House_Prices_Advanced_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "nyYRiqeod2pe"
   },
   "outputs": [],
   "source": [
    "# to predict the final price of each home\n",
    "\n",
    "# 79 explanatory variables\n",
    "\n",
    "# Practice Skills\n",
    "# Creative feature engineering \n",
    "# Advanced regression techniques like random forest and gradient boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "aNLuEUi9kr_K"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c019lR77naEL"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../DataSets/House_train.csv')\n",
    "df_test = pd.read_csv('../DataSets/House_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "piF-koUXnsAd",
    "outputId": "708058f3-c6bd-454c-d5e2-6bd94465e5fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 81), (1459, 80))"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "id": "wER8Gmp6n22-",
    "outputId": "1e3f5ccd-31aa-4f16-9fc5-9180302961f8"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
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       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>...</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>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",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
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       "      <td>Unf</td>\n",
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       "      <td>150</td>\n",
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       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
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       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
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       "      <td>1262</td>\n",
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       "      <td>2007</td>\n",
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       "      <td>181500</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>CollgCr</td>\n",
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       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
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       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>9</td>\n",
       "      <td>2008</td>\n",
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       "    <tr>\n",
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       "      <td>1Fam</td>\n",
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       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
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       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1998.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
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       "    <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>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
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       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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  ...  SaleType  SaleCondition SalePrice\n",
       "0   1          60       RL  ...        WD         Normal    208500\n",
       "1   2          20       RL  ...        WD         Normal    181500\n",
       "2   3          60       RL  ...        WD         Normal    223500\n",
       "3   4          70       RL  ...        WD        Abnorml    140000\n",
       "4   5          60       RL  ...        WD         Normal    250000\n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "q49N1R98og4L",
    "outputId": "e7c97a5f-9948-4b1c-a2b2-983a3d46004b"
   },
   "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>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>602.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>StoneBr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  ...  YrSold  SaleType SaleCondition\n",
       "0  1461          20       RH  ...    2010        WD        Normal\n",
       "1  1462          20       RL  ...    2010        WD        Normal\n",
       "2  1463          60       RL  ...    2010        WD        Normal\n",
       "3  1464          60       RL  ...    2010        WD        Normal\n",
       "4  1465         120       RL  ...    2010        WD        Normal\n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-JV2VG-1SLEo",
    "outputId": "a15c66a4-5e19-4e4b-8af9-d0aecbf28bac"
   },
   "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_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pOhDKA1iSLlj",
    "outputId": "f7bf2f33-c66c-4675-d685-94f707b61691"
   },
   "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": "code",
   "execution_count": null,
   "metadata": {
    "id": "HFOq7pquluJo"
   },
   "outputs": [],
   "source": [
    "# find corelation and then try to remove columns which has corelation less than 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qauC7kcRluW1",
    "outputId": "06151b69-677d-4cb3-82ed-f8597e9ee037"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id              -0.021917\n",
       "MSSubClass      -0.084284\n",
       "LotFrontage      0.351799\n",
       "LotArea          0.263843\n",
       "OverallQual      0.790982\n",
       "OverallCond     -0.077856\n",
       "YearBuilt        0.522897\n",
       "YearRemodAdd     0.507101\n",
       "MasVnrArea       0.477493\n",
       "BsmtFinSF1       0.386420\n",
       "BsmtFinSF2      -0.011378\n",
       "BsmtUnfSF        0.214479\n",
       "TotalBsmtSF      0.613581\n",
       "1stFlrSF         0.605852\n",
       "2ndFlrSF         0.319334\n",
       "LowQualFinSF    -0.025606\n",
       "GrLivArea        0.708624\n",
       "BsmtFullBath     0.227122\n",
       "BsmtHalfBath    -0.016844\n",
       "FullBath         0.560664\n",
       "HalfBath         0.284108\n",
       "BedroomAbvGr     0.168213\n",
       "KitchenAbvGr    -0.135907\n",
       "TotRmsAbvGrd     0.533723\n",
       "Fireplaces       0.466929\n",
       "GarageYrBlt      0.486362\n",
       "GarageCars       0.640409\n",
       "GarageArea       0.623431\n",
       "WoodDeckSF       0.324413\n",
       "OpenPorchSF      0.315856\n",
       "EnclosedPorch   -0.128578\n",
       "3SsnPorch        0.044584\n",
       "ScreenPorch      0.111447\n",
       "PoolArea         0.092404\n",
       "MiscVal         -0.021190\n",
       "MoSold           0.046432\n",
       "YrSold          -0.028923\n",
       "SalePrice        1.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.corr()['SalePrice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tlDsrwtrS7LN"
   },
   "source": [
    "# Description study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "irrwRaMHScke"
   },
   "outputs": [],
   "source": [
    "# Column: - 'Alley': Type of alley access to property\n",
    "#            NA \tNo alley access\n",
    "#       Here, in Alley column there is NA means Np Alley access, \n",
    "#       here NA doesn't means missing value, So, Alley column need to hadle carefuly \n",
    "\n",
    "# Column: 'BsmtQual': Evaluates the height of the basement\n",
    "#       NA\tNo Basement\n",
    "\n",
    "# Column: 'BsmtCond': Evaluates the general condition of the basement'\n",
    "#       NA\tNo Basement\n",
    "\n",
    "# Column: 'BsmtExposure': Refers to walkout or garden level walls'\n",
    "#       NA\tNo Basement\n",
    "\n",
    "# Column: 'BsmtFinType1': Rating of basement finished area'\n",
    "#       NA\tNo Basement\n",
    "\n",
    "# Column: 'BsmtFinType2': Rating of basement finished area (if multiple types)'\n",
    "#       NA\tNo Basement\n",
    "\n",
    "# Column: 'FireplaceQu': Fireplace quality\n",
    "#       NA\tNo Fireplace\n",
    "\n",
    "# Column: 'GarageType': Garage location\n",
    "#       NA\tNo Garage\n",
    "\n",
    "# Column: 'GarageFinish': Interior finish of the garage\n",
    "#       NA\tNo Garage\n",
    "\n",
    "# Column: 'GarageQual': Garage quality\n",
    "#       NA\tNo Garage\n",
    "\n",
    "# Column: 'GarageCond': Garage condition\n",
    "#       NA\tNo Garage\n",
    "\n",
    "# Column: 'PoolQC': Pool quality\n",
    "#       NA\tNo Pool\n",
    "\n",
    "# Column: 'Fence': Fence quality\n",
    "#       NA\tNo Fence\n",
    "\n",
    "# Column: 'MiscFeature': Miscellaneous feature not covered in other categories\n",
    "#       NA\tNone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nCLep5a07UXz"
   },
   "outputs": [],
   "source": [
    "# columns with NA, but it doesn't means missing value\n",
    "# 'Alley', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature'  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fJJowyg4TIdg"
   },
   "source": [
    "# Imputaion - Handelling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WOWdKCico12v"
   },
   "outputs": [],
   "source": [
    "# columns with actual missing values\n",
    "\n",
    "missing_val_train_Columns = ['LotFrontage', 'MasVnrType', 'MasVnrArea', 'Electrical', 'GarageYrBlt'] \n",
    "missing_val_test_Columns = ['LotFrontage', 'MasVnrType', 'MasVnrArea', 'Electrical', 'GarageYrBlt', 'Utilities', 'MSZoning', 'Exterior1st', 'Exterior2nd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'KitchenQual', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'Functional', 'GarageCars', 'GarageArea', 'SaleType']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EJu3Tk5ryQVc"
   },
   "outputs": [],
   "source": [
    "# all missing columns\n",
    "\n",
    "missing_val_train_Columns1 = list(df_train.isna().sum()[df_train.isna().sum()>0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Cns6QPrZ0vmj",
    "outputId": "31ae5dd6-50d5-4e49-85fe-d480886901a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage',\n",
       " 'Alley',\n",
       " 'MasVnrType',\n",
       " 'MasVnrArea',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'Electrical',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageYrBlt',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature']"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_train_Columns1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FiHcZHUz14a_"
   },
   "outputs": [],
   "source": [
    "missing_val_test_Columns1 = list(df_test.isna().sum()[df_test.isna().sum()>0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fZe5WLGS5cLj",
    "outputId": "aee63fd9-46e1-4af7-82da-95a0e2fb2374"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning',\n",
       " 'LotFrontage',\n",
       " 'Alley',\n",
       " 'Utilities',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'MasVnrType',\n",
       " 'MasVnrArea',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinType2',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'KitchenQual',\n",
       " 'Functional',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageYrBlt',\n",
       " 'GarageFinish',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature',\n",
       " 'SaleType']"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_test_Columns1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YooIGiWk52Go",
    "outputId": "d84f8bed-0fd8-459c-87b6-21cbb686f19a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature']"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns includes NA, but it doesn't means missing value\n",
    "\n",
    "missing_val_train_Columns2 = [col for col in missing_val_train_Columns1 if not col in missing_val_train_Columns]\n",
    "missing_val_train_Columns2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AEuvilP07CVz",
    "outputId": "78159bf4-4b87-47b3-fc83-b8d651ab4389"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature']"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_test_Columns2 = [col for col in missing_val_test_Columns1 if not col in missing_val_test_Columns]\n",
    "missing_val_test_Columns2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wv6ncCKZ7EWM",
    "outputId": "b5900ce9-cf96-4af2-d2d8-4b4a8de4818c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missing_val_train_Columns2), len(missing_val_test_Columns2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "41Z8VTU7_EMv",
    "outputId": "0d1b62f3-ab55-4717-94a1-892a3d673f06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrType', 'MasVnrArea', 'Electrical', 'GarageYrBlt']"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_train_Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XExxq-1GfCXy",
    "outputId": "b6acce00-d5ab-4613-e4c4-730d9b603f44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage',\n",
       " 'MasVnrType',\n",
       " 'MasVnrArea',\n",
       " 'Electrical',\n",
       " 'GarageYrBlt',\n",
       " 'Utilities',\n",
       " 'MSZoning',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'KitchenQual',\n",
       " 'TotalBsmtSF',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'Functional',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'SaleType']"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_test_Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Puqm21ygCs5E",
    "outputId": "da5b1906-525b-46c0-b68c-1d8011db7462"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   LotFrontage  1201 non-null   float64\n",
      " 1   MasVnrType   1452 non-null   object \n",
      " 2   MasVnrArea   1452 non-null   float64\n",
      " 3   Electrical   1459 non-null   object \n",
      " 4   GarageYrBlt  1379 non-null   float64\n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 57.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train[missing_val_train_Columns].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aKlinT9BfEcK",
    "outputId": "b7e15866-98a7-4fda-ff76-57657c170ccb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 20 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   LotFrontage   1232 non-null   float64\n",
      " 1   MasVnrType    1443 non-null   object \n",
      " 2   MasVnrArea    1444 non-null   float64\n",
      " 3   Electrical    1459 non-null   object \n",
      " 4   GarageYrBlt   1381 non-null   float64\n",
      " 5   Utilities     1457 non-null   object \n",
      " 6   MSZoning      1455 non-null   object \n",
      " 7   Exterior1st   1458 non-null   object \n",
      " 8   Exterior2nd   1458 non-null   object \n",
      " 9   BsmtFinSF1    1458 non-null   float64\n",
      " 10  BsmtFinSF2    1458 non-null   float64\n",
      " 11  BsmtUnfSF     1458 non-null   float64\n",
      " 12  KitchenQual   1458 non-null   object \n",
      " 13  TotalBsmtSF   1458 non-null   float64\n",
      " 14  BsmtFullBath  1457 non-null   float64\n",
      " 15  BsmtHalfBath  1457 non-null   float64\n",
      " 16  Functional    1457 non-null   object \n",
      " 17  GarageCars    1458 non-null   float64\n",
      " 18  GarageArea    1458 non-null   float64\n",
      " 19  SaleType      1458 non-null   object \n",
      "dtypes: float64(11), object(9)\n",
      "memory usage: 228.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test[missing_val_test_Columns].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UdFnxNl7St9K"
   },
   "outputs": [],
   "source": [
    "temp_obj_train = list(df_train[missing_val_train_Columns].select_dtypes(include='O'))\n",
    "temp_obj_test = list(df_test[missing_val_test_Columns].select_dtypes(include='O'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MgAuKghyUXBB",
    "outputId": "9b0012d9-2211-4d74-dc91-f878e19a36f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MasVnrType', 'Electrical']"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_obj_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nq8rcm_Uf4L5",
    "outputId": "261ed064-5dd0-46ab-9280-d9c1c4ed38ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MasVnrType',\n",
       " 'Electrical',\n",
       " 'Utilities',\n",
       " 'MSZoning',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'KitchenQual',\n",
       " 'Functional',\n",
       " 'SaleType']"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_obj_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gMiKm3WFUXT5"
   },
   "outputs": [],
   "source": [
    "temp_intflt_train = list(df_train[missing_val_train_Columns].select_dtypes(exclude='O'))\n",
    "temp_intflt_test = list(df_test[missing_val_test_Columns].select_dtypes(exclude='O'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mP_XFxRxVagf",
    "outputId": "9ae1d24f-2427-4009-f7b8-0efd9f923ecd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_intflt_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LaqEhlycgGwa",
    "outputId": "372ef9a9-76cc-40d0-80dd-0b7323344b3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage',\n",
       " 'MasVnrArea',\n",
       " 'GarageYrBlt',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'GarageCars',\n",
       " 'GarageArea']"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_intflt_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "98W-IRGdVaj0"
   },
   "outputs": [],
   "source": [
    "for col in temp_obj_train:\n",
    "  df_train[col].fillna(df_train[col].mode().iloc[0], inplace = True)\n",
    "  df_test[col].fillna(df_train[col].mode().iloc[0], inplace = True)\n",
    "\n",
    "for col in temp_obj_test:\n",
    "  df_test[col].fillna(df_train[col].mode().iloc[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x66opjgcWUWU"
   },
   "outputs": [],
   "source": [
    "for col in temp_intflt_train:\n",
    "  df_train[col].fillna(df_train[col].median(), inplace = True)\n",
    "  df_test[col].fillna(df_train[col].median(), inplace = True)\n",
    "\n",
    "for col in temp_intflt_test:\n",
    "  df_test[col].fillna(df_train[col].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fLPccf-K4GZ4"
   },
   "outputs": [],
   "source": [
    "# Checking of whether handelling of missing values are done or not in [missing_val_train_Columns] and [missing_val_test_Columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uYubiziSbOQ4",
    "outputId": "b9ebde94-648d-4e5d-ecdf-0584e236953c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   LotFrontage  1460 non-null   float64\n",
      " 1   MasVnrType   1460 non-null   object \n",
      " 2   MasVnrArea   1460 non-null   float64\n",
      " 3   Electrical   1460 non-null   object \n",
      " 4   GarageYrBlt  1460 non-null   float64\n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 57.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train[missing_val_train_Columns].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HVIMw9sVbOT9",
    "outputId": "b80b21fc-09b8-4964-c244-8614da2837aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   LotFrontage  1459 non-null   float64\n",
      " 1   MasVnrType   1459 non-null   object \n",
      " 2   MasVnrArea   1459 non-null   float64\n",
      " 3   Electrical   1459 non-null   object \n",
      " 4   GarageYrBlt  1459 non-null   float64\n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 57.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test[missing_val_train_Columns].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LFybo6M-iaml",
    "outputId": "a602e898-392a-4a7f-9caf-81934bf52f2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 20 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   LotFrontage   1459 non-null   float64\n",
      " 1   MasVnrType    1459 non-null   object \n",
      " 2   MasVnrArea    1459 non-null   float64\n",
      " 3   Electrical    1459 non-null   object \n",
      " 4   GarageYrBlt   1459 non-null   float64\n",
      " 5   Utilities     1459 non-null   object \n",
      " 6   MSZoning      1459 non-null   object \n",
      " 7   Exterior1st   1459 non-null   object \n",
      " 8   Exterior2nd   1459 non-null   object \n",
      " 9   BsmtFinSF1    1459 non-null   float64\n",
      " 10  BsmtFinSF2    1459 non-null   float64\n",
      " 11  BsmtUnfSF     1459 non-null   float64\n",
      " 12  KitchenQual   1459 non-null   object \n",
      " 13  TotalBsmtSF   1459 non-null   float64\n",
      " 14  BsmtFullBath  1459 non-null   float64\n",
      " 15  BsmtHalfBath  1459 non-null   float64\n",
      " 16  Functional    1459 non-null   object \n",
      " 17  GarageCars    1459 non-null   float64\n",
      " 18  GarageArea    1459 non-null   float64\n",
      " 19  SaleType      1459 non-null   object \n",
      "dtypes: float64(11), object(9)\n",
      "memory usage: 228.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test[missing_val_test_Columns].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AsuHO34TbOXS",
    "outputId": "1583c06d-c22c-424c-9782-ea5fca04e992"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_test.isna().sum()>0), sum(df_train.isna().sum()>0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_fwdrXLAjgQg"
   },
   "source": [
    "# columns with NA as category, i.e. NA is not missing value\n",
    "# replacing these NA by some string lets say 'ABCD'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lUNJpb_XQGwh",
    "outputId": "3d56d1f6-2fdf-4f8b-9f7c-2ebdcc528966"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature']"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_train_Columns2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iai0CLLjXxbQ",
    "outputId": "e4b70918-732d-4997-d089-fed218863bc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alley',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature']"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_test_Columns2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2zbWf8E3X0kG",
    "outputId": "81e1bf69-9fb2-4516-c611-3dab9b782ec1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Alley         91 non-null     object\n",
      " 1   BsmtQual      1423 non-null   object\n",
      " 2   BsmtCond      1423 non-null   object\n",
      " 3   BsmtExposure  1422 non-null   object\n",
      " 4   BsmtFinType1  1423 non-null   object\n",
      " 5   BsmtFinType2  1422 non-null   object\n",
      " 6   FireplaceQu   770 non-null    object\n",
      " 7   GarageType    1379 non-null   object\n",
      " 8   GarageFinish  1379 non-null   object\n",
      " 9   GarageQual    1379 non-null   object\n",
      " 10  GarageCond    1379 non-null   object\n",
      " 11  PoolQC        7 non-null      object\n",
      " 12  Fence         281 non-null    object\n",
      " 13  MiscFeature   54 non-null     object\n",
      "dtypes: object(14)\n",
      "memory usage: 159.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train[missing_val_train_Columns2].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KVLP8nlJX7wh",
    "outputId": "80218f22-d792-4b7e-d729-94d80194e5e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Alley         107 non-null    object\n",
      " 1   BsmtQual      1415 non-null   object\n",
      " 2   BsmtCond      1414 non-null   object\n",
      " 3   BsmtExposure  1415 non-null   object\n",
      " 4   BsmtFinType1  1417 non-null   object\n",
      " 5   BsmtFinType2  1417 non-null   object\n",
      " 6   FireplaceQu   729 non-null    object\n",
      " 7   GarageType    1383 non-null   object\n",
      " 8   GarageFinish  1381 non-null   object\n",
      " 9   GarageQual    1381 non-null   object\n",
      " 10  GarageCond    1381 non-null   object\n",
      " 11  PoolQC        3 non-null      object\n",
      " 12  Fence         290 non-null    object\n",
      " 13  MiscFeature   51 non-null     object\n",
      "dtypes: object(14)\n",
      "memory usage: 159.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test[missing_val_test_Columns2].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zoRmtATtYAr2",
    "outputId": "9e816bec-3770-478c-c39d-d3e932eb5379"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Alley'].dtype=='O'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cfwvDlCCYkl_",
    "outputId": "4d3827ee-f19d-4cc1-9a43-dfbdf962aa1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grvl    50\n",
       "Pave    41\n",
       "Name: Alley, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Alley'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-VmTa8YBxnEA"
   },
   "outputs": [],
   "source": [
    "# following code not work\n",
    "# df_train.replace('NA', 'ABCD', inplace=True)\n",
    "# df_test.replace('NA', 'ABCD', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FKjLFTyJdETs"
   },
   "outputs": [],
   "source": [
    "df_train.replace(to_replace= np.nan, value = 'ABCD', inplace = True)\n",
    "df_test.replace(to_replace= np.nan, value = 'ABCD', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yYmn86IH08GP",
    "outputId": "0d9ee0fb-0930-4a9a-b822-0facde319b02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_train.isna().sum()), sum(df_test.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5KmiL95U1Rbl"
   },
   "source": [
    "# handelling missing value done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sP5NdjVB1fjg"
   },
   "source": [
    "# Feature Encoding: Encoding of categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j5_S9Xp1ivNS",
    "outputId": "c0a9b1e3-c745-49ef-a145-3e47eb214baf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
       "       'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.select_dtypes(include = 'O').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z1YW_s-Pn9S2"
   },
   "outputs": [],
   "source": [
    "all_cat_columns = list(df_train.select_dtypes(include = 'O').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rCstg3kq2AKW",
    "outputId": "05303fe7-7938-4357-c496-d097e40cf5b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
       "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
       "       'SaleType', 'SaleCondition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.select_dtypes(include = 'O').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yb4TwmxlhWfZ",
    "outputId": "f4b94f0a-f04f-4c22-b515-5ea9f4d1f436"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_cat_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cYAphOqLki2b"
   },
   "outputs": [],
   "source": [
    "# Encoding needed for columns:\n",
    "\n",
    "# columns with Nominal data: No Relation: eg. blood groups - A+, B, AB, O\n",
    "# one_hot_enc_columns = ['MSZoning', \"Street\", 'Alley', 'LandContour', 'LotConfig', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MiscFeature', 'SaleCondition', 'Foundation', 'Heating']\n",
    "\n",
    "# Columns with ordinal data: Relation: levels- high, mediam, low\n",
    "# label_enc_columns = ['LotShape', 'Utilities', 'LandSlope', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'CentralAir', 'Electrical', 'Functional', 'GarageType', 'GarageFinish', 'PavedDrive', 'Fence', 'SaleType']; different labels\n",
    "\n",
    "# label_enc_columns1 = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'HeatingQC', 'KitchenQual']; same labels: {'Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'}\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9cvR4CJMk04E"
   },
   "outputs": [],
   "source": [
    "one_hot_enc_columns = ['MSZoning', \"Street\", 'Alley', 'LandContour', 'LotConfig', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MiscFeature', 'SaleCondition', 'Foundation', 'Heating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GM_v7XBp3PTq"
   },
   "outputs": [],
   "source": [
    "# Trial of one_hot_encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PHQqe2X-rN2v",
    "outputId": "271ee770-6bd0-4d34-8a3e-2d05446ed10b"
   },
   "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": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['MSZoning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9-ZnGCczHXq7",
    "outputId": "c283a701-7d9e-488e-cb22-75e04cf7e09f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RL         1118\n",
       "RM          242\n",
       "FV           74\n",
       "C (all)      15\n",
       "RH           10\n",
       "Name: MSZoning, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['MSZoning'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "h7UDjwtscHux",
    "outputId": "a79b0df7-93b9-4b0e-c545-b14df54faa71"
   },
   "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>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
       "0               0            0            1            0\n",
       "1               0            0            1            0\n",
       "2               0            0            1            0\n",
       "3               0            0            1            0\n",
       "4               0            0            1            0\n",
       "...           ...          ...          ...          ...\n",
       "1455            0            0            1            0\n",
       "1456            0            0            1            0\n",
       "1457            0            0            1            0\n",
       "1458            0            0            1            0\n",
       "1459            0            0            1            0\n",
       "\n",
       "[1460 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame()\n",
    "temp1 = pd.get_dummies(df_train['MSZoning'], prefix='MSZoning', drop_first=True)\n",
    "pd.concat([temp, temp1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k_kxvJTkrN55",
    "outputId": "4b58c56f-535c-4ef1-e9bd-be94d03c9a29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      MSZoning_FV  MSZoning_RH  ...  Heating_OthW  Heating_Wall\n",
      "0               0            0  ...             0             0\n",
      "1               0            0  ...             0             0\n",
      "2               0            0  ...             0             0\n",
      "3               0            0  ...             0             0\n",
      "4               0            0  ...             0             0\n",
      "...           ...          ...  ...           ...           ...\n",
      "1455            0            0  ...             0             0\n",
      "1456            0            0  ...             0             0\n",
      "1457            0            0  ...             0             0\n",
      "1458            0            0  ...             0             0\n",
      "1459            0            0  ...             0             0\n",
      "\n",
      "[1460 rows x 127 columns]\n"
     ]
    }
   ],
   "source": [
    "one_hot_enc_train = pd.DataFrame()\n",
    "\n",
    "for col in one_hot_enc_columns:\n",
    "  temp1 = pd.get_dummies(df_train[col], prefix=col, drop_first=True)\n",
    "  one_hot_enc_train = pd.concat([one_hot_enc_train, temp1], axis=1)\n",
    "print(one_hot_enc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZR4voNPL-6Tz",
    "outputId": "af0315cb-801a-49e8-e584-456395001f2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      MSZoning_FV  MSZoning_RH  ...  Heating_Grav  Heating_Wall\n",
      "0               0            1  ...             0             0\n",
      "1               0            0  ...             0             0\n",
      "2               0            0  ...             0             0\n",
      "3               0            0  ...             0             0\n",
      "4               0            0  ...             0             0\n",
      "...           ...          ...  ...           ...           ...\n",
      "1454            0            0  ...             0             0\n",
      "1455            0            0  ...             0             0\n",
      "1456            0            0  ...             0             0\n",
      "1457            0            0  ...             0             0\n",
      "1458            0            0  ...             0             0\n",
      "\n",
      "[1459 rows x 113 columns]\n"
     ]
    }
   ],
   "source": [
    "one_hot_enc_test = pd.DataFrame()\n",
    "\n",
    "for col in one_hot_enc_columns:\n",
    "  temp1 = pd.get_dummies(df_test[col], prefix=col, drop_first=True)\n",
    "  one_hot_enc_test = pd.concat([one_hot_enc_test, temp1], axis=1)\n",
    "print(one_hot_enc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oMLzc1EnAFrd"
   },
   "outputs": [],
   "source": [
    "# columns to consider as X columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RslmzIR4KDvl"
   },
   "outputs": [],
   "source": [
    "temp = list(one_hot_enc_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dbsmd5hAKDye"
   },
   "outputs": [],
   "source": [
    "temp1 = list(one_hot_enc_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kX-nGlr1KD36"
   },
   "outputs": [],
   "source": [
    "columns_of_one_hot_enc = [col for col in temp if col in temp1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a_hH0k1gKD69",
    "outputId": "643db062-8a18-43a9-fd88-fb711a892c04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 63,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(columns_of_one_hot_enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dGUe-t2e-Qhn"
   },
   "outputs": [],
   "source": [
    "X_columns = list(df_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bWyHQEM03YUn",
    "outputId": "73edac52-91c0-4694-91ae-1f53962fe433"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NaSgot-Wfdo8"
   },
   "outputs": [],
   "source": [
    "X_columns = [col for col in X_columns if not col in one_hot_enc_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6FbZ8QUB2u4H",
    "outputId": "2983e673-e78a-41ac-a2b7-9b7112913e96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0qZ_W1c-2u7d"
   },
   "outputs": [],
   "source": [
    "X_columns.extend(columns_of_one_hot_enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Me0PaNOl2u91",
    "outputId": "2d24236f-f51f-43a3-969c-68283f6524ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w57XZ3J-QLnh"
   },
   "outputs": [],
   "source": [
    "# do later\n",
    "\n",
    "# df_train = pd.concat([df_train, one_hot_enc_train], axis=1)\n",
    "\n",
    "# df_test = pd.concat([df_test, one_hot_enc_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mD2AylpX2vBN"
   },
   "outputs": [],
   "source": [
    "# Columns with ordinal data: Relation: eg. levels- high, mediam, low\n",
    "\n",
    "# 1. different labels\n",
    "label_enc_columns = ['LotShape', 'Utilities', 'LandSlope', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'CentralAir', 'Electrical', 'Functional', 'GarageType', 'GarageFinish', 'PavedDrive', 'Fence', 'SaleType']\n",
    "\n",
    "# 2.same labels: {'Ex', 'Gd', 'TA', 'Fa', 'Po', 'NA'}\n",
    "label_enc_columns1 = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'HeatingQC', 'KitchenQual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "46qL8S032vH8",
    "outputId": "5b888433-4e73-42db-bb98-147f87a34200"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    1365\n",
       "N      95\n",
       "Name: CentralAir, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['CentralAir'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0M0_bhKjGFIl",
    "outputId": "5656abe2-76df-40da-8eca-ddcc33635150"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    1358\n",
       "N     101\n",
       "Name: CentralAir, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['CentralAir'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G7RXc4ZPudET"
   },
   "outputs": [],
   "source": [
    "# Label Encoding by applying lambda function\n",
    "\n",
    "df_train['LotShape_encod'] = df_train['LotShape'].apply(lambda X:3 if X=='Reg' else 2 if X=='IR1' else 1 if X=='IR2' else 0)\n",
    "df_test['LotShape_encod'] = df_test['LotShape'].apply(lambda X:3 if X=='Reg' else 2 if X=='IR1' else 1 if X=='IR2' else 0)\n",
    "\n",
    "df_train['Utilities_encod'] = df_train['Utilities'].apply(lambda X:3 if X=='AllPub' else 2 if X=='NoSewr' else 1 if X=='NoSeWa' else 0)\n",
    "df_test['Utilities_encod'] = df_test['Utilities'].apply(lambda X:3 if X=='AllPub' else 2 if X=='NoSewr' else 1 if X=='NoSeWa' else 0)\n",
    "\n",
    "df_train['LandSlope_encod'] = df_train['LandSlope'].apply(lambda X:2 if X=='Gtl' else 1 if X=='Mod' else 0)\n",
    "df_test['LandSlope_encod'] = df_test['LandSlope'].apply(lambda X:2 if X=='Gtl' else 1 if X=='Mod' else 0)\n",
    "\n",
    "df_train['BsmtExposure_encod'] = df_train['BsmtExposure'].apply(lambda X:4 if X=='Gd' else 3 if X=='Av' else 2 if X=='Mn' else 1 if X=='No' else 0)\n",
    "df_test['BsmtExposure_encod'] = df_test['BsmtExposure'].apply(lambda X:4 if X=='Gd' else 3 if X=='Av' else 2 if X=='Mn' else 1 if X=='No' else 0)\n",
    "\n",
    "df_train['BsmtFinType1_encod'] = df_train['BsmtFinType1'].apply(lambda X:4 if X=='GLQ' else 3 if X=='ALQ' else 2 if X=='BLQ' else 1 if X=='Rec' else 0)\n",
    "df_test['BsmtFinType1_encod'] = df_test['BsmtFinType1'].apply(lambda X:4 if X=='GLQ' else 3 if X=='ALQ' else 2 if X=='BLQ' else 1 if X=='Rec' else 0)\n",
    "\n",
    "df_train['BsmtFinType2_encod'] = df_train['BsmtFinType2'].apply(lambda X:4 if X=='GLQ' else 3 if X=='ALQ' else 2 if X=='BLQ' else 1 if X=='Rec' else 0)\n",
    "df_test['BsmtFinType2_encod'] = df_test['BsmtFinType2'].apply(lambda X:4 if X=='GLQ' else 3 if X=='ALQ' else 2 if X=='BLQ' else 1 if X=='Rec' else 0)\n",
    "\n",
    "df_train['CentralAir_encod'] = df_train['CentralAir'].apply(lambda X:1 if X=='Y' else 0)\n",
    "df_test['CentralAir_encod'] = df_test['CentralAir'].apply(lambda X:1 if X=='Y' else 0)\n",
    "\n",
    "df_train['Electrical_encod'] = df_train['Electrical'].apply(lambda X:4 if X=='SBrkr' else 3 if X=='FuseA' else 2 if X=='FuseF' else 1 if X=='FuseP' else 0)\n",
    "df_test['Electrical_encod'] = df_test['Electrical'].apply(lambda X:4 if X=='SBrkr' else 3 if X=='FuseA' else 2 if X=='FuseF' else 1 if X=='FuseP' else 0)\n",
    "\n",
    "df_train['Functional_encod'] = df_train['Functional'].apply(lambda X:5 if X=='Typ' else 4 if X=='Min1' else 3 if X=='Min2' else 2 if X=='Mod' else 1 if X=='Maj1' else 0)\n",
    "df_test['Functional_encod'] = df_test['Functional'].apply(lambda X:5 if X=='Typ' else 4 if X=='Min1' else 3 if X=='Min2' else 2 if X=='Mod' else 1 if X=='Maj1' else 0)\n",
    "\n",
    "df_train['GarageType_encod'] = df_train['GarageType'].apply(lambda X:5 if X=='2Types' else 4 if X=='Attchd' else 3 if X=='Basment' else 2 if X=='BuiltIn' else 1 if X=='CarPort' else 0)\n",
    "df_test['GarageType_encod'] = df_test['GarageType'].apply(lambda X:5 if X=='2Types' else 4 if X=='Attchd' else 3 if X=='Basment' else 2 if X=='BuiltIn' else 1 if X=='CarPort' else 0)\n",
    "\n",
    "df_train['GarageFinish_encod'] = df_train['GarageFinish'].apply(lambda X:3 if X=='RFn' else 2 if X=='RFn' else 1 if X=='Unf' else 0)\n",
    "df_test['GarageFinish_encod'] = df_test['GarageFinish'].apply(lambda X:3 if X=='RFn' else 2 if X=='RFn' else 1 if X=='Unf' else 0)\n",
    "\n",
    "df_train['PavedDrive_encod'] = df_train['PavedDrive'].apply(lambda X:2 if X=='Y' else 1 if X=='P' else 0)\n",
    "df_test['PavedDrive_encod'] = df_test['PavedDrive'].apply(lambda X:2 if X=='Y' else 1 if X=='P' else 0)\n",
    "\n",
    "df_train['Fence_encod'] = df_train['Fence'].apply(lambda X:4 if X=='GdPrv' else 3 if X=='MnPrv' else 2 if X=='GdWo' else 1 if X=='MnWw' else 0)\n",
    "df_test['Fence_encod'] = df_test['Fence'].apply(lambda X:4 if X=='GdPrv' else 3 if X=='MnPrv' else 2 if X=='GdWo' else 1 if X=='MnWw' else 0)\n",
    "\n",
    "df_train['SaleType_encod'] = df_train['SaleType'].apply(lambda X:4 if X=='WD' else 3 if X=='CWD' else 2 if X=='VWD' else 1 if X=='New' else 0)\n",
    "df_test['SaleType_encod'] = df_test['SaleType'].apply(lambda X:4 if X=='WD' else 3 if X=='CWD' else 2 if X=='VWD' else 1 if X=='New' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f1bbQyRhqrK5"
   },
   "outputs": [],
   "source": [
    "# removing label_enc_columns from X_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DlksUzfdqrNd",
    "outputId": "a54c65de-410c-452c-9b09-0d8b5e2fec61"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "161"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_columns=[col for col in X_columns if not col in label_enc_columns]\n",
    "len(X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6Hg7IBrRIPQk"
   },
   "outputs": [],
   "source": [
    "# adding '_encod' column names to the X_columns\n",
    "\n",
    "temp = ['LotShape_encod', 'Utilities_encod', 'LandSlope_encod', 'BsmtExposure_encod', 'BsmtFinType1_encod', 'BsmtFinType2_encod', 'CentralAir_encod', 'Electrical_encod', 'Functional_encod', 'GarageType_encod', 'GarageFinish_encod', 'PavedDrive_encod', 'Fence_encod', 'SaleType_encod']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gZYZ55aeIEQL"
   },
   "outputs": [],
   "source": [
    "X_columns.extend(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Eye-aZBHqrTD",
    "outputId": "fd0b5199-9f0a-404b-f885-f9f95bc36860"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HzPLn_J4jhQZ"
   },
   "outputs": [],
   "source": [
    "# Labelencoding of label_enc_columns1 by using map function:\n",
    "\n",
    "label_enc_columns1 = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'HeatingQC', 'KitchenQual']\n",
    "dict1 = {'Ex':5, 'Gd':4, 'TA':3, 'Fa':2, 'Po':1, 'ABCD':0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cD_-wF50oa1K"
   },
   "outputs": [],
   "source": [
    "for col in label_enc_columns1:\n",
    "  df_train[col] = df_train[col].map(dict1)\n",
    "  df_test[col] = df_test[col].map(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HXn2RNlERHKu",
    "outputId": "bfac080c-3bfc-4a0b-edbc-2afaf3199c7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    892\n",
       "4    491\n",
       "5     55\n",
       "2     21\n",
       "Name: ExterQual, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['ExterQual'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B5B3WN0ORHNy"
   },
   "outputs": [],
   "source": [
    "# label encoding of 'label_enc_columns1' done and it is replced in dataframe with in place of original columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JM5x2zYAAUj1"
   },
   "outputs": [],
   "source": [
    "df_train = pd.concat([df_train, one_hot_enc_train], axis=1)\n",
    "df_test = pd.concat([df_test, one_hot_enc_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6YHoqBilpVEe",
    "outputId": "eafc62be-6a0c-4624-cc72-6618ea37b019"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222"
      ]
     },
     "execution_count": 85,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df_train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y5ohnLI8phPi",
    "outputId": "4d9e1607-c273-4792-f0bb-e4d0632650c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 86,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df_test.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7ABeILisA7Gd",
    "outputId": "d2cc3b9e-75e4-4e76-99be-e4d1147f2667"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 87,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df_test.select_dtypes(include = 'O').columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FcaKMMvVTERw"
   },
   "source": [
    "# Feature Encoding Done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wxVlX6K9Td5l"
   },
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wpV3vEDqUn-u"
   },
   "outputs": [],
   "source": [
    "X_columns.remove('SalePrice')\n",
    "X_columns.remove('Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hsUFKQb4Dt9w",
    "outputId": "18e48371-a325-40f9-af37-f88aed478592"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 89,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "htnxkcZAP-SP"
   },
   "outputs": [],
   "source": [
    "temp = []\n",
    "\n",
    "for col in X_columns:\n",
    "  if (df_train[col].isna().sum()>0)==True:\n",
    "    temp1 = [col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kyIr1PkCUrXl",
    "outputId": "dbb36df4-f220-4014-bc43-512b91aaf0a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning_FV',\n",
       " 'MSZoning_RH',\n",
       " 'MSZoning_RL',\n",
       " 'MSZoning_RM',\n",
       " 'Street_Pave',\n",
       " 'Alley_Grvl',\n",
       " 'Alley_Pave',\n",
       " 'LandContour_HLS',\n",
       " 'LandContour_Low',\n",
       " 'LandContour_Lvl',\n",
       " 'LotConfig_CulDSac',\n",
       " 'LotConfig_FR2',\n",
       " 'LotConfig_FR3',\n",
       " 'LotConfig_Inside',\n",
       " 'Neighborhood_Blueste',\n",
       " 'Neighborhood_BrDale',\n",
       " 'Neighborhood_BrkSide',\n",
       " 'Neighborhood_ClearCr',\n",
       " 'Neighborhood_CollgCr',\n",
       " 'Neighborhood_Crawfor',\n",
       " 'Neighborhood_Edwards',\n",
       " 'Neighborhood_Gilbert',\n",
       " 'Neighborhood_IDOTRR',\n",
       " 'Neighborhood_MeadowV',\n",
       " 'Neighborhood_Mitchel',\n",
       " 'Neighborhood_NAmes',\n",
       " 'Neighborhood_NPkVill',\n",
       " 'Neighborhood_NWAmes',\n",
       " 'Neighborhood_NoRidge',\n",
       " 'Neighborhood_NridgHt',\n",
       " 'Neighborhood_OldTown',\n",
       " 'Neighborhood_SWISU',\n",
       " 'Neighborhood_Sawyer',\n",
       " 'Neighborhood_SawyerW',\n",
       " 'Neighborhood_Somerst',\n",
       " 'Neighborhood_StoneBr',\n",
       " 'Neighborhood_Timber',\n",
       " 'Neighborhood_Veenker',\n",
       " 'Condition1_Feedr',\n",
       " 'Condition1_Norm',\n",
       " 'Condition1_PosA',\n",
       " 'Condition1_PosN',\n",
       " 'Condition1_RRAe',\n",
       " 'Condition1_RRAn',\n",
       " 'Condition1_RRNe',\n",
       " 'Condition1_RRNn',\n",
       " 'Condition2_Feedr',\n",
       " 'Condition2_Norm',\n",
       " 'Condition2_PosA',\n",
       " 'Condition2_PosN',\n",
       " 'BldgType_2fmCon',\n",
       " 'BldgType_Duplex',\n",
       " 'BldgType_Twnhs',\n",
       " 'BldgType_TwnhsE',\n",
       " 'HouseStyle_1.5Unf',\n",
       " 'HouseStyle_1Story',\n",
       " 'HouseStyle_2.5Unf',\n",
       " 'HouseStyle_2Story',\n",
       " 'HouseStyle_SFoyer',\n",
       " 'HouseStyle_SLvl',\n",
       " 'RoofStyle_Gable',\n",
       " 'RoofStyle_Gambrel',\n",
       " 'RoofStyle_Hip',\n",
       " 'RoofStyle_Mansard',\n",
       " 'RoofStyle_Shed',\n",
       " 'RoofMatl_Tar&Grv',\n",
       " 'RoofMatl_WdShake',\n",
       " 'RoofMatl_WdShngl',\n",
       " 'Exterior1st_AsphShn',\n",
       " 'Exterior1st_BrkComm',\n",
       " 'Exterior1st_BrkFace',\n",
       " 'Exterior1st_CBlock',\n",
       " 'Exterior1st_CemntBd',\n",
       " 'Exterior1st_HdBoard',\n",
       " 'Exterior1st_MetalSd',\n",
       " 'Exterior1st_Plywood',\n",
       " 'Exterior1st_Stucco',\n",
       " 'Exterior1st_VinylSd',\n",
       " 'Exterior1st_Wd Sdng',\n",
       " 'Exterior1st_WdShing',\n",
       " 'Exterior2nd_AsphShn',\n",
       " 'Exterior2nd_Brk Cmn',\n",
       " 'Exterior2nd_BrkFace',\n",
       " 'Exterior2nd_CBlock',\n",
       " 'Exterior2nd_CmentBd',\n",
       " 'Exterior2nd_HdBoard',\n",
       " 'Exterior2nd_ImStucc',\n",
       " 'Exterior2nd_MetalSd',\n",
       " 'Exterior2nd_Plywood',\n",
       " 'Exterior2nd_Stone',\n",
       " 'Exterior2nd_Stucco',\n",
       " 'Exterior2nd_VinylSd',\n",
       " 'Exterior2nd_Wd Sdng',\n",
       " 'Exterior2nd_Wd Shng',\n",
       " 'MasVnrType_BrkFace',\n",
       " 'MasVnrType_None',\n",
       " 'MasVnrType_Stone',\n",
       " 'MiscFeature_Gar2',\n",
       " 'MiscFeature_Othr',\n",
       " 'MiscFeature_Shed',\n",
       " 'SaleCondition_AdjLand',\n",
       " 'SaleCondition_Alloca',\n",
       " 'SaleCondition_Family',\n",
       " 'SaleCondition_Normal',\n",
       " 'SaleCondition_Partial',\n",
       " 'Foundation_CBlock',\n",
       " 'Foundation_PConc',\n",
       " 'Foundation_Slab',\n",
       " 'Foundation_Stone',\n",
       " 'Foundation_Wood',\n",
       " 'Heating_GasW',\n",
       " 'Heating_Grav',\n",
       " 'Heating_Wall']"
      ]
     },
     "execution_count": 91,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aCAa3oAsYP3e"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cZTPm991YP6m"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MRo8lCEsUs6K"
   },
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9yJxu5xCUuhk",
    "outputId": "fea41090-dc71-48b8-edab-20a5fcfb6fb4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 92,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scalar = MinMaxScaler()\n",
    "scalar.fit(df_train[X_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JQ3c9SGXVpMv"
   },
   "outputs": [],
   "source": [
    "X_train_scaled = scalar.transform(df_train[X_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hoBR-0u3HKqG"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8BCCdRANHRQ8"
   },
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B78XqSuoHUIF",
    "outputId": "f92d5ea1-2438-4a68-ceb4-4febffb6212f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 96,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled, df_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5xXo-_kmYcL-",
    "outputId": "698bedc1-2753-4f2c-ffa6-d5bed9d6ab05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.893145707696248"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train_scaled, df_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O5flwWpJQklO"
   },
   "outputs": [],
   "source": [
    "X_test_scaled = scalar.transform(df_test[X_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kRoQyW25ZOBK",
    "outputId": "59a365b3-1ff1-4e3b-f352-4fc4518c25d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([104044.13942283, 127216.8640633 , 169333.36817936, ...,\n",
       "       149032.24714404, 102862.99476098, 224014.42151185])"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k8dAtyMQcgfu",
    "outputId": "0991d6da-4394-45b2-bfaa-cb9e7db78615"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1459"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AR28k5LEZz1b"
   },
   "outputs": [],
   "source": [
    "df_test['SalePrice'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "TB9THriUdeIa",
    "outputId": "877271d3-8983-4297-a988-0502141d90b9"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "    async function download(id, filename, size) {\n",
       "      if (!google.colab.kernel.accessAllowed) {\n",
       "        return;\n",
       "      }\n",
       "      const div = document.createElement('div');\n",
       "      const label = document.createElement('label');\n",
       "      label.textContent = `Downloading \"${filename}\": `;\n",
       "      div.appendChild(label);\n",
       "      const progress = document.createElement('progress');\n",
       "      progress.max = size;\n",
       "      div.appendChild(progress);\n",
       "      document.body.appendChild(div);\n",
       "\n",
       "      const buffers = [];\n",
       "      let downloaded = 0;\n",
       "\n",
       "      const channel = await google.colab.kernel.comms.open(id);\n",
       "      // Send a message to notify the kernel that we're ready.\n",
       "      channel.send({})\n",
       "\n",
       "      for await (const message of channel.messages) {\n",
       "        // Send a message to notify the kernel that we're ready.\n",
       "        channel.send({})\n",
       "        if (message.buffers) {\n",
       "          for (const buffer of message.buffers) {\n",
       "            buffers.push(buffer);\n",
       "            downloaded += buffer.byteLength;\n",
       "            progress.value = downloaded;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
       "      const a = document.createElement('a');\n",
       "      a.href = window.URL.createObjectURL(blob);\n",
       "      a.download = filename;\n",
       "      div.appendChild(a);\n",
       "      a.click();\n",
       "      div.remove();\n",
       "    }\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "download(\"download_81bdab2f-d084-43a3-b02d-e37e7a5db81b\", \"house_price_submission_LinReg.csv\", 34436)"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test[['Id', 'SalePrice']].to_csv('house_price_submission_LinReg.csv', index = False)\n",
    "from google.colab import files\n",
    "files.download(\"house_price_submission_LinReg.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YCN9r5u_cbFK"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of House Prices - Advanced Regression Techniques.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}