Best_for_home_prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Read the data\n",
    "test_df = pd.read_csv('test.csv')\n",
    "df = pd.read_csv('train.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['MSZoning'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LotFrontage'] = df['LotFrontage'].fillna(df['LotFrontage'].mean())\n",
    "df['BsmtCond'] = df['BsmtCond'].fillna(df['BsmtCond'].mode()[0])\n",
    "df['BsmtQual'] = df['BsmtQual'].fillna(df['BsmtQual'].mode()[0])\n",
    "df['FireplaceQu'] = df['FireplaceQu'].fillna(df['FireplaceQu'].mode()[0])\n",
    "df['GarageType'] = df['GarageType'].fillna(df['GarageType'].mode()[0])\n",
    "df['GarageFinish'] = df['GarageFinish'].fillna(df['GarageFinish'].mode()[0])\n",
    "df['GarageQual'] = df['GarageQual'].fillna(df['GarageQual'].mode()[0])\n",
    "df['GarageCond'] = df['GarageCond'].fillna(df['GarageCond'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['LotFrontage'] = test_df['LotFrontage'].fillna(test_df['LotFrontage'].mean())\n",
    "test_df['BsmtCond'] = test_df['BsmtCond'].fillna(test_df['BsmtCond'].mode()[0])\n",
    "test_df['BsmtQual'] = test_df['BsmtQual'].fillna(test_df['BsmtQual'].mode()[0])\n",
    "test_df['FireplaceQu'] = test_df['FireplaceQu'].fillna(test_df['FireplaceQu'].mode()[0])\n",
    "test_df['GarageType'] = test_df['GarageType'].fillna(test_df['GarageType'].mode()[0])\n",
    "test_df['GarageFinish'] = test_df['GarageFinish'].fillna(test_df['GarageFinish'].mode()[0])\n",
    "test_df['GarageQual'] = test_df['GarageQual'].fillna(test_df['GarageQual'].mode()[0])\n",
    "test_df['GarageCond'] = test_df['GarageCond'].fillna(test_df['GarageCond'].mode()[0])\n",
    "test_df['MSZoning'] = test_df['MSZoning'].fillna(test_df['MSZoning'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Alley', 'GarageYrBlt', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop(['Alley', 'GarageYrBlt', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning\n",
       "RL         1114\n",
       "RM          242\n",
       "FV           74\n",
       "C (all)      15\n",
       "RH           10\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MSZoning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1422, 75)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass       0\n",
       "MSZoning         0\n",
       "LotFrontage      0\n",
       "LotArea          0\n",
       "Street           0\n",
       "                ..\n",
       "MoSold           0\n",
       "YrSold           0\n",
       "SaleType         0\n",
       "SaleCondition    0\n",
       "SalePrice        0\n",
       "Length: 75, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MasVnrType'] = df['MasVnrType'].fillna(df['MasVnrType'].mode()[0])\n",
    "df['MasVnrArea'] = df['MasVnrArea'].fillna(df['MasVnrArea'].mode()[0])\n",
    "df['BsmtExposure'] = df['BsmtExposure'].fillna(df['BsmtExposure'].mode()[0])\n",
    "df['BsmtFinType2'] = df['BsmtFinType2'].fillna(df['BsmtFinType2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['MasVnrType'] = test_df['MasVnrType'].fillna(test_df['MasVnrType'].mode()[0])\n",
    "test_df['MasVnrArea'] = test_df['MasVnrArea'].fillna(test_df['MasVnrArea'].mode()[0])\n",
    "test_df['BsmtExposure'] = test_df['BsmtExposure'].fillna(test_df['BsmtExposure'].mode()[0])\n",
    "test_df['BsmtFinType2'] = test_df['BsmtFinType2'].fillna(test_df['BsmtFinType2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Utilities'] = test_df['Utilities'].fillna(test_df['Utilities'].mode()[0])\n",
    "test_df['Exterior1st'] = test_df['Exterior1st'].fillna(test_df['Exterior1st'].mode()[0])\n",
    "test_df['Exterior2nd'] = test_df['Exterior2nd'].fillna(test_df['Exterior2nd'].mode()[0])\n",
    "test_df['BsmtFinType1'] = test_df['BsmtFinType1'].fillna(test_df['BsmtFinType1'].mode()[0])\n",
    "test_df['BsmtFinSF1'] = test_df['BsmtFinSF1'].fillna(test_df['BsmtFinSF1'].mean())\n",
    "test_df['BsmtFinSF2'] = test_df['BsmtFinSF2'].fillna(test_df['BsmtFinSF2'].mean())\n",
    "test_df['BsmtUnfSF'] = test_df['BsmtUnfSF'].fillna(test_df['BsmtUnfSF'].mean())\n",
    "test_df['TotalBsmtSF'] = test_df['TotalBsmtSF'].fillna(test_df['TotalBsmtSF'].mean())\n",
    "test_df['BsmtFullBath'] = test_df['BsmtFullBath'].fillna(test_df['BsmtFullBath'].mode()[0])\n",
    "test_df['BsmtHalfBath'] = test_df['BsmtHalfBath'].fillna(test_df['BsmtHalfBath'].mode()[0])\n",
    "test_df['KitchenQual'] = test_df['KitchenQual'].fillna(test_df['KitchenQual'].mode()[0])\n",
    "test_df['Functional'] = test_df['Functional'].fillna(test_df['Functional'].mode()[0])\n",
    "test_df['GarageCars'] = test_df['GarageCars'].fillna(test_df['GarageCars'].mean())\n",
    "test_df['GarageArea'] = test_df['GarageArea'].fillna(test_df['GarageArea'].mean())\n",
    "test_df['SaleType'] = test_df['SaleType'].fillna(test_df['SaleType'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, df.isnull().any()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# columns = [('Street', 2),\n",
    "#  'Utilities', 'CentralAir', \n",
    "#  'LandSlope','PavedDrive', \n",
    "#  'LotShape','LandContour',\n",
    "#  'ExterQual', 'KitchenQual',\n",
    "#  'MSZoning','LotConfig','BldgType', 5)'ExterCond', 5 'HeatingQC', 5 'Condition2', \n",
    "#  'RoofStyle', 6 'Foundation', \n",
    "#  'Heating', 6),Functional', \n",
    "#  'SaleCondition6),\n",
    "#  'RoofMatl', 7)'HouseStyle', \n",
    "#  'Condition1', \n",
    "#  'SaleType', 9)'Exterior1st',),\n",
    "#  'Exterior2nd',),\n",
    "#  'Neighborhood'5)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [col for col in df.columns if df[col].dtype == \"object\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def category_onehot_multcols(multcolumns):\n",
    "    df_final = final_df\n",
    "    i = 0\n",
    "    for fields in multcolumns:\n",
    "        print(fields)\n",
    "        df1=pd.get_dummies(final_df[fields], drop_first=True)\n",
    "\n",
    "        final_df.drop([fields], axis=1, inplace=True)\n",
    "        if i == 0:\n",
    "            df_final=df1.copy()\n",
    "        else:\n",
    "            df_final=pd.concat([df_final, df1], axis=1)\n",
    "        i = i+1\n",
    "\n",
    "    df_final=pd.concat([final_df, df_final], axis=1)\n",
    "\n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 74)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv('formulatedtest.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df=pd.concat([df, test_df], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2881, 75)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning\n",
      "Street\n",
      "LotShape\n",
      "LandContour\n",
      "Utilities\n",
      "LotConfig\n",
      "LandSlope\n",
      "Neighborhood\n",
      "Condition1\n",
      "Condition2\n",
      "BldgType\n",
      "HouseStyle\n",
      "RoofStyle\n",
      "RoofMatl\n",
      "Exterior1st\n",
      "Exterior2nd\n",
      "MasVnrType\n",
      "ExterQual\n",
      "ExterCond\n",
      "Foundation\n",
      "BsmtQual\n",
      "BsmtCond\n",
      "BsmtExposure\n",
      "BsmtFinType1\n",
      "BsmtFinType2\n",
      "Heating\n",
      "HeatingQC\n",
      "CentralAir\n",
      "Electrical\n",
      "KitchenQual\n",
      "Functional\n",
      "FireplaceQu\n",
      "GarageType\n",
      "GarageFinish\n",
      "GarageQual\n",
      "GarageCond\n",
      "PavedDrive\n",
      "SaleType\n",
      "SaleCondition\n"
     ]
    }
   ],
   "source": [
    "final_df=category_onehot_multcols(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2881, 234)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.loc[:, ~final_df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2881, 174)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>ConLI</th>\n",
       "      <th>ConLw</th>\n",
       "      <th>New</th>\n",
       "      <th>Oth</th>\n",
       "      <th>WD</th>\n",
       "      <th>AdjLand</th>\n",
       "      <th>Alloca</th>\n",
       "      <th>Family</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 174 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  ConLI  ConLw    New  \\\n",
       "0          2003       196.0       706.0         0.0  ...  False  False  False   \n",
       "1          1976         0.0       978.0         0.0  ...  False  False  False   \n",
       "2          2002       162.0       486.0         0.0  ...  False  False  False   \n",
       "3          1970         0.0       216.0         0.0  ...  False  False  False   \n",
       "4          2000       350.0       655.0         0.0  ...  False  False  False   \n",
       "\n",
       "     Oth    WD  AdjLand  Alloca  Family  Normal  Partial  \n",
       "0  False  True    False   False   False    True    False  \n",
       "1  False  True    False   False   False    True    False  \n",
       "2  False  True    False   False   False    True    False  \n",
       "3  False  True    False   False   False   False    False  \n",
       "4  False  True    False   False   False    True    False  \n",
       "\n",
       "[5 rows x 174 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Train=final_df.iloc[:1422, :]\n",
    "df_Test=final_df.iloc[1422:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\annay\\AppData\\Local\\Temp\\ipykernel_10236\\2353574549.py:1: 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",
      "  df_Test.drop(['SalePrice'], axis=1, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df_Test.drop(['SalePrice'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 173)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_Train.drop(['SalePrice'], axis=1)\n",
    "y_train = df_Train['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'xgboost'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[83], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxgboost\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m XGBRegressor\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  }
 ],
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