house-price-first-attempt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# Loading the required packages and libraries\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that there are 79 features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 0\n",
       "MSSubClass         0\n",
       "MSZoning           0\n",
       "LotFrontage      259\n",
       "LotArea            0\n",
       "                ... \n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleType           0\n",
       "SaleCondition      0\n",
       "SalePrice          0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for visualizing the null vaues\n",
    "sns.heatmap(df.isnull(),yticklabels = False,cbar = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###         3.1 Missing value handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling the missing value we need to see the test and train data simultaneously.     \n",
    "We will be replacing the null values with mode for categorical values and mean for continous values.    \n",
    "We will delete columns with more than 50% null values as the available information add no value for our model.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see MSZoning is having missing values in test data but not in train data. We need to handle it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RL         1151\n",
       "RM          218\n",
       "FV           65\n",
       "RH           16\n",
       "C (all)      10\n",
       "Name: MSZoning, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling missing data for MSZoning\n",
    "df['MSZoning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['MSZoning'] = df_test['MSZoning'].fillna(df_test['MSZoning'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LotFrontage'] = df['LotFrontage'].fillna(df['LotFrontage'].mean()) # replacing with mean for train\n",
    "df_test['LotFrontage'] = df_test['LotFrontage'].fillna(df_test['LotFrontage'].mean()) # replacing with mean for train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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    1460 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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Alley'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for train\n",
    "df_test.drop(['Alley'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "utilities has missing values in test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AllPub    1459\n",
       "NoSeWa       1\n",
       "Name: Utilities, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Utilities'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Utilities'] = df_test['Utilities'].fillna(df_test['Utilities'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "AsphShn      1\n",
       "Stone        1\n",
       "Name: Exterior2nd, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Exterior2nd'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_test['Exterior1st'] = df_test['Exterior1st'].fillna(df_test['Exterior1st'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_test['Exterior2nd'] = df_test['Exterior2nd'].fillna(df_test['Exterior2nd'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       864\n",
       "BrkFace    445\n",
       "Stone      128\n",
       "BrkCmn      15\n",
       "Name: MasVnrType, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MasVnrType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MasVnrType'] = df['MasVnrType'].fillna(df['MasVnrType'].mode()[0]) # replacing with mode\n",
    "df_test['MasVnrType'] = df_test['MasVnrType'].fillna(df_test['MasVnrType'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MasVnrArea'] = df['MasVnrArea'].fillna(df['MasVnrArea'].mean()) # replacing with mean for train\n",
    "df_test['MasVnrArea'] = df_test['MasVnrArea'].fillna(df_test['MasVnrArea'].mean()) # replacing with mean for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BsmtCond'] = df['BsmtCond'].fillna(df['BsmtCond'].mode()[0]) # replacing with mode\n",
    "df_test['BsmtCond'] = df_test['BsmtCond'].fillna(df_test['BsmtCond'].mode()[0]) # replacing with mode\n",
    "df['BsmtQual'] = df['BsmtQual'].fillna(df['BsmtQual'].mode()[0]) # replacing with mode\n",
    "df_test['BsmtQual'] = df_test['BsmtQual'].fillna(df_test['BsmtQual'].mode()[0]) # replacing with mode\n",
    "df['BsmtExposure'] = df['BsmtExposure'].fillna(df['BsmtExposure'].mode()[0]) # replacing with mode\n",
    "df_test['BsmtExposure'] = df_test['BsmtExposure'].fillna(df_test['BsmtExposure'].mode()[0]) # replacing with mode\n",
    "\n",
    "\n",
    "df_test['BsmtFinSF2'] = df_test['BsmtFinSF2'].fillna(df_test['BsmtFinSF2'].mean()) # replacing with mean for test\n",
    "df_test['BsmtUnfSF'] = df_test['BsmtUnfSF'].fillna(df_test['BsmtUnfSF'].mean()) # replacing with mean for test\n",
    "df_test['TotalBsmtSF'] = df_test['TotalBsmtSF'].fillna(df_test['TotalBsmtSF'].mean()) # replacing with mean for test\n",
    "df_test['BsmtFullBath'] = df_test['BsmtFullBath'].fillna(df_test['BsmtFullBath'].mean()) # replacing with mean for test\n",
    "df_test['BsmtHalfBath'] = df_test['BsmtHalfBath'].fillna(df_test['BsmtHalfBath'].mean()) # replacing with mean for test\n",
    "df_test['GarageArea'] = df_test['GarageArea'].fillna(df_test['GarageArea'].mean()) # replacing with mean for test\n",
    "\n",
    "\n",
    "df['BsmtFinType1'] = df['BsmtFinType1'].fillna(df['BsmtFinType1'].mode()[0]) # replacing with mode\n",
    "df_test['BsmtFinType1'] = df_test['BsmtFinType1'].fillna(df_test['BsmtFinType1'].mode()[0]) # replacing with mode\n",
    "df['BsmtFinType2'] = df['BsmtFinType2'].fillna(df['BsmtFinType2'].mode()[0]) # replacing with mode\n",
    "df_test['BsmtFinType2'] = df_test['BsmtFinType2'].fillna(df_test['BsmtFinType2'].mode()[0]) # replacing with mode\n",
    "\n",
    "df_test['KitchenQual'] = df_test['KitchenQual'].fillna(df_test['KitchenQual'].mode()[0]) # replacing with mode\n",
    "df_test['Functional'] = df_test['Functional'].fillna(df_test['Functional'].mode()[0]) # replacing with mode\n",
    "df_test['GarageCars'] = df_test['GarageCars'].fillna(df_test['GarageCars'].mode()[0]) # replacing with mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GarageType'] = df['GarageType'].fillna(df['GarageType'].mode()[0]) # replacing with mode\n",
    "df_test['GarageType'] = df_test['GarageType'].fillna(df_test['GarageType'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GarageFinish'] = df['GarageFinish'].fillna(df['GarageFinish'].mode()[0]) # replacing with mode\n",
    "df_test['GarageFinish'] = df_test['GarageFinish'].fillna(df_test['GarageFinish'].mode()[0]) # replacing with mode\n",
    "df['GarageQual'] = df['GarageQual'].fillna(df['GarageQual'].mode()[0]) # replacing with mode\n",
    "df_test['GarageQual'] = df_test['GarageQual'].fillna(df_test['GarageQual'].mode()[0]) # replacing with mode\n",
    "df['GarageCond'] = df['GarageCond'].fillna(df['GarageCond'].mode()[0]) # replacing with mode\n",
    "df_test['GarageCond'] = df_test['GarageCond'].fillna(df_test['GarageCond'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005.0    65\n",
       "2006.0    59\n",
       "2004.0    53\n",
       "2003.0    50\n",
       "2007.0    49\n",
       "          ..\n",
       "1908.0     1\n",
       "1927.0     1\n",
       "1933.0     1\n",
       "1900.0     1\n",
       "1906.0     1\n",
       "Name: GarageYrBlt, Length: 97, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GarageYrBlt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GarageYrBlt'] = df['GarageYrBlt'].fillna(df['GarageYrBlt'].mode()[0]) # replacing with mode\n",
    "df_test['GarageYrBlt'] = df_test['GarageYrBlt'].fillna(df_test['GarageYrBlt'].mode()[0]) # replacing with mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the columns which have lot of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PoolQC'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for train\n",
    "df_test.drop(['PoolQC'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for test\n",
    "df.drop(['Fence'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for train\n",
    "df_test.drop(['Fence'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for test\n",
    "df.drop(['MiscFeature'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for train\n",
    "df_test.drop(['MiscFeature'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for test\n",
    "df.drop(['FireplaceQu'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for train\n",
    "df_test.drop(['FireplaceQu'],axis = 1, inplace = True) # dropping the Alley features as it has a lot of missing values for test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_test.isnull(),yticklabels = False, cbar = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels = False, cbar = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 76)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 76 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    1460 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   LotShape       1460 non-null   object \n",
      " 7   LandContour    1460 non-null   object \n",
      " 8   Utilities      1460 non-null   object \n",
      " 9   LotConfig      1460 non-null   object \n",
      " 10  LandSlope      1460 non-null   object \n",
      " 11  Neighborhood   1460 non-null   object \n",
      " 12  Condition1     1460 non-null   object \n",
      " 13  Condition2     1460 non-null   object \n",
      " 14  BldgType       1460 non-null   object \n",
      " 15  HouseStyle     1460 non-null   object \n",
      " 16  OverallQual    1460 non-null   int64  \n",
      " 17  OverallCond    1460 non-null   int64  \n",
      " 18  YearBuilt      1460 non-null   int64  \n",
      " 19  YearRemodAdd   1460 non-null   int64  \n",
      " 20  RoofStyle      1460 non-null   object \n",
      " 21  RoofMatl       1460 non-null   object \n",
      " 22  Exterior1st    1460 non-null   object \n",
      " 23  Exterior2nd    1460 non-null   object \n",
      " 24  MasVnrType     1460 non-null   object \n",
      " 25  MasVnrArea     1460 non-null   float64\n",
      " 26  ExterQual      1460 non-null   object \n",
      " 27  ExterCond      1460 non-null   object \n",
      " 28  Foundation     1460 non-null   object \n",
      " 29  BsmtQual       1460 non-null   object \n",
      " 30  BsmtCond       1460 non-null   object \n",
      " 31  BsmtExposure   1460 non-null   object \n",
      " 32  BsmtFinType1   1460 non-null   object \n",
      " 33  BsmtFinSF1     1460 non-null   int64  \n",
      " 34  BsmtFinType2   1460 non-null   object \n",
      " 35  BsmtFinSF2     1460 non-null   int64  \n",
      " 36  BsmtUnfSF      1460 non-null   int64  \n",
      " 37  TotalBsmtSF    1460 non-null   int64  \n",
      " 38  Heating        1460 non-null   object \n",
      " 39  HeatingQC      1460 non-null   object \n",
      " 40  CentralAir     1460 non-null   object \n",
      " 41  Electrical     1459 non-null   object \n",
      " 42  1stFlrSF       1460 non-null   int64  \n",
      " 43  2ndFlrSF       1460 non-null   int64  \n",
      " 44  LowQualFinSF   1460 non-null   int64  \n",
      " 45  GrLivArea      1460 non-null   int64  \n",
      " 46  BsmtFullBath   1460 non-null   int64  \n",
      " 47  BsmtHalfBath   1460 non-null   int64  \n",
      " 48  FullBath       1460 non-null   int64  \n",
      " 49  HalfBath       1460 non-null   int64  \n",
      " 50  BedroomAbvGr   1460 non-null   int64  \n",
      " 51  KitchenAbvGr   1460 non-null   int64  \n",
      " 52  KitchenQual    1460 non-null   object \n",
      " 53  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 54  Functional     1460 non-null   object \n",
      " 55  Fireplaces     1460 non-null   int64  \n",
      " 56  GarageType     1460 non-null   object \n",
      " 57  GarageYrBlt    1460 non-null   float64\n",
      " 58  GarageFinish   1460 non-null   object \n",
      " 59  GarageCars     1460 non-null   int64  \n",
      " 60  GarageArea     1460 non-null   int64  \n",
      " 61  GarageQual     1460 non-null   object \n",
      " 62  GarageCond     1460 non-null   object \n",
      " 63  PavedDrive     1460 non-null   object \n",
      " 64  WoodDeckSF     1460 non-null   int64  \n",
      " 65  OpenPorchSF    1460 non-null   int64  \n",
      " 66  EnclosedPorch  1460 non-null   int64  \n",
      " 67  3SsnPorch      1460 non-null   int64  \n",
      " 68  ScreenPorch    1460 non-null   int64  \n",
      " 69  PoolArea       1460 non-null   int64  \n",
      " 70  MiscVal        1460 non-null   int64  \n",
      " 71  MoSold         1460 non-null   int64  \n",
      " 72  YrSold         1460 non-null   int64  \n",
      " 73  SaleType       1460 non-null   object \n",
      " 74  SaleCondition  1460 non-null   object \n",
      " 75  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(38)\n",
      "memory usage: 867.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 76)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 75)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the list of categorical features\n",
    "columns=['MSZoning','Street','LotShape','LandContour','Utilities','LotConfig','LandSlope','Neighborhood',\n",
    "         'Condition2','BldgType','Condition1','HouseStyle','SaleType','SaleCondition','ExterCond',\n",
    "         'ExterQual','Foundation','BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2',\n",
    "         'RoofStyle','RoofMatl','Exterior1st','Exterior2nd','MasVnrType','Heating','HeatingQC',\n",
    "         'CentralAir','Electrical','KitchenQual','Functional','GarageType','GarageFinish','GarageQual',\n",
    "         'GarageCond','PavedDrive']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting categorical variables to one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to convert categorical variables to one hot encoding\n",
    "def category_onehot_multcols(multcolumns):\n",
    "    df_final=final_df\n",
    "    i=0\n",
    "    for fields in multcolumns:\n",
    "        \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",
    "            \n",
    "            df_final=pd.concat([df_final,df1],axis=1)\n",
    "        i=i+1\n",
    "       \n",
    "        \n",
    "    df_final=pd.concat([final_df,df_final],axis=1)\n",
    "        \n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a copy of dataframe for future use\n",
    "main_df=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concanating the test and train files to implement one hot encoding\n",
    "final_df=pd.concat([df,df_test],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2918, 76)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning\n",
      "Street\n",
      "LotShape\n",
      "LandContour\n",
      "Utilities\n",
      "LotConfig\n",
      "LandSlope\n",
      "Neighborhood\n",
      "Condition2\n",
      "BldgType\n",
      "Condition1\n",
      "HouseStyle\n",
      "SaleType\n",
      "SaleCondition\n",
      "ExterCond\n",
      "ExterQual\n",
      "Foundation\n",
      "BsmtQual\n",
      "BsmtCond\n",
      "BsmtExposure\n",
      "BsmtFinType1\n",
      "BsmtFinType2\n",
      "RoofStyle\n",
      "RoofMatl\n",
      "Exterior1st\n",
      "Exterior2nd\n",
      "MasVnrType\n",
      "Heating\n",
      "HeatingQC\n",
      "CentralAir\n",
      "Electrical\n",
      "KitchenQual\n",
      "Functional\n",
      "GarageType\n",
      "GarageFinish\n",
      "GarageQual\n",
      "GarageCond\n",
      "PavedDrive\n"
     ]
    }
   ],
   "source": [
    "final_df=category_onehot_multcols(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2918, 234)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing duplicated columns\n",
    "final_df =final_df.loc[:,~final_df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2918, 178)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating the test and training data\n",
    "df_Train=final_df.iloc[:1459,:]\n",
    "df_Test=final_df.iloc[1459:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 178)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sapnasharma/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py:3997: 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",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# dropping the \"SalePrice\" column from test data\n",
    "df_Test.drop(['SalePrice'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing data for feeding into model\n",
    "X_train=df_Train.drop(['SalePrice'],axis=1)\n",
    "y_train=df_Train['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>Min1</th>\n",
       "      <th>Min2</th>\n",
       "      <th>Typ</th>\n",
       "      <th>Attchd</th>\n",
       "      <th>Basment</th>\n",
       "      <th>BuiltIn</th>\n",
       "      <th>CarPort</th>\n",
       "      <th>Detchd</th>\n",
       "      <th>RFn</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 177 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1          60         65.0     8450            7            5       2003   \n",
       "1   2          20         80.0     9600            6            8       1976   \n",
       "2   3          60         68.0    11250            7            5       2001   \n",
       "3   4          70         60.0     9550            7            5       1915   \n",
       "4   5          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  Min1  Min2  Typ  Attchd  \\\n",
       "0          2003       196.0       706.0  ...     0     0    1       1   \n",
       "1          1976         0.0       978.0  ...     0     0    1       1   \n",
       "2          2002       162.0       486.0  ...     0     0    1       1   \n",
       "3          1970         0.0       216.0  ...     0     0    1       0   \n",
       "4          2000       350.0       655.0  ...     0     0    1       1   \n",
       "\n",
       "   Basment  BuiltIn  CarPort  Detchd  RFn  P  \n",
       "0        0        0        0       0    1  0  \n",
       "1        0        0        0       0    1  0  \n",
       "2        0        0        0       0    1  0  \n",
       "3        0        0        0       1    0  0  \n",
       "4        0        0        0       0    1  0  \n",
       "\n",
       "[5 rows x 177 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['GarageYrBlt'] = pd.to_numeric(X_train['GarageYrBlt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2003.0\n",
       "1       1976.0\n",
       "2       2001.0\n",
       "3       1998.0\n",
       "4       2000.0\n",
       "         ...  \n",
       "1455    1999.0\n",
       "1456    1978.0\n",
       "1457    1941.0\n",
       "1458    1950.0\n",
       "1459    1965.0\n",
       "Name: GarageYrBlt, Length: 1459, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['GarageYrBlt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Base Model implementation and evaluating it's RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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=0, 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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# implementing XGBoost regressor\n",
    "import xgboost\n",
    "classifier=xgboost.XGBRegressor()\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = \"finalazied_model.plk\"\n",
    "pickle.dump(classifier,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sapnasharma/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_Test['GarageYrBlt'] = pd.to_numeric(df_Test['GarageYrBlt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1958.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Test['GarageYrBlt'][1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the house prices\n",
    "y_predict = classifier.predict(df_Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126564.48, 150072.28, 186359.94, ..., 194201.23, 103751.97,\n",
       "       230613.06], dtype=float32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the house price\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Creating Sample Submission file\n",
    "pred=pd.DataFrame(y_predict)\n",
    "sub_df=pd.read_csv('sample_submission.csv')\n",
    "datasets=pd.concat([sub_df['Id'],pred],axis=1)\n",
    "datasets.columns=['Id','SalePrice']\n",
    "datasets.to_csv('sub1.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Base Mode Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Got an RMSE score of 0.14065  on kaggle leaderboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Hyper parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "regressor=xgboost.XGBRegressor()\n",
    "booster=['gbtree','gblinear']\n",
    "base_score=[0.25,0.5,0.75,1]\n",
    "\n",
    "## Hyper Parameter Optimization\n",
    "\n",
    "n_estimators = [100, 500, 900, 1100, 1500]\n",
    "max_depth = [2, 3, 5, 10, 15]\n",
    "\n",
    "learning_rate=[0.05,0.1,0.15,0.20]\n",
    "min_child_weight=[1,2,3,4]\n",
    "\n",
    "# Defining the grid of hyperparameters to search\n",
    "hyperparameter_grid = {\n",
    "    'n_estimators': n_estimators,\n",
    "    'max_depth':max_depth,\n",
    "    'learning_rate':learning_rate,\n",
    "    'min_child_weight':min_child_weight,\n",
    "    'booster':booster,\n",
    "    'base_score':base_score\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the random search with 4-fold cross validation\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "regressor = xgboost.XGBRegressor()\n",
    "random_cv = RandomizedSearchCV(estimator=regressor,\n",
    "            param_distributions=hyperparameter_grid,\n",
    "            cv=5, n_iter=50,\n",
    "            scoring = 'neg_mean_absolute_error',n_jobs = 4,\n",
    "            verbose = 5, \n",
    "            return_train_score = True,\n",
    "            random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:   12.5s\n",
      "[Parallel(n_jobs=4)]: Done  64 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:  4.4min\n",
      "[Parallel(n_jobs=4)]: Done 250 out of 250 | elapsed:  6.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, gamma=None,\n",
       "                                          gpu_id=None, importance_type='gain',\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=None,\n",
       "                                          max_delta_step=None, max_depth=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          n_estimators=100, n...\n",
       "                                          validate_parameters=None,\n",
       "                                          verbosity=None),\n",
       "                   n_iter=50, n_jobs=4,\n",
       "                   param_distributions={'base_score': [0.25, 0.5, 0.75, 1],\n",
       "                                        'booster': ['gbtree', 'gblinear'],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2],\n",
       "                                        'max_depth': [2, 3, 5, 10, 15],\n",
       "                                        'min_child_weight': [1, 2, 3, 4],\n",
       "                                        'n_estimators': [100, 500, 900, 1100,\n",
       "                                                         1500]},\n",
       "                   random_state=42, return_train_score=True,\n",
       "                   scoring='neg_mean_absolute_error', verbose=5)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "random_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.25, 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=0.1, max_delta_step=0, max_depth=2,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=900, n_jobs=0, 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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the best estimate\n",
    "random_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the above results we tune our regerssor for better performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# substituting the best parameters\n",
    "regressor=xgboost.XGBRegressor(base_score=0.25, 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=0.1, max_delta_step=0, max_depth=2,\n",
    "             min_child_weight=1, missing=None, monotone_constraints='()',\n",
    "             n_estimators=900, n_jobs=0, 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.25, 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=0.1, max_delta_step=0, max_depth=2,\n",
       "             min_child_weight=1, missing=None, monotone_constraints='()',\n",
       "             n_estimators=900, n_jobs=0, 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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict1 = regressor.predict(df_Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([125581.42, 162919.4 , 188219.47, ..., 175510.75, 120531.73,\n",
       "       238281.58], dtype=float32)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Create Sample Submission file and Submit using ANN\n",
    "pred=pd.DataFrame(y_predict1)\n",
    "sub_df=pd.read_csv('sample_submission.csv')\n",
    "datasets=pd.concat([sub_df['Id'],pred],axis=1)\n",
    "datasets.columns=['Id','SalePrice']\n",
    "datasets.to_csv('housepricetuned.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Got an RMSE score of 0.14036 on kaggle leaderboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got a slight improvement with hyperparameter tuning (from 0.14065 to 0.14036). Needs more tuning!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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