd Sdng' 'HdBoard' 'BrkFace' 'WdShing' 'CemntBd'\n",
      " 'Plywood' 'AsbShng' 'Stucco' 'BrkComm' 'AsphShn' 'Stone' 'ImStucc'\n",
      " 'CBlock']\n",
      "\n",
      "\n",
      "\n",
      "VinylSd    515\n",
      "HdBoard    222\n",
      "MetalSd    220\n",
      "Wd Sdng    206\n",
      "Plywood    108\n",
      "CemntBd     61\n",
      "BrkFace     50\n",
      "WdShing     26\n",
      "Stucco      25\n",
      "AsbShng     20\n",
      "Stone        2\n",
      "BrkComm      2\n",
      "AsphShn      1\n",
      "ImStucc      1\n",
      "CBlock       1\n",
      "Name: Exterior1st, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Exterior2nd\n",
      "\n",
      "\n",
      "\n",
      "['VinylSd' 'MetalSd' 'Wd Shng' 'HdBoard' 'Plywood' 'Wd Sdng' 'CmentBd'\n",
      " 'BrkFace' 'Stucco' 'AsbShng' 'Brk Cmn' 'ImStucc' 'AsphShn' 'Stone'\n",
      " 'Other' 'CBlock']\n",
      "\n",
      "\n",
      "\n",
      "VinylSd    504\n",
      "MetalSd    214\n",
      "HdBoard    207\n",
      "Wd Sdng    197\n",
      "Plywood    142\n",
      "CmentBd     60\n",
      "Wd Shng     38\n",
      "Stucco      26\n",
      "BrkFace     25\n",
      "AsbShng     20\n",
      "ImStucc     10\n",
      "Brk Cmn      7\n",
      "Stone        5\n",
      "AsphShn      3\n",
      "CBlock       1\n",
      "Other        1\n",
      "Name: Exterior2nd, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "MasVnrType\n",
      "\n",
      "\n",
      "\n",
      "['BrkFace' 'None' 'Stone' 'BrkCmn' nan]\n",
      "\n",
      "\n",
      "\n",
      "None       864\n",
      "BrkFace    445\n",
      "Stone      128\n",
      "BrkCmn      15\n",
      "Name: MasVnrType, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtQual\n",
      "\n",
      "\n",
      "\n",
      "['Gd' 'TA' 'Ex' nan 'Fa']\n",
      "\n",
      "\n",
      "\n",
      "TA    649\n",
      "Gd    618\n",
      "Ex    121\n",
      "Fa     35\n",
      "Name: BsmtQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtCond\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Gd' nan 'Fa' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "TA    1311\n",
      "Gd      65\n",
      "Fa      45\n",
      "Po       2\n",
      "Name: BsmtCond, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtExposure\n",
      "\n",
      "\n",
      "\n",
      "['No' 'Gd' 'Mn' 'Av' nan]\n",
      "\n",
      "\n",
      "\n",
      "No    953\n",
      "Av    221\n",
      "Gd    134\n",
      "Mn    114\n",
      "Name: BsmtExposure, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType1\n",
      "\n",
      "\n",
      "\n",
      "['GLQ' 'ALQ' 'Unf' 'Rec' 'BLQ' nan 'LwQ']\n",
      "\n",
      "\n",
      "\n",
      "Unf    430\n",
      "GLQ    418\n",
      "ALQ    220\n",
      "BLQ    148\n",
      "Rec    133\n",
      "LwQ     74\n",
      "Name: BsmtFinType1, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType2\n",
      "\n",
      "\n",
      "\n",
      "['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ']\n",
      "\n",
      "\n",
      "\n",
      "Unf    1256\n",
      "Rec      54\n",
      "LwQ      46\n",
      "BLQ      33\n",
      "ALQ      19\n",
      "GLQ      14\n",
      "Name: BsmtFinType2, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "KitchenQual\n",
      "\n",
      "\n",
      "\n",
      "['Gd' 'TA' 'Ex' 'Fa']\n",
      "\n",
      "\n",
      "\n",
      "TA    735\n",
      "Gd    586\n",
      "Ex    100\n",
      "Fa     39\n",
      "Name: KitchenQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "Functional\n",
      "\n",
      "\n",
      "\n",
      "['Typ' 'Min1' 'Maj1' 'Min2' 'Mod' 'Maj2' 'Sev']\n",
      "\n",
      "\n",
      "\n",
      "Typ     1360\n",
      "Min2      34\n",
      "Min1      31\n",
      "Mod       15\n",
      "Maj1      14\n",
      "Maj2       5\n",
      "Sev        1\n",
      "Name: Functional, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "FireplaceQu\n",
      "\n",
      "\n",
      "\n",
      "[nan 'TA' 'Gd' 'Fa' 'Ex' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "Gd    380\n",
      "TA    313\n",
      "Fa     33\n",
      "Ex     24\n",
      "Po     20\n",
      "Name: FireplaceQu, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageType\n",
      "\n",
      "\n",
      "\n",
      "['Attchd' 'Detchd' 'BuiltIn' 'CarPort' nan 'Basment' '2Types']\n",
      "\n",
      "\n",
      "\n",
      "Attchd     870\n",
      "Detchd     387\n",
      "BuiltIn     88\n",
      "Basment     19\n",
      "CarPort      9\n",
      "2Types       6\n",
      "Name: GarageType, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageFinish\n",
      "\n",
      "\n",
      "\n",
      "['RFn' 'Unf' 'Fin' nan]\n",
      "\n",
      "\n",
      "\n",
      "Unf    605\n",
      "RFn    422\n",
      "Fin    352\n",
      "Name: GarageFinish, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageQual\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Fa' 'Gd' nan 'Ex' 'Po']\n",
      "\n",
      "\n",
      "\n",
      "TA    1311\n",
      "Fa      48\n",
      "Gd      14\n",
      "Po       3\n",
      "Ex       3\n",
      "Name: GarageQual, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "GarageCond\n",
      "\n",
      "\n",
      "\n",
      "['TA' 'Fa' nan 'Gd' 'Po' 'Ex']\n",
      "\n",
      "\n",
      "\n",
      "TA    1326\n",
      "Fa      35\n",
      "Gd       9\n",
      "Po       7\n",
      "Ex       2\n",
      "Name: GarageCond, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "SaleType\n",
      "\n",
      "\n",
      "\n",
      "['WD' 'New' 'COD' 'ConLD' 'ConLI' 'CWD' 'ConLw' 'Con' 'Oth']\n",
      "\n",
      "\n",
      "\n",
      "WD       1267\n",
      "New       122\n",
      "COD        43\n",
      "ConLD       9\n",
      "ConLw       5\n",
      "ConLI       5\n",
      "CWD         4\n",
      "Oth         3\n",
      "Con         2\n",
      "Name: SaleType, dtype: int64\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in cats_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i)\n",
    "        print(\"\\n\\n\")\n",
    "        print(df[i].unique())\n",
    "        print(\"\\n\\n\")\n",
    "        print(df[i].value_counts())\n",
    "        print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cubic-transsexual",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cats_df:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        df[i].fillna(df[i].mode()[0] , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "baking-guatemala",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cats_df:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        print(i , df[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "material-sight",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cats_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        test[i].fillna(df[i].mode()[0] , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "annual-holly",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cats_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i , test[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "direct-cleaning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass         0\n",
       "MSZoning           0\n",
       "LotFrontage      259\n",
       "LotArea            0\n",
       "Street             0\n",
       "                ... \n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleType           0\n",
       "SaleCondition      0\n",
       "SalePrice          0\n",
       "Length: 76, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "expressed-chain",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 259 float64\n",
      "MasVnrArea 8 float64\n",
      "GarageYrBlt 81 float64\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        print(i , df[i].isna().sum(), df[i].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "gross-bachelor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 227 float64\n",
      "MasVnrArea 15 float64\n",
      "BsmtFinSF1 1 float64\n",
      "BsmtFinSF2 1 float64\n",
      "BsmtUnfSF 1 float64\n",
      "TotalBsmtSF 1 float64\n",
      "BsmtFullBath 2 float64\n",
      "BsmtHalfBath 2 float64\n",
      "GarageYrBlt 78 float64\n",
      "GarageCars 1 float64\n",
      "GarageArea 1 float64\n"
     ]
    }
   ],
   "source": [
    "for i in test.columns:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i , test[i].isna().sum(), test[i].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "parliamentary-process",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass         0\n",
       "MSZoning           0\n",
       "LotFrontage      227\n",
       "LotArea            0\n",
       "Street             0\n",
       "                ... \n",
       "MiscVal            0\n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleType           0\n",
       "SaleCondition      0\n",
       "Length: 75, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "gentle-relationship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints_df = df.select_dtypes(exclude = \"object\").columns\n",
    "ints_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "annoying-warehouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_df = df.select_dtypes(include=\"object\").columns\n",
    "cats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "floating-communist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats_test = test.select_dtypes(include=\"object\").columns\n",
    "cats_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "interim-offense",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints_df = test.select_dtypes(exclude = \"object\").columns\n",
    "ints_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "green-courage",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cats_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i , test[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "acute-proposal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage 227\n",
      "MasVnrArea 15\n",
      "BsmtFinSF1 1\n",
      "BsmtFinSF2 1\n",
      "BsmtUnfSF 1\n",
      "TotalBsmtSF 1\n",
      "BsmtFullBath 2\n",
      "BsmtHalfBath 2\n",
      "GarageYrBlt 78\n",
      "GarageCars 1\n",
      "GarageArea 1\n"
     ]
    }
   ],
   "source": [
    "for i in ints_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i , test[i].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "alternative-transport",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df , columns = cats_df , prefix = cats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "valuable-houston",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.get_dummies(test , columns = cats_test , prefix = cats_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dying-committee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276, 259)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns) , len(test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "collaborative-theme",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice\n",
      "Utilities_NoSeWa\n",
      "Condition2_RRAe\n",
      "Condition2_RRAn\n",
      "Condition2_RRNn\n",
      "HouseStyle_2.5Fin\n",
      "RoofMatl_ClyTile\n",
      "RoofMatl_Membran\n",
      "RoofMatl_Metal\n",
      "RoofMatl_Roll\n",
      "Exterior1st_ImStucc\n",
      "Exterior1st_Stone\n",
      "Exterior2nd_Other\n",
      "Heating_Floor\n",
      "Heating_OthW\n",
      "Electrical_Mix\n",
      "GarageQual_Ex\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    if i not in test.columns:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "prime-webster",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.columns:\n",
    "    if i not in test.columns and i != \"SalePrice\":\n",
    "        test[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "republican-expression",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(276, 275)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns) , len(test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "outdoor-costume",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>60.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>65.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>8450.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>9550.0</td>\n",
       "      <td>14260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0       1        2       3        4\n",
       "MSSubClass               60.0    20.0     60.0    70.0     60.0\n",
       "LotFrontage              65.0    80.0     68.0    60.0     84.0\n",
       "LotArea                8450.0  9600.0  11250.0  9550.0  14260.0\n",
       "OverallQual               7.0     6.0      7.0     7.0      8.0\n",
       "OverallCond               5.0     8.0      5.0     5.0      5.0\n",
       "...                       ...     ...      ...     ...      ...\n",
       "SaleCondition_AdjLand     0.0     0.0      0.0     0.0      0.0\n",
       "SaleCondition_Alloca      0.0     0.0      0.0     0.0      0.0\n",
       "SaleCondition_Family      0.0     0.0      0.0     0.0      0.0\n",
       "SaleCondition_Normal      1.0     1.0      1.0     0.0      1.0\n",
       "SaleCondition_Partial     0.0     0.0      0.0     0.0      0.0\n",
       "\n",
       "[276 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "existing-borough",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop([\"SalePrice\"] , axis = 1)\n",
    "y = df[\"SalePrice\"]\n",
    "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size = 0.2 , random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "qualified-applicant",
   "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>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>20</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8400</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1957</td>\n",
       "      <td>1957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>922</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>60</td>\n",
       "      <td>59.0</td>\n",
       "      <td>7837</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1993</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>30</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8777</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1910</td>\n",
       "      <td>1950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7200</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1937</td>\n",
       "      <td>1950</td>\n",
       "      <td>252.0</td>\n",
       "      <td>569</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1924</td>\n",
       "      <td>1950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1095</th>\n",
       "      <td>20</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9317</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>50</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7804</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1928</td>\n",
       "      <td>1950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>622</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>20</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8172</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1955</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>50</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7642</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1918</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>120</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3684</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1168 rows × 275 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "254           20         70.0     8400            5            6       1957   \n",
       "1066          60         59.0     7837            6            7       1993   \n",
       "638           30         67.0     8777            5            7       1910   \n",
       "799           50         60.0     7200            5            7       1937   \n",
       "380           50         50.0     5000            5            6       1924   \n",
       "...          ...          ...      ...          ...          ...        ...   \n",
       "1095          20         78.0     9317            6            5       2006   \n",
       "1130          50         65.0     7804            4            3       1928   \n",
       "1294          20         60.0     8172            5            7       1955   \n",
       "860           50         55.0     7642            7            8       1918   \n",
       "1126         120         53.0     3684            7            5       2007   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  SaleType_ConLw  \\\n",
       "254           1957         0.0         922           0  ...               0   \n",
       "1066          1994         0.0           0           0  ...               0   \n",
       "638           1950         0.0           0           0  ...               0   \n",
       "799           1950       252.0         569           0  ...               0   \n",
       "380           1950         0.0         218           0  ...               0   \n",
       "...            ...         ...         ...         ...  ...             ...   \n",
       "1095          2006         0.0          24           0  ...               0   \n",
       "1130          1950         0.0         622           0  ...               0   \n",
       "1294          1990         0.0         167           0  ...               0   \n",
       "860           1998         0.0           0           0  ...               0   \n",
       "1126          2007       130.0           0           0  ...               0   \n",
       "\n",
       "      SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  \\\n",
       "254              0             0            1                      0   \n",
       "1066             0             0            1                      0   \n",
       "638              0             0            1                      0   \n",
       "799              0             0            1                      0   \n",
       "380              0             0            1                      0   \n",
       "...            ...           ...          ...                    ...   \n",
       "1095             0             0            1                      0   \n",
       "1130             0             0            1                      0   \n",
       "1294             0             0            1                      0   \n",
       "860              0             0            1                      0   \n",
       "1126             0             0            1                      0   \n",
       "\n",
       "      SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "254                       0                     0                     0   \n",
       "1066                      0                     0                     0   \n",
       "638                       0                     0                     0   \n",
       "799                       0                     0                     0   \n",
       "380                       0                     0                     0   \n",
       "...                     ...                   ...                   ...   \n",
       "1095                      0                     0                     0   \n",
       "1130                      0                     0                     0   \n",
       "1294                      0                     0                     0   \n",
       "860                       0                     0                     0   \n",
       "1126                      0                     0                     0   \n",
       "\n",
       "      SaleCondition_Normal  SaleCondition_Partial  \n",
       "254                      1                      0  \n",
       "1066                     1                      0  \n",
       "638                      1                      0  \n",
       "799                      1                      0  \n",
       "380                      1                      0  \n",
       "...                    ...                    ...  \n",
       "1095                     1                      0  \n",
       "1130                     1                      0  \n",
       "1294                     1                      0  \n",
       "860                      1                      0  \n",
       "1126                     1                      0  \n",
       "\n",
       "[1168 rows x 275 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "comparative-mailman",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage\n",
      "MasVnrArea\n",
      "GarageYrBlt\n"
     ]
    }
   ],
   "source": [
    "for i in X_train.columns:\n",
    "    if X_train[i].isna().sum() != 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "lesser-austin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[\"LotFrontage\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "documented-occurrence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[\"MasVnrArea\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "premium-outreach",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[\"GarageYrBlt\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "latter-implementation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass                 0\n",
       "LotFrontage              217\n",
       "LotArea                    0\n",
       "OverallQual                0\n",
       "OverallCond                0\n",
       "                        ... \n",
       "SaleCondition_AdjLand      0\n",
       "SaleCondition_Alloca       0\n",
       "SaleCondition_Family       0\n",
       "SaleCondition_Normal       0\n",
       "SaleCondition_Partial      0\n",
       "Length: 275, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "competitive-quilt",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n"
     ]
    }
   ],
   "source": [
    "for i in ints_df:\n",
    "    if df[i].isna().sum() != 0:\n",
    "        X_train[i].fillna(X_train[i].median() , inplace = True)\n",
    "        X_test[i].fillna(X_train[i].median() , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cardiac-cartridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       ...\n",
       "       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',\n",
       "       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',\n",
       "       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',\n",
       "       'SaleCondition_Partial'],\n",
       "      dtype='object', length=275)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints_df = X.select_dtypes(exclude = \"object\").columns\n",
    "ints_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "educated-linux",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ints_test:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        test[i].fillna(X_train[i].median() , inplace = True)\n",
    "        test[i].fillna(X_train[i].median() , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "embedded-round",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-d8b7de1dbd18>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train[ints_df] = scaler.fit_transform(X_train[ints_df])\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\indexing.py:1738: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n",
      "<ipython-input-57-d8b7de1dbd18>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_test[ints_df] = scaler.transform(X_test[ints_df])\n",
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\pandas\\core\\indexing.py:1738: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train[ints_df] = scaler.fit_transform(X_train[ints_df])\n",
    "X_test[ints_df] = scaler.transform(X_test[ints_df])\n",
    "test[ints_df] = scaler.transform(test[ints_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "injured-interview",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in X_train.columns:\n",
    "    if X_train[i].isna().sum() != 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "private-tulsa",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in X_train.columns:\n",
    "    if X_train[i].isna().sum() != 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "growing-produce",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in test.columns:\n",
    "    if test[i].isna().sum() != 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "living-harassment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "random = RandomForestRegressor(random_state = 42)\n",
    "random.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "educated-funds",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9794083023143713"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.score(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "established-lighting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8895202870824191"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "middle-congress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear = LinearRegression()\n",
    "linear.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "sound-royalty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9340656127418742"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear.score(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "greenhouse-construction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.6568416964092923e+22"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "thick-apparel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost\n",
    "xgb = xgboost.XGBRegressor()\n",
    "xgb.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fifty-specialist",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9996982354429245"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "soviet-wyoming",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8897205460183699"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "noticed-mobile",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:   47.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'n_estimators': [50, 100, 150, 200, 250, 300, 350,\n",
       "                                          400]},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rf_grid = {\"n_estimators\": [50 , 100 , 150 , 200 , 250 , 300 , 350 , 400]\n",
    "#            \"criterion\" : [\"msa\" , \"mse\"],\n",
    "#            \"max_features\" : [\"auto\" , \"sqrt\" , \"log2\"] , \n",
    "#            \"bootstrap\" : [True , False] \n",
    "          }\n",
    "\n",
    "rs_model = GridSearchCV(RandomForestRegressor(),\n",
    "                              param_grid=rf_grid,\n",
    "                              cv=5,\n",
    "                              n_jobs = -1,\n",
    "                              verbose = True)\n",
    "\n",
    "rs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "according-hawaii",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 250}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "instant-interest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9785430597543784"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_model.score(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "given-ferry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8967203650928468"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_model.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cutting-sally",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DINESHREDDY\\Desktop\\sample_project\\env\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8897205460183699"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "annoying-miami",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_preds = random.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "rapid-court",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([131746.  , 155111.95, 183565.71, ..., 163993.3 , 118647.5 ,\n",
       "       225338.01])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "aerial-tutorial",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"Id\" : ids , \n",
    "       \"SalePrice\" : random_preds}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "sapphire-discipline",
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.DataFrame(data , columns = [\"Id\" , \"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "computational-frequency",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>131746.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>155111.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>183565.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>188018.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>217893.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461  131746.00\n",
       "1  1462  155111.95\n",
       "2  1463  183565.71\n",
       "3  1464  188018.65\n",
       "4  1465  217893.56"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "extensive-accessory",
   "metadata": {},
   "outputs": [],
   "source": [
    "final.to_csv(\"HackerEarth House Rate 1.csv\" , index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}