House_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About Data \n",
    "The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n",
    "\n",
    "With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n",
    "\n",
    "[link] (https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation\n",
    "\n",
    "Advanced regression techniques like random forest and gradient boostin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "#Importing Libraries for visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Setting Instances for visualization\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "\n",
    "#importing Library for warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading Both Train and Test Files\n",
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')\n",
    "\n",
    "#Displaying first five rows\n",
    "train_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape of Train Data\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **1460** rows and **81** columns in the **train_data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **1459** rows and **80** columns in the **test_data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# train dataframe info\n",
    "train_data.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      "Id               1459 non-null int64\n",
      "MSSubClass       1459 non-null int64\n",
      "MSZoning         1455 non-null object\n",
      "LotFrontage      1232 non-null float64\n",
      "LotArea          1459 non-null int64\n",
      "Street           1459 non-null object\n",
      "Alley            107 non-null object\n",
      "LotShape         1459 non-null object\n",
      "LandContour      1459 non-null object\n",
      "Utilities        1457 non-null object\n",
      "LotConfig        1459 non-null object\n",
      "LandSlope        1459 non-null object\n",
      "Neighborhood     1459 non-null object\n",
      "Condition1       1459 non-null object\n",
      "Condition2       1459 non-null object\n",
      "BldgType         1459 non-null object\n",
      "HouseStyle       1459 non-null object\n",
      "OverallQual      1459 non-null int64\n",
      "OverallCond      1459 non-null int64\n",
      "YearBuilt        1459 non-null int64\n",
      "YearRemodAdd     1459 non-null int64\n",
      "RoofStyle        1459 non-null object\n",
      "RoofMatl         1459 non-null object\n",
      "Exterior1st      1458 non-null object\n",
      "Exterior2nd      1458 non-null object\n",
      "MasVnrType       1443 non-null object\n",
      "MasVnrArea       1444 non-null float64\n",
      "ExterQual        1459 non-null object\n",
      "ExterCond        1459 non-null object\n",
      "Foundation       1459 non-null object\n",
      "BsmtQual         1415 non-null object\n",
      "BsmtCond         1414 non-null object\n",
      "BsmtExposure     1415 non-null object\n",
      "BsmtFinType1     1417 non-null object\n",
      "BsmtFinSF1       1458 non-null float64\n",
      "BsmtFinType2     1417 non-null object\n",
      "BsmtFinSF2       1458 non-null float64\n",
      "BsmtUnfSF        1458 non-null float64\n",
      "TotalBsmtSF      1458 non-null float64\n",
      "Heating          1459 non-null object\n",
      "HeatingQC        1459 non-null object\n",
      "CentralAir       1459 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1459 non-null int64\n",
      "2ndFlrSF         1459 non-null int64\n",
      "LowQualFinSF     1459 non-null int64\n",
      "GrLivArea        1459 non-null int64\n",
      "BsmtFullBath     1457 non-null float64\n",
      "BsmtHalfBath     1457 non-null float64\n",
      "FullBath         1459 non-null int64\n",
      "HalfBath         1459 non-null int64\n",
      "BedroomAbvGr     1459 non-null int64\n",
      "KitchenAbvGr     1459 non-null int64\n",
      "KitchenQual      1458 non-null object\n",
      "TotRmsAbvGrd     1459 non-null int64\n",
      "Functional       1457 non-null object\n",
      "Fireplaces       1459 non-null int64\n",
      "FireplaceQu      729 non-null object\n",
      "GarageType       1383 non-null object\n",
      "GarageYrBlt      1381 non-null float64\n",
      "GarageFinish     1381 non-null object\n",
      "GarageCars       1458 non-null float64\n",
      "GarageArea       1458 non-null float64\n",
      "GarageQual       1381 non-null object\n",
      "GarageCond       1381 non-null object\n",
      "PavedDrive       1459 non-null object\n",
      "WoodDeckSF       1459 non-null int64\n",
      "OpenPorchSF      1459 non-null int64\n",
      "EnclosedPorch    1459 non-null int64\n",
      "3SsnPorch        1459 non-null int64\n",
      "ScreenPorch      1459 non-null int64\n",
      "PoolArea         1459 non-null int64\n",
      "PoolQC           3 non-null object\n",
      "Fence            290 non-null object\n",
      "MiscFeature      51 non-null object\n",
      "MiscVal          1459 non-null int64\n",
      "MoSold           1459 non-null int64\n",
      "YrSold           1459 non-null int64\n",
      "SaleType         1458 non-null object\n",
      "SaleCondition    1459 non-null object\n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 912.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Fuction for dropping columns\n",
    "def  drop_null (data):\n",
    "    for column in data:\n",
    "        if data[column].count() / len(data) <= 0.3:\n",
    "            data.drop(column, axis=1, inplace=True)\n",
    "            print('Dropped Column', column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This fuction will eliminate all columns having more then **30%** null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped Column Alley\n",
      "Dropped Column PoolQC\n",
      "Dropped Column Fence\n",
      "Dropped Column MiscFeature\n",
      "(1460, 76)\n"
     ]
    }
   ],
   "source": [
    "#Calling function created for column having null values\n",
    "#for Train dataset\n",
    "drop_null(train_data)\n",
    "\n",
    "#dropping id\n",
    "train_data.drop('Id', axis=1,  inplace=True)\n",
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped Column Alley\n",
      "Dropped Column PoolQC\n",
      "Dropped Column Fence\n",
      "Dropped Column MiscFeature\n",
      "(1459, 75)\n"
     ]
    }
   ],
   "source": [
    "#Calling function created for column having null values\n",
    "#for Train dataset\n",
    "drop_null(test_data)\n",
    "\n",
    "#dropping id\n",
    "test_data.drop('Id', axis=1,  inplace=True)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above column had more then **30** missing values hence are removed from the both the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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 × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
       "1          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
       "2          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
       "3          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
       "4          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
       "\n",
       "  Utilities LotConfig LandSlope    ...    EnclosedPorch 3SsnPorch ScreenPorch  \\\n",
       "0    AllPub    Inside       Gtl    ...                0         0           0   \n",
       "1    AllPub       FR2       Gtl    ...                0         0           0   \n",
       "2    AllPub    Inside       Gtl    ...                0         0           0   \n",
       "3    AllPub    Corner       Gtl    ...              272         0           0   \n",
       "4    AllPub       FR2       Gtl    ...                0         0           0   \n",
       "\n",
       "  PoolArea MiscVal  MoSold  YrSold  SaleType  SaleCondition SalePrice  \n",
       "0        0       0       2    2008        WD         Normal    208500  \n",
       "1        0       0       5    2007        WD         Normal    181500  \n",
       "2        0       0       9    2008        WD         Normal    223500  \n",
       "3        0       0       2    2006        WD        Abnorml    140000  \n",
       "4        0       0      12    2008        WD         Normal    250000  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dealing with null values\n",
    "\n",
    "Defining Categorical and Numerical columns and also defining conditions to fill null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing others missing data from train data\n",
    "missing_data_stats = train_data.isnull().sum()\n",
    "cols = missing_data_stats[missing_data_stats>0].index.tolist()\n",
    "cat_cols = train_data.select_dtypes(exclude=['int64', 'float64']).columns\n",
    "\n",
    "\n",
    "for c in cols:\n",
    "    if c in cat_cols:\n",
    "        mode = train_data[c].mode()[0]\n",
    "        train_data[c] = train_data[c].fillna(mode)\n",
    "    else:\n",
    "        median = train_data[c].median()\n",
    "        train_data[c] = train_data[c].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing others missing data from test data\n",
    "missing_data_stats = test_data.isnull().sum()\n",
    "cols = missing_data_stats[missing_data_stats>0].index.tolist()\n",
    "cat_cols = test_data.select_dtypes(exclude=['int64', 'float64']).columns\n",
    "\n",
    "\n",
    "for c in cols:\n",
    "    if c in cat_cols:\n",
    "        mode = test_data[c].mode()[0]\n",
    "        test_data[c] = test_data[c].fillna(mode)\n",
    "    else:\n",
    "        median = test_data[c].median()\n",
    "        test_data[c] = test_data[c].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass       0\n",
       "MSZoning         0\n",
       "LotFrontage      0\n",
       "LotArea          0\n",
       "Street           0\n",
       "LotShape         0\n",
       "LandContour      0\n",
       "Utilities        0\n",
       "LotConfig        0\n",
       "LandSlope        0\n",
       "Neighborhood     0\n",
       "Condition1       0\n",
       "Condition2       0\n",
       "BldgType         0\n",
       "HouseStyle       0\n",
       "OverallQual      0\n",
       "OverallCond      0\n",
       "YearBuilt        0\n",
       "YearRemodAdd     0\n",
       "RoofStyle        0\n",
       "RoofMatl         0\n",
       "Exterior1st      0\n",
       "Exterior2nd      0\n",
       "MasVnrType       0\n",
       "MasVnrArea       0\n",
       "ExterQual        0\n",
       "ExterCond        0\n",
       "Foundation       0\n",
       "BsmtQual         0\n",
       "BsmtCond         0\n",
       "                ..\n",
       "BsmtHalfBath     0\n",
       "FullBath         0\n",
       "HalfBath         0\n",
       "BedroomAbvGr     0\n",
       "KitchenAbvGr     0\n",
       "KitchenQual      0\n",
       "TotRmsAbvGrd     0\n",
       "Functional       0\n",
       "Fireplaces       0\n",
       "FireplaceQu      0\n",
       "GarageType       0\n",
       "GarageYrBlt      0\n",
       "GarageFinish     0\n",
       "GarageCars       0\n",
       "GarageArea       0\n",
       "GarageQual       0\n",
       "GarageCond       0\n",
       "PavedDrive       0\n",
       "WoodDeckSF       0\n",
       "OpenPorchSF      0\n",
       "EnclosedPorch    0\n",
       "3SsnPorch        0\n",
       "ScreenPorch      0\n",
       "PoolArea         0\n",
       "MiscVal          0\n",
       "MoSold           0\n",
       "YrSold           0\n",
       "SaleType         0\n",
       "SaleCondition    0\n",
       "SalePrice        0\n",
       "Length: 76, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass       0\n",
       "MSZoning         0\n",
       "LotFrontage      0\n",
       "LotArea          0\n",
       "Street           0\n",
       "LotShape         0\n",
       "LandContour      0\n",
       "Utilities        0\n",
       "LotConfig        0\n",
       "LandSlope        0\n",
       "Neighborhood     0\n",
       "Condition1       0\n",
       "Condition2       0\n",
       "BldgType         0\n",
       "HouseStyle       0\n",
       "OverallQual      0\n",
       "OverallCond      0\n",
       "YearBuilt        0\n",
       "YearRemodAdd     0\n",
       "RoofStyle        0\n",
       "RoofMatl         0\n",
       "Exterior1st      0\n",
       "Exterior2nd      0\n",
       "MasVnrType       0\n",
       "MasVnrArea       0\n",
       "ExterQual        0\n",
       "ExterCond        0\n",
       "Foundation       0\n",
       "BsmtQual         0\n",
       "BsmtCond         0\n",
       "                ..\n",
       "BsmtFullBath     0\n",
       "BsmtHalfBath     0\n",
       "FullBath         0\n",
       "HalfBath         0\n",
       "BedroomAbvGr     0\n",
       "KitchenAbvGr     0\n",
       "KitchenQual      0\n",
       "TotRmsAbvGrd     0\n",
       "Functional       0\n",
       "Fireplaces       0\n",
       "FireplaceQu      0\n",
       "GarageType       0\n",
       "GarageYrBlt      0\n",
       "GarageFinish     0\n",
       "GarageCars       0\n",
       "GarageArea       0\n",
       "GarageQual       0\n",
       "GarageCond       0\n",
       "PavedDrive       0\n",
       "WoodDeckSF       0\n",
       "OpenPorchSF      0\n",
       "EnclosedPorch    0\n",
       "3SsnPorch        0\n",
       "ScreenPorch      0\n",
       "PoolArea         0\n",
       "MiscVal          0\n",
       "MoSold           0\n",
       "YrSold           0\n",
       "SaleType         0\n",
       "SaleCondition    0\n",
       "Length: 75, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.897260</td>\n",
       "      <td>69.863699</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.117123</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>46.549315</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.300571</td>\n",
       "      <td>22.027677</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>180.731373</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>161.319273</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1474.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage        LotArea  OverallQual  OverallCond  \\\n",
       "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     56.897260    69.863699   10516.828082     6.099315     5.575342   \n",
       "std      42.300571    22.027677    9981.264932     1.382997     1.112799   \n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    60.000000    7553.500000     5.000000     5.000000   \n",
       "50%      50.000000    69.000000    9478.500000     6.000000     5.000000   \n",
       "75%      70.000000    79.000000   11601.500000     7.000000     6.000000   \n",
       "max     190.000000   313.000000  215245.000000    10.000000     9.000000   \n",
       "\n",
       "         YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2  \\\n",
       "count  1460.000000   1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean   1971.267808   1984.865753   103.117123   443.639726    46.549315   \n",
       "std      30.202904     20.645407   180.731373   456.098091   161.319273   \n",
       "min    1872.000000   1950.000000     0.000000     0.000000     0.000000   \n",
       "25%    1954.000000   1967.000000     0.000000     0.000000     0.000000   \n",
       "50%    1973.000000   1994.000000     0.000000   383.500000     0.000000   \n",
       "75%    2000.000000   2004.000000   164.250000   712.250000     0.000000   \n",
       "max    2010.000000   2010.000000  1600.000000  5644.000000  1474.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    1460.000000  \n",
       "mean   180921.195890  \n",
       "std     79442.502883  \n",
       "min     34900.000000  \n",
       "25%    129975.000000  \n",
       "50%    163000.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "\n",
    "I only used train dataSet for visualization and EDA exploration............."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above are the **Descriptive Statistics** of all the numerical values columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      1460.000000\n",
      "mean     180921.195890\n",
      "std       79442.502883\n",
      "min       34900.000000\n",
      "25%      129975.000000\n",
      "50%      163000.000000\n",
      "75%      214000.000000\n",
      "max      755000.000000\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train_data['SalePrice'].describe())\n",
    "plt.figure(figsize=(9, 8))\n",
    "sns.distplot(train_data['SalePrice'], color='g', bins=100, hist_kws={'alpha': 0.8});\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.boxplot(y='SalePrice', x='OverallQual', data=train_data)\n",
    "plt.title('Sales Price comaparison with Overall Qality of house')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wYMFCu8yS1IEMzZIkdSGv2CxJ0tQYmiVJ6kJesVmSpKkxNEuS1IW8YrMkSVNjaJYkqQt5xWZJkqamt+gCJElSMSqVQdasuccusyRJkyiNXTlTAOwD3Ll27SOMjnpcJEmSJGl70dNTYubMHQH2Be6a8nbtKkiSJEmSpG2doVmSJEmSpBYMzZIkSZIktWBoliRJkiSpBUOzJEmSJEktGJolSZIkSWrB0CxJkiRJUguGZkmSJEmSWjA0S5IkSZLUgqFZkiRJkqQWDM2SJEmSJLVgaJYkSZIkqQVDsyRJkiRJLRiaJUmSJElqwdAsSZIkSVILve3acUrp7cAZTUP7Av8GfA34NDAduCIizsrXPwi4CNgJWA6cFhH1lNLewKXAbkAA8yPikZRSGbgM2A94AJgXEfenlJ4DfBF4FbAeOCkiVrbrdUqSJEmStl9t6zRHxEURcVBEHATMB34B/D1wMVABDgAOSSmdkG9yKXBGRMwBSsA78vELgAsiYn/gZuDsfPwTQDUiDgC+AJyXj78PeDQfPxNY3K7XKEmSJEnavm2t07M/Bywg6wrfERF3RkSdLCi/MaU0G5geETfm6y/Ox3cAjgS+0jyeP349WacZ4MvACfn6G8cjYjmwa96tliRJkiTpaWnb6dljUkrHkQXi/0gp/SlwX9OX7wP2AvZoMT4LeDgP2M3jNG+Tn8b9MLDrJPu6e6o1z5y541RXlSRJkiRtx9oemoF3kc1hhqyz3Wj6WgkYfRrj5ONj6zTb3L6mbO3aRxgdHf+0kiRJkqRtVU9P6Rk1SNt6enZ+Ua6jgK/nQ/cAuzet8mLg3knGfwHsnFKalo/vno8DrMnXI6XUC7wQWDvJviRJkiRJelraPaf55cCqiHg0X/4ekFJKL8mD8EnA1RGxGng8pXREvt5b8vEngSrwpnz8rcDV+eOr8mXyr1fz9TeOp5TmAo9HxJRPzZYkSZIkaUy7Q/N+ZJ1fACLiceAUYAi4HVjJby7yNR/4TEppJbAjcH4+fjrwzpTS7UA/cFY+fjZwWErptnyd9+TjnwWem4+fTxbAJUmSJEl62kqNhnN3m+wD3OmcZkmSJEnavjTNad4XuGvK27WrIEmSJEmStnWGZkmSJEmSWjA0S5IkSZLUgqFZkiRJkqQWDM2SJEmSJLVgaJYkSZIkqQVDsyRJkiRJLRiaJUmSJElqwdAsSZIkSVILhmZJkiRJklowNEuSJEmS1IKhWZIkSZKkFgzNkiRJkiS1YGiWJEmSJKkFQ7MkSZIkSS0YmiVJkiRJasHQLEmSJElSC4ZmSZIkSZJaMDRLkiRJktSCoVmSJEmSpBYMzZIkSZIktWBoliRJkiSpBUOzJEmSJEktGJolSZIkSWrB0CxJkiRJUguGZkmSJEmSWjA0S5IkSZLUgqFZkiRJkqQWDM2SJEmSJLVgaJYkSZIkqQVDsyRJkiRJLRiaJUmSJElqwdAsSVKXqtXWsWjRQmq1WtGlSJLUsQzNkiR1qeHhIVatWsnw8FDRpUiS1LEMzZIkdaFabR3V6jIajQbV6jK7zZIktWBoliSpCw0PD9FoNABoNEbtNkuS1IKhWZKkLjQysoJ6vQ5AvV5nZKRacEWSJHUmQ7MkSV2or28uvb29APT29tLX119wRZIkdSZDsyRJXahSGaRUKgFQKvVQqQwWXJEkSZ3J0CxJUhcql2dw6KGHA3DooYdTLpcLrkiSpM5kaJYkSZIkqQVDsyRJXahWW8dNN90AwE033eAtpyRJasHQLElSF/KWU5IkTY2hWZKkLuQtpyRJmhpDsyRJXchbTkmSNDWGZkmSupC3nJIkaWp627nzlNIfAh8FXgAsiYj3p5SOAz4NTAeuiIiz8nUPAi4CdgKWA6dFRD2ltDdwKbAbEMD8iHgkpVQGLgP2Ax4A5kXE/Sml5wBfBF4FrAdOioiV7XydkiRta8rlGfT3D7B06XX09w94yylJklpoW6c5pbQfcCHwBuDlwCtTSicAFwMV4ADgkHwMsmB8RkTMAUrAO/LxC4ALImJ/4Gbg7Hz8E0A1Ig4AvgCcl4+/D3g0Hz8TWNyu1yhJ0rasUhlkzpz97TJLkjSJdp6e/UdkneR7IuJJ4E3AY8AdEXFnRNTJgvIbU0qzgekRcWO+7eJ8fAfgSOArzeP549eTdZoBvgyckK+/cTwilgO75t1qSZLUpFyewYIFC+0yS5I0iXaenv0S4ImU0teBvYFvArcB9zWtcx+wF7BHi/FZwMN5wG4ep3mb/DTuh4FdJ9nX3VMtfObMHae6qiRJkiRpO9bO0NxL1iUeAB4Bvk42x7jRtE4JGCXreE9lnHx8bJ1mm9vXlK1d+wijo+OfVpIkSZK0rerpKT2jBmk7T8++H7guIh6IiPXAV4HjgN2b1nkxcC9wT4vxXwA7p5Sm5eO75+MAa/L1SCn1Ai8E1k6yL0mSJEmSnpZ2huZvAq9NKZXz0HsC2dzklFJ6ST52EnB1RKwGHk8pHZFv+5Z8/EmgSjYfGuCtwNX546vyZfKvV/P1N46nlOYCj0fElE/NliRJkiRpTNtOz46I76WU/gFYAewAXAt8DlgJDAHPIwu4Yxf5mg98IaW0E3ArcH4+fjpwSUrpLLJ5yX+aj58NLE4p3QbU8u0BPgt8Ph//NVkAlyRJkiTpaSs1Gs7dbbIPcKdzmiVJkiRp+9I0p3lf4K4pb9eugiRJUmer1daxaNFCarVa0aVIktSxDM2SJHWp4eEhVq1ayfDwUNGlSJLUsQzNkiR1oVptHdXqMhqNBtXqMrvNkiS1YGiWJKkLDQ8PMXZdk0Zj1G6zJEktGJolSepCIyMrqNfrANTrdUZGqgVXJElSZzI0S5LUhfr65tLbm915sre3l76+/oIrkiSpMxmaJUnqQpXKIKVSCYBSqYdKZbDgiiRJ6kyGZkmSulC5PIP+/gFKpRL9/QOUy+WiS5IkqSP1Fl2AJEkqRqUyyJo199hlliRpEqWxK2cKgH2AO9eufYTRUY+LJEmSJG0venpKzJy5I8C+wF1T3q5dBUmSJEmStK0zNEuSJEmS1IKhWZIkSZKkFgzNkiRJkiS1YGiWJKlL1WrrWLRoIbVarehSJEnqWIZmSZK61JVXXk7ET7nyysuLLkWSpI5laJYkqQvVauu44YYVANxwQ9VusyRJLRiaJUnqQldeeTmjo6MAjI6O2m2WJKkFQ7MkSV3oxhuvH7e8oqBKJEnqbIZmSZIkSZJaMDRLktSFDjvsiHHLcwuqRJKkzmZoliSpC82bdxKlUgmAUqmHefNOKrgiSZI6k6FZkqQuVC7PoK+vH4C+vn7K5XLBFUmS1Jl6iy5AkiQVY968k3jwwQfsMkuSNAk7zZIkSZIktWBoliSpSw0PD7Fq1UqGh4eKLkWSpI5laJYkqQvVauuoVpfRaDSoVpdRq9WKLkmSpI5kaJYkqQsNDw/RaDQAaDRG7TZLktSCoVmSpC40MrKCer0OQL1eZ2SkWnBFkiR1JkOzJEldqK9vLr292U00ent7N95+SpIkbcrQLElSF6pUBimVSgCUSj1UKoMFVyRJUmcyNEuS1IXK5Rn09w9QKpXo7x+gXC4XXZIkSR2pt+gCJElSMSqVQdasuccusyRJkyiNXTlTAOwD3Ll27SOMjnpcJEmSJGl70dNTYubMHQH2Be6a8nbtKkiSJEmSpG2doVmSJEmSpBYMzZIkSZIktWBoliRJkiSpBUOzJEmSJEktGJolSZIkSWrB0CxJkiRJUguGZkmSJEmSWjA0S5IkSZLUgqFZkiRJkqQWDM2SJHWpWm0dixYtpFarFV2KJEkdy9AsSVKXGh4eYtWqlQwPDxVdiiRJHau3nTtPKS0FdgOezIfeBfw2cBawA/BPEfEv+brHAZ8GpgNXRMRZ+fhBwEXATsBy4LSIqKeU9gYuzfcfwPyIeCSlVAYuA/YDHgDmRcT97XydkiRta2q1dVSry2g0GlSry6hUBimXy0WXJUlSx2lbpzmlVALmAAdGxEERcRBwD/C3wFzgIOCdKaWXppSmAxcDFeAA4JCU0gn5ri4FzoiIOUAJeEc+fgFwQUTsD9wMnJ2PfwKoRsQBwBeA89r1GiVJ2lYNDw/RaDQAaDRG7TZLktRCO0/PTvnfS1JKP0wpnQEcB/xXRDwUEY8CXwH+BDgUuCMi7oyIOllQfmNKaTYwPSJuzPe1OB/fATgy337jeP749WSdZoAvAyfk60uSpNzIyArq9ToA9XqdkZFqwRVJktSZ2nl69gzgO8B7yU7FXgZcAdzXtM59ZIF5jwnG95pkfBbwcB6wm8dp3iY/jfthYFfg3qkWPnPmjlNdVZKkbdIxxxzNkiVLqNfr9Pb2cuyxx7Drri8suixJkjpO20JzRNwA3DC2nFL6Itmc5U80rVYCRsk63o1nMU4+PrZOs1LT16Zk7dpHGB0dv3tJkrYfr33tiVx77bUAlEo9HH/8iTzwwK8KrkqSpPbp6Sk9owZpO+c0z00pHds0VALuAnZvGnsxWQf4nqc5/gtg55TStHx8d37TSV6Tr0dKqRd4IbD22b8iSZK2H+XyDPr7ByiVSvT3D3gRMEmSWmjnnOYy8MmU0vNSSi8ETgbeDBybUto1pfR8YBC4BvgekFJKL8mD8EnA1RGxGng8pXREvs+35ONPAlXgTfn4W4Gr88dX5cvkX6/m60uSpCaVyiBz5uxPpTJYdCmSJHWstoXmiPgm8C3g+8AtwMURcT3wYWAp8APg8oi4KSIeB04BhoDbgZX85iJf84HPpJRWAjsC5+fjp5Ndfft2oJ/sNlaQXUX7sJTSbfk672nXa5QkSZIkbd9KY7ebEAD7AHc6p1mS1A0uueQili69jqOPfg0nn/y2osuRJKmtmuY070s2dXhq27WrIEmS1LlqtXVUq8toNBpUq8uo1WpFlyRJUkcyNEuS1IWGh4cYO9us0RhleHio4IokSepMhmZJkrrQyMgK6vU6APV6nZGRasEVSZLUmQzNkiR1ob6+ufT29gLQ29tLX19/wRVJktSZDM2SJHWhSmWQUqkEQKnU422nJElqwdAsSVIXKpdn0N8/QKlUor9/gHK5XHRJkiR1pN6iC5AkScWoVAZZs+Yeu8ySJE3C+zRvah+8T7MkSZKkbdyKFd+lWl22xfb3y19mtybceectd2ZSf/8Ac+cetcX2tznP9D7NdpolSZIkSZOq1bZ8aN5W2Gne1D7YaZYkdYnVq+/i3HMXsmDBOey99+yiy5EkdbBzzz0HgA996KMFV/LMPdNOsxcCkySpS1144WdZv349F154ftGlSJLUsQzNkiR1odWr7+Lee+8BYM2ae7j77tUFVyRJUmcyNEuS1IUuvPCz45btNkuSNBFDsyRJXWisyzxmzZp7WqwpSVJ3MzRLktSF9thjr02W99xzrxZrSpLU3QzNkiR1odNOe++45fcVVIkkSZ3N0CxJUheaPXufjd3mPffcy1tOSZLUgqFZkqQuNX/+yZRKJebPP7XoUiRJ6liGZkmSutQtt9wEwM03f6/gSiRJ6lyGZkmSulCtto5qdRmNRoNqdRm1Wq3okiRJ6kiGZkmSutDw8BCjo6MAjI5uYHh4qOCKJEnqTIZmSZK60MjICjZs2ADAhg0bGBmpFlyRJEmdydAsSVIXOvjgQ8YtH1pQJZIkdTZDsyRJkiRJLRiaJUnqQrfc8t/jlm8qqBJJkjqboVmSpC7k6dmSJE2NoVmSpC70xBO/Hrf8REGVSJLU2QzNkiR1oVtvvXncsqdnS5I0EUOzJEldaOx2U62WJUlSpncqK6WUeoC/BF4GnJH/+YeI8F9YSZK2QT09PYyOjm6yLEmSnmqq/0J+Evg94FCgBLwO+Ey7ipIkSe11+OFzxy33F1SJJEmdbaqh+VjgFODxiHgYOB54TbuKkiRJ7fXa175+k+XXve71LdaUJKm7TTU0PxkRG8/hiohfA/X2lCRJktpt2bLrNlleuvS6FmtKktTdphqaf5JSeg8wLWU+D/ygjXVJkqQ2uv765eOWv1tQJZIkdbaphub3A68EXgSsAF4AnNmuoiRJUnv19EybdFmSJGWmFJrzecx/GxEvAvYDPhURa9tamSRJapv16x+bdFmSJGWmFJpTSu8FvpYvzgKGUkofU6ZQAAAgAElEQVRva1tVkiRJkiR1gKmenv0uYC5ARNwJvILslG1JkrQNOuSQV49bPrygSiRJ6mxTDc3T8lO0AYiIXwKN9pQkSZLa7c1vPnXc8inFFCJJUofrneJ6K1NKfwf8f2Rh+VTgjrZVJUmSJElSB5hqp/k0YA7wfeDm/PG721WUJElqryuvvHzSZUmSlJlSpzki/g/44zbXIkmStpIbb7x+3PIK3vnO0wuqRpKkzjVpaE4p/VNEnJlS+gYTzGGOiBPbVpkkSWqbRqMx6bIkScpsrtP8nfzvr7S7EEmStPXsttuLuP/++5qWX1xgNZIkda5JQ3NEfCN/+NaIOHYr1CNJkraCWm3duOWHCqpE6iy12jouuOA8Tj/9TMrlctHlSOoAU70QWDml9IK2ViJJkraagw8+dNzyq1usKXWX4eEhVq1ayfDwUNGlSOoQU73l1KPA6pTSj4BHxganMqc5pfSPwKyIOCWldBBwEbATsBw4LSLqKaW9gUuB3YAA5kfEIymlMnAZsB/wADAvIu5PKT0H+CLwKmA9cFJErEwplYBPAn8AjALviIhNr3QiSZJ44olfj1t+oqBKpM5Rq62jWl1Go9GgWl1GpTJot1nSlDvNXwT+ErgEGGr6M6mU0rHAyU1DlwJnRMQcoAS8Ix+/ALggIvYnu6XV2fn4J4BqRBwAfAE4Lx9/H/BoPn4msDgfHwQOAF4KvAFYnFKa6gcDkiR1jVtvvXnc8k0FVSJ1juHhoY0XxWs0Ru02SwKmEJpTSi8DfgVcFxGXNP/ZzHa7AH8LLMqXZwPTI+LGfJXFwBtTSjsAR/Kbi40tBt6YP349WacZ4MvACfn6G8cjYjmwa96tfj3w7xExGhGrgLuBvs29RqkdarV1LFq0kFqtVnQpkiRpCkZGVlCv1wGo1+uMjFQLrkhSJ9jcLadOBT4F3AH8dkrppIhYMsV9fx74MPBb+fIewH1NX78P2AuYBTwcEfVx45tsk5/G/TCw6yT7ajX+tMycuePT3UR6iiuuuIRVq1ayZMnXec973lN0OZK0iYGBAb7zne9ssrzrri8ssKLu9tBDD/F3f/d3fPCDH2SXXXYpupyudcwxR7NkyRLq9Tq9vb0ce+wx/lxIuR12mAbQlT8Tmzt1+X3AyyLi3pTS4WSd482G5pTS24GfR8R3Ukqn5MM9bHqv5xLZvOPx4+TjY+s0a7XN5saflrVrH2F01PtV6pmr1dZx7bXX0mg0WLLkWo4//kTnREnqKHPm/O4moTmll/PAA78qsKLudskll3Dbbbdx8cX/ysknv63ocrrWa197Itdeey0ApVIPxx9/oj8XUu7JJzcAbNM/Ez09pWfUIN3s6dkRcW/+9w1kXd6peBNwfErpB8DHgBOBtwO7N63zYuBe4BfAzimlafn47vk4wJp8PfK5yS8E1gL3tNhXq3Fpq3JOlKROt3jxRZssf+lLny+oEo2/+JTTeopTLs+gv3+AUqlEf/+AH3hLAjYfmse3W+sTrjVORLwmIl4WEQcBHwG+HhGnAo+nlI7IV3sLcHVEPAlUyYI2wFuBq/PHV+XL5F+v5utvHE8pzQUej4i78/H5KaVpKaWXAHOA/55KzdKW5JwoSZ1u/frHJl3W1uMHrZ2lUhlkzpz9qVQGiy5FUoeY6tWzxzzbc5bnA59JKa0EdgTOz8dPB96ZUrod6AfOysfPBg5LKd2WrzM2MfSzwHPz8fPJAjhkFxO7DfgRMAy8LSLWP8uapaetr28uvb3Z7Ife3l76+voLrkiS1Kn8oLWzlMszWLBgoV1mSRttbk7zy/OLb415fr5cAhoRsdPmniAiFpPfEioifggcOsE6q4GBCcYfIju1e/z442x6K6ux8QbwgfyPVJhKZZBqdRmQzYny02pJnebAA1/JD39468blgw46uMBqultf31yWL1+68eJTftAqSZ1lc53m3wZ+r+nP2PLL8r8lTcA5UZI6XV/f3HHLRxZUiSqVQUql7NqnftAqSZ1n0tAcEavH/gAvAl5HdhunPfIxSS04J0pSJ/NCYJ3DD1olqbNNaU5zftuoLwF/DZSB4ZTSO9pYl7TNc06UpE7mhcA6ix+0SlLnmuqFwN4HHA48HBG/AA4GzmxbVZIkSV3ED1olqXNt7kJgYzZExMMpJQAi4ucppSndfkqSJG0ZK1Z8d+NFBp+t5z//+Tz22GNNyy/g3HPPedb77e8fYO7co571fiRJ6hRT7TQ/lFI6iPyWUyml+cBDbatKkiS11R577DnpsiRJyky103wm8B/Ab6eU7gPWA5W2VSVJkp5i7tyjtmgX993vPpXHHnuMgw46mD//87/eYvuVJGl7MqVOc0T8FDgQeCXwGiBFxI/bWZgkSWqvPfbYk+nTn8+pp76z6FIkSepYk3aaU0p/0eJLx6eUiIhPt6EmSZK0FfT27sDs2ft48SlJkiaxudOzf2+rVCFJkiRJUgeaNDRHxKlbqxBJkiRJkjrNlC4EllI6HPggsCNQAqYB+0bE3m2sTZIkSZKkQk31llMXASPATsBlwMPAULuKkiRJkiSpE0w1NDci4u+BZcBKYB5wfLuKkiRJ6ia12joWLVpIrVYruhRJ0jhTDc2/yv/+X+BlEbEe2NCekiRJkrrL8PAQq1atZHjYE/kkqdNMNTTflFK6Avgv4AMppU8B9faVJUmS1B1qtXVUq8toNBpUq8vsNktSh9lsaE4plYAPAJ8B7gM+DuwP/L/2liZJkrT9Gx4eotFoANBojNptlqQOM2loTim9FLgTeC3wQ+Am4EPAK4DZba9OkiRpOzcysoJ6PTuBr16vMzJSLbgiSVKzzXWaPwl8OCK+SdZZLgG/C7waWNje0iRJkrZ/fX1zmTZtGgDTpk2jr6+/4IokSc02F5r3jojL8sdHA1+LiNGI+Dmwc3tLkyRJ2v5VKoOMjo4CMDraoFIZLLgiSVKzzYXm5itk9wHLm5aft+XLkSRJ6j6lUin/u+BC5O2/JD3F5kLzQymlA1NKc4Hdge8CpJT6gDXtLk6SJGl7Nzw81BSaS14IrGDe/kvSeJsLzQuA68huNfXhiHg0pfQB4FvAR9pdnCRJ0vZuZGQFGzZkJ/dt2LDBC4EVyNt/SZrIpKE5Im4E9gR2i4h/yodHgEMj4rvtLk6SJGl719c3l97eXgB6e3u9EFiBvP2XpIls9j7NEfFERNSalkci4o72liVJktQdKpXBptOze7wQWIG8/ZekiWw2NEuSJKl9yuUZ9PcPUCqV6O8foFwuF11S17LrL2kihmZJkqSCVSqDzJmzv13mgtn17zxezVydwNAsSZJUsHJ5BgsWLLTLXDC7/p3Hq5mrExiaJUmSpJxd/87h1czVKQzNkiRJUs6uf+fwaubqFIZmSZIkSR3Hq5mrUxiaJUmSJHUcr2auTmFoliRJktRxvJq5OoWhWZIkSVLH8Wrm6hS9RRcgSZIkSROpVAZZs+Yeu8wqlKFZkiRJUkcau5q5VCRPz5YkSZLUkWq1dSxatNB7NKtQhmZJkiRJHenKKy8n4qdceeXlRZeiLmZoliRJktRxarV13HDDCgBuuKFqt1mFMTRLkiRJOU8H7hxXXnk5o6OjAIyOjtptVmEMzZIkSVJueHiIVatWMjw8VHQpXe/GG68ft7yioErU7QzNkiRJElmXuVpdRqPRoFpdZrdZEmBoliRJkoCsy9xoNABoNEbtNhfssMOOGLc8t6BK1O0MzZIkSRIwMrKCer0OQL1eZ2SkWnBF3W3evJMolUoAlEo9zJt3UsEVqVsZmiVJkiSgr28uvb29APT29tLX119wRd2tXJ6x8XvQ19dPuVwuuCJ1K0OzJEmSBFQqg5t0NiuVwYIr0rx5J5HSAXaZVajedu48pfQx4E+ABvDFiPh0Suk44NPAdOCKiDgrX/cg4CJgJ2A5cFpE1FNKewOXArsBAcyPiEdSSmXgMmA/4AFgXkTcn1J6DvBF4FXAeuCkiFjZztcpSZKkbV+5PIP+/gGWLr2O/v4BO5sdoFyewYIFC4suQ12ubZ3mlNJRwDHAy8kC7HtTSgcCFwMV4ADgkJTSCfkmlwJnRMQcoAS8Ix+/ALggIvYHbgbOzsc/AVQj4gDgC8B5+fj7gEfz8TOBxe16jZIkSdq+VCqDzJmzv11mSRu1LTRHxHeBoyOiTtYl7gXKwB0RcWc+finwxpTSbGB6RNyYb744H98BOBL4SvN4/vj1ZJ1mgC8DJ+TrbxyPiOXArnm3WpIkSZKkp6Wtp2dHxJMppXOADwD/AewB3Ne0yn3AXpOMzwIezgN28zjN2+SncT8M7DrJvu6eat0zZ+441VUlSdpm7bDDNAB23fWFBVcidY4rrriEVatWsmTJ13nPe95TdDlSx+jmfzPaGpoBIuKjKaW/B74BzCGb3zymBIySdbynMk4+PrZOs83ta8rWrn2E0dHxTytJ0vblySc3APDAA78quBKpM9Rq67j22mtpNBosWbKE448/0XnNUm57+Dejp6f0jBqk7ZzTvH9+cS8i4jHgP4EBYPem1V4M3Avc02L8F8DOKaVp+fju+TjAmnw9Ukq9wAuBtZPsS5IkSWppeHhok/s0Dw8PFVyRarV1LFq0kFqtVnQp6mLtvOXUfsAXUkrPza9oXQE+D6SU0kvyIHwScHVErAYeTykdkW/7lnz8SaAKvCkffytwdf74qnyZ/OvVfP2N4ymlucDjETHlU7MlSZK2NoNBZxgZqdJoZGcbNhoNRkaWF1yRhoeHWLVqpR9gqFDtvBDYVcC3gO8DtwAjEfHvwCnAEHA7sJLfXORrPvCZlNJKYEfg/Hz8dOCdKaXbgX7grHz8bOCwlNJt+Tpjk04+Czw3Hz+fLIBLkiR1LINBZ9hll1mbLM+cOavFmtoaarV1VKvLaDQaVKvL/FBJhWn3hcAWAgvHjX0HOHCCdX8IHDrB+Gqy07rHjz8EnDjB+OPAyc+wZEmSpK1qfDCoVAadR1uQtWsf3GT5wQcfbLGmtobh4aGmzv8ow8NDnHzy2wquSt2o7RcCkyRJUmsGg85xxBH9LF16HY1Gg1KpxBFHHFl0SV1tZGTFJnPMR0aq2/XPxmWXLebuu1cXXUZLq1ffBcC5555TbCEt7L33bObPP6Ut+zY0S5IkFajbgkEnq1QGWb58KfV6nWnTeqlUBosuqav19c3d+P3o7e2lr6+/6JLa6u67V3Pnz4LdZj2/6FImNP252Q2JHn345wVX8lS/ePCxtu7f0CxJklSgbgsGnaxcnsGRRx7N0qXXceSRR3uafMEqlUGq1WUAlEo9XfEhxm6zns/8NxxQdBnbnMu+9tO27r+dV8+WJEnSZlQqg5RKJaB7gkEnGxg4juc973kcffRxRZfS9crlGfT3D1AqlejvH/BDDBXG0CxJklSgcnkGhx56OACHHnq4waBg3/72t1i/fj3XXPOtoksR2YdKc+bs74dJKpShWZIkSSK7kvn112f3Zr7++uXe4qgDlMszWLBgoR8mqVCGZkmSpALVauu46aYbALjpphsMagW68srLm5Ya45YldStDsyRJUoEmuuWUinHDDSvGLVcLqkRSJzE0S5IkFWiiW06pGKOjo5MuS+pOhmZJkqQC9fXNbbp6dslbTklShzE0S5IkFWhg4Lim07Mb3uqoQC960e6bLL/4xbu3WFNSNzE0S5IkFWjZsus26TQvXXpdwRV1r7e+9c/GLb+9oEokdRJDsyRJUoFGRlZs0ml2TnNxbrnlpk2Wb775ewVVojG12joWLVroVeVVKEOzJElSgZzT3Dmuv746bnl5QZVozPDwEKtWrfSq8iqUoVmSJKlAzmnuHDNmzBi3vEtBlQiyLnO1uoxGo0G1usxuswpjaJYkSSrQsmWbzmF2TnNxHnjgF+OW/6+gSgTew1ydo7foAiRJkrrZ+DnMIyPLOfnktxVUzbZpxYrvUq0ue9b72bBhw1OWzz33nGe93/7+AebOPepZ76fbTHQPc382VAQ7zZIkSQXaZZdZmyzPnDmrxZpqt5122nnSZW1dfX1z6e3Neny9vb3O91dh7DRLkiQVaO3aBzdZfvDBB1usqVbmzj1qi3Rya7V1vP/9pwFQKvXw8Y//A+Vy+VnvV89MpTK48QyCUqmHSmWw2ILUtew0S5IkFeiII/rHLR9ZUCUql2ds7C739fUbmAtWLs+gv3+AUqlEf/+A3w8VxtAsSZJUoIGBTa+W7dWzi7XbbrsxffrzmTfvpKJLEVm3ec6c/e0yq1CGZkmSpAJ9+9vf2mT5mmu+1WJNbQ29vTswe/Y+djU7RLk8gwULFvr9UKEMzZIkSQW68cbrxy2vKKgSSdJEDM2SJEmSOtLq1Xdx2mmncPfdq4suRV3M0CxJklSgWbN222R51113a7Gm1H0uvPCzrF+/ngsvPL/oUtTFDM2SJEkFqtXWbbK8bt26FmtK3WX16ru49957AFiz5h67zSqM92mWJG1Vtdo6LrjgPE4//czt/sIul122uKP/k7d69V0AnHvuOcUWMom9957N/PmnFF3GU6xY8d2N9499tqZPn86vf/34Jstb4nvS3z+wRe5dLBXlwgs/O275fBYt+lRB1aibGZolSVvV8PAQq1atZHh4iJNPflvR5bTV3Xev5o47/ocXTN+l6FIm1BjdAYB773mo4Eom9uj6zqxrS5s1a9Ym3eZZs3YtsBqpc4x1mcesWXNPizWl9jI0S5K2mlptHdXqMhqNBtXqMiqVwe2+2/yC6bvw8t/5/aLL2Cb96I6rii6hpblzj9qiXdz3v/80arV1HHPM8dv9h0nSVO2xx16bBOc999yrwGrUzZzTLEnaaoaHh2g0GgA0GqMMDw8VXJHUGWbNmsX06c+nUhksuhSpY5x22nvHLb+voErU7QzNkqStZmRkBfV6HYB6vc7ISLXgiqTO0Nu7A7Nn77Pdn3khPR2zZ+/DHntk3eU999yLvfeeXXBF6laGZknSVtPXN5dp06YBMG3aNPr6+guuSJLUyU477b1Mnz7dLrMKZWiWJG01lcpg0+nZDU9FlSRNavbsfbjwwsV2mVUoQ7Mkaav6TWguuBBJkqQp8OrZkqStZnh4iJ6eHjZs2EBPT6krbjslSd1kS97DHOCXv6wBsPPOW2a+v/cv1zNhp1mStNWMjKxgw4YNAGzYsMELgUmSJlWr1ajVakWXoS5np1mStNX09c1l+fKl1Ot1ent7vRCYJG1ntvQ9zM899xwAPvShj26xfUpPl51mSdJWU6kMUiqVACiVerwQmCRJ6niGZknSVlMuz6C/f4BSqUR//4D3pJUkSR3P07MlSVtVpTLImjX32GWWJEnbBEOzJGmrKpdnsGDBwqLLkCRJmhJPz5YkSZIkqQVDsyRJkiRJLRiaJUmSJElqwdAsSZIkSVILhmZJkiRJklpo69WzU0ofBebli9+KiL9OKR0HfBqYDlwREWfl6x4EXATsBCwHTouIekppb+BSYDcggPkR8UhKqQxcBuwHPADMi4j7U0rPAb4IvApYD5wUESvb+TolSZIkSduntnWa83B8PPAK4CDg4JTSnwIXAxXgAOCQlNIJ+SaXAmdExBygBLwjH78AuCAi9gduBs7Oxz8BVCPiAOALwHn5+PuAR/PxM4HF7XqNkiRJkqTtWzs7zfcBfxkRTwCklH4KzAHuiIg787FLgTemlG4HpkfEjfm2i4Fz0v/f3r1H2V3W9x5/TyZXEzIDBBZBSAJL/VZ7ClgQa5yYeKR22VqjYmVJLODxUg/l1J5z6DmmahFbxbq6aGvrXTnQErGrQqUtcKpQgonxRrxgEb9cDpkUCA25zCST+0zm/LF/o2OYnQvsPc+e2e/XWrOS/ezffuaz92/27Pn+nuf3/CI+D7wCeP2o9nuA/w38RnUfwE3AJyJiWtX+RwCZ+fWIOCkiFmTmxiY+V0kNtnbtPaxZs7ph/fX39wHQ1dXdkP6WLFlGT8/ShvQlSZKk1tW0ojkz7x/5f0Q8n9o07b+iVkyP2AScBpxap30esCMzBw9pZ/RjqmncO4CTDtPXURfNJ54452g3ldQkc+fOYtq0zob1t2NHPwDz5p3YkP7mzp3FSScd15C+NHk18me4XU2b1tkW77WRn5V2eK6tzn3RWtppf/iZ8ew08/Oiqec0A0TELwK3AX8ADFIbbR7RARykNk18+CjaqdpHthntSH0dta1bBzh48NBvK2k8nXXW+Zx11vkN6++aa64G4Mor39+wPp96amfD+tLkdODAUOkIE96BA0Nt8V4b+Vlph+fa6twXraWd9oefGc/O0XxeTJnS8YwGSJu6enZEvBy4C3hvZt4APAbMH7XJKcATh2nfDHRFxMhhl/lVO8Dj1XZExFTgOGDrYfqSJEmSJOmYNHMhsNOBr1BbvfpLVfO3a3fF86pC+GLgjszsBfZWRTbAb1ftB4A1wEVV+yXAHdX/b69uU92/ptr+p+0R0QPs9XxmSZIkSdIz0czp2VcCM4FrI2Kk7dPAZcDN1X23A1+u7lsBfC4i5gLfAz5etV8O3BAR76d2XvJbqvYPANdHxP1AX/V4qJ03/ZmqfR+1AlySJEmSpGPWzIXA3gO8p87dZ4+x/Q+Bp53AWI1CLxujfRvwujHa9wKXHmNcSZIkSZKepukLgUmSJrZWv/wXeAkwSZLUPBbNkqRx1dfX+KK5VfX397Fr91bue+j20lEmpF27t9Lf39Q1SyVJOiKLZknSYfX0LG3oKO7I5b9WrryqYX1KkiQ1i0WzJElN0tXVza6dBznr+b9eOsqEdN9Dt7fFjARJUmtzzpMkSZIkSXU40ixJkqRxtWrV9Wzc2Fs6xph6ezcAPzuVpBUtWLCQFSsuKx1DahsWzZIkSRpXGzf2suHhR5g/9+TSUZ5m9pSZAOzbvLNwkrFt2rG5dAQ1SX9/H9u27mbVVx4oHWXC2bxlNycM9zWtf4tmSZIkjbv5c0/mXYvfUjrGhPPZdTeVjiC1HYtmSZI06a1a1brTgcEpwZJqi0dO7djJite/sHSUCWfVVx5g9tzmLRxp0SxJkia9jRt7efjBB5gza3rpKGPqGBoC4Ml/f6RwkrEN7NlfOoKaZNUqDyg9Gx5Mag8WzZIkqS3MmTWd857XeufQTgT3Pux5tJPVxo29PPzwg8yZM6d0lDF1dNT+ffLJJ8oGGcPAwEDpCBonFs2SJElSG5szZw7nnntu6RgTzvr160tH0DjxOs2SJEmSJNVh0SxJkiRJUh0WzZIkSZIk1WHRLEmSJElSHRbNkiRJkiTVYdEsSZIkSVIdFs2SJEmSJNVh0SxJkiRJUh0WzZIkSZIk1WHRLEmSJElSHRbNkiRJkiTVMbV0AEmSJLWX/v4+tu/YymfX3VQ6yoSzacdmjp8xVDqG1FYsmiVpklm16no2buwtHaOu3t4NAFxzzdVlg9SxYMFCVqy4rGH97dqzjfseur1h/TXS/gN7AJg+bVbhJGPbtWcbcEJD+urv72Pnnv3c+/DmhvTXbnbu2c+s/r7SMdQE/f197Ny5k/Xr15eOMuHs3LmTWbMa+77YvGU3q77yQEP7bJRduw8AMPs50wonebrNW3Zzxtzm9W/RLEmTzMaNvfy/fIB5na35K37GwYMA7Hj4ocJJnm7L0GBD+1uwYGFD+2u0kQMYp5723LJB6jqh5V9DPTNdXd3M3NfJuxa/pXSUCeez625iRtdxpWOoCVr9992W7RsAOPmU08sGGcMZc5v7+rXmX1SSpGdlXudU3tjdmBG6dnJL37aG9tfIEetmGBntX7nyqsJJmq+rq5s9O7Zy3vNOLh1lQrr34c10dXWXjqEm6OrqZs+e3Zx77rmlo0w469evb+j7ws+M1uVCYJIkSZIk1WHRLEmSJElSHU7PltQQq1a5+NSz0ejFpyRJktQYFs2SGmLjxl7ykZ8wtXtG6ShjOji1tsDTI1sfLZzk6Qb79pWOIEmSpDosmiU1zNTuGRy/7LTSMSac7asfKx1BkiRJdXhOsyRJkiRJdVg0S5IkSZJUh9OzJUlSWxjYs597H95cOsaY9h8YAmD6tM7CScY2sGd/6QhqooGBAdavX186xpj276/97E2fPr1wkqcbGBgoHUHjxKJZkiRNegsWLCwd4bBGVvg/5fRFRXMcTqNfw007NvPZdTc1tM9G2LlvFwDHzZhdOMnYNu3YzKKTj2tYfxPmvXHKqWWD1NHqr58aw6JZkiRNeq1+SbeRy+GtXHlV4STjo5ULjSd7twIw7+RTCicZ26KTj2vo6+d7Qzoyi2ZJkiSNq1Yu1CzSJB3KhcAkSZIkSarDolmSJEmSpDqcni1Jk0x/fx9bBwe5pW9b6SgTzpbBQYb7+0rHkCRJLcSRZkmSJEmS6nCkWZImma6ubjqeeoo3dp9QOsqEc0vfNuZ2dZeOIUnSs7Z27T2sWbO6Yf2NXP5rZLG8RliyZBk9PUsb1l+zWDRLkiRJkg6ru7t9DypbNEuSJEnSJNPTs3RCjOJOBJ7TLEmSJElSHU0faY6IucA64LWZuSEiLgCuBWYBf5eZ76+2Owf4PDAX+Drw7swcjIgFwI3AyUACKzJzICK6gVXAmcBTwJsz88mImA58ATgP2ANcnJk/afbzlCRJkiRNPk0daY6IlwJrgRdUt2cB1wHLgRcCL4mI11Sb3whckZkvADqAd1btnwQ+mZm/ANwLfKBq/xNgTWa+EPgc8JdV++8Bu6r23weub9oTlCRJkiRNas2env1O4HeBJ6rb5wMPZeajmTlIrVD+rYhYCMzKzG9V211ftU8DXgF8eXR79f/foDbSDHAT8Jpq+5+2Z+bXgZOq0WpJkiRJko5JU6dnZ+Y7ACJipOlUYNOoTTYBpx2mfR6woyqwR7f/XF/VNO4dwEmH6Wvj0eY+8cQ5R7uppMq0aZ2lI0xo06Z1ctJJxzWsLz1zjdwXrW7kZ6Vdnm8rc1+0DvdFa3F/qBWM9+rZU4DhUbc7gIPH0KBsc8gAAA7USURBVE7VPrLNaEfq66ht3TrAwYOHfltJh3PgwFDpCBPagQNDPPXUzob1pWeukfui1Y38rLTL821l7ovW4b5oLe4PNdKUKR3PaIB0vFfPfgyYP+r2KdSmbtdr3wx0RcTIsMl8fjbV+/FqOyJiKnAcsPUwfUmSJEmSdEzGe6T520BExPOAR4GLgesyszci9kbEyzPzG8BvA3dk5oGIWANcBHwRuAS4o+rr9ur2R6r711Tbj7SvjYgeYG9mHvXUbEmaDLYMDXJL37bSMca0+2Bt8s9zprTeVQ+3DA0yt3QISZLUUsa1aM7MvRFxGXAzMJNa4TuyyNcK4HPVJaq+B3y8ar8cuCEi3k/tvOS3VO0fAK6PiPuBvurxAH8FfKZq30etAJektrFgwcLSEQ5re+8GAE5ZuKhojrHMpfVfP0mSNL7GpWjOzEWj/n8XcPYY2/yQ2urah7b3AsvGaN8GvG6M9r3Apc8qsKRj1t/fx2DfPravfqx0lAlnsG8f/VP7GtbfihWXNayvZrjmmqsBWLnyqsJJJEmSjmy8p2dLDbVq1fVs3NjbsP76+/vo62tc8dJo3d3ddHV1N6y/BQsWtnyBJUmSJJVk0awJ7Uc/+iFPbnqC6Z2HLqb+zAwNDzN0TGutj6+n9u1m2+ZNR97wKOwfGqa/v3EHCLq6utkyuJ3jl5125I31c7avfqyhB0MkSSpl7dp7WLNmdcP6661O6RmZpfRsLVmyjJ6epQ3pS+3DolkT3vTODubPmVY6xoSzaeBA6QiSJEmH1d3tQWWVZ9GsCa2rq5sZu7fw9hfPKx1lwvnC97cw09FNSZLUQD09Sx3J1aRj0SxJkqQJrZFTghs9HRicEixNdBbNkiRJUsXpwJIOZdGsCe/JgQN84ftbSsd4moH9QwDMmd5ZOMnYnhw4wKIG99nKl5w6uHcQgCkzW+/X3mDfPjixdApJmricEiypmVrvr0fpGCxYsLB0hLr+o5reNW/+oqI56llEY1+/Vt4X8LPpdgtPXFQ0x5hObP3XT5IkqV1ZNGtCa+VrDI+cC7Vy5VWFk4yPVt4X0H77Q5IkSY0xpXQASZIkSZJalUWzJEmSJEl1OD1bGsVLVkiSJEkazaJZahIvWSFJk1cjD7KCB1olqZVZNEujeMkKSVIJHmiVpNZl0XyM1q69hxtvvL5h/e3fv4+hoaGG9dcMnZ2dTJ8+o2H9vfWtl1mYSpImNA+ySlL7cCEwSZIkSZLqcKT5GHlkWZIkNVpf33Y++cm/5PLLf9+p2pLUYhxpliRJKuzWW2/mwQd/wq233lw6iiTpEBbNkiRJBfX1bWfNmtUMDw+zZs1q+vr6SkeSJI1i0SxJklTQrbfezPDwMADDwwcdbZakFmPRLEmSVNC6dWsZHBwEYHBwkHXr1hROJEkazaJZkiSpoMWLe5g6tbY269SpU1m8eEnhRJKk0SyaJUmSClq+/EI6OjoA6OiYwvLlFxZOJEkazaJZkiSpoO7u41myZBkdHR0sWbLMS05JUouxaJYkSSps2bILmDlzJq985QWlo0iSDmHRLEmSVNjq1Xeyd+9e7r77ztJRJEmHmFo6gCSpta1dew9r1qxuWH+9vRsAuOaaqxvW55Ily+jpWdqw/qTxdOh1mpcvv9Ap2pLUQhxpliSNq+7ubgsCaRSv0yxJrc2RZkktqdVHN9tpZLOnZ2nbPFephLGu03zppW8vnEqSNMKiWVJbcGRTk0GrH0yC9jqg1CiLF/fw9a/fzeDgoNdplqQWZNEsqSU5uik1nweTWsPy5Rf+9GCI12mWpNZj0SxJ0gThwaTJaeQ6zXfffafXaZakFmTRLEmSVNjy5Rfy+OOPOcosSS2oY2S1RgGwCHh069YBDh70dZEkSZKkyWLKlA5OPHEOwBnAhqN+XLMCSZIkSZI00Vk0S5IkSZJUh0WzJEmSJEl1WDRLkiRJklSHRbMkSZIkSXVYNEuSJEmSVIdFsyRJkiRJdVg0S5IkSZJUh0WzJEmSJEl1WDRLkiRJklSHRbMkSZIkSXVYNEuSJEmSVMfU0gGaISIuBt4PTAP+IjM/UTiSJEmSJGkCmnQjzRHxXODDQA9wDvCuiHhR2VSSJEmSpIloMo40XwD8a2ZuA4iILwNvAj50FI/tBJgypaN56SRJkiRJ425Undd5LI+bjEXzqcCmUbc3Aecf5WPnAxx//OxGZ5IkSZIktYb5wCNHu/FkLJqnAMOjbncAB4/ysd8FllArtIcanEuSJEmSVE4ntYL5u8fyoMlYND9GrfAdcQrwxFE+dh+wtuGJJEmSJEmt4KhHmEdMxqL5TuCDEXESsAu4EHhX2UiSJEmSpIlo0q2enZmPA+8D7gZ+AHwxM79TNpUkSZIkaSLqGB4ePvJWkiRJkiS1oUk30ixJkiRJUqNYNEuSJEmSVIdFsyRJkiRJdVg0S5IkSZJUh0WzJEmSJEl1TMbrNLe1iJgLrANem5kbCsdpaxFxFfDm6uZtmfm/SuZpZxHxIeBNwDDwhcy8tnCkthcRfwbMy8zLSmdpVxFxN3AycKBq+p3M/HbBSG0rIn4TuAqYDXw1M99TOFLbioh3AFeMajoD+NvMvKLOQ9REEfFWYGV1847MvLJknnYWEe8F3gbsA/4uMz9cONK4cqR5EomIlwJrgReUztLuIuIC4NXAi4FzgHMj4g1lU7WniFgK/GfgLOA84L9FRJRN1d4i4lXApaVztLOI6KD2WXF2Zp5TfVkwFxARZwKfBl5P7ffUL0fEa8qmal+Z+fmR9wSwAtgMfLBsqvYUEc8BPg4sBc4GllR/X2mcVa/7xcBLqP1t+9KIeGPZVOPLonlyeSfwu8ATpYOITcD/zMz9mXkAeABYUDhTW8rMe4BXZuYgtVG1qcCusqnaV0ScAHwY+EjpLG1u5MDRVyPihxHhKFo5b6A2avNY9XlxEeABjNbwKeAPM3NL6SBtqpNarTIbmFZ97SmaqH29GPiXzNyRmUPA/6V2oK9tWDRPIpn5jsxcUzqHIDPvz8xvAUTE86lN0769bKr2lZkHIuJq4MfAXcDjhSO1s88A7wO2lw7S5o6n9l54A/Aq4N0R8atlI7Wt5wGdEfGPEfED4HJ8fxRXjazNysy/L52lXWXmTuADwE+Ax4AN1E5B1Pj7HvBrEXFCRMwEXgecUjjTuLJolpooIn4R+BrwB5n5UOk87SwzrwJOAk6nNitD46w6V/DfM/Ou0lnaXWZ+MzMvycz+ahTtC8Cvl87VpqYCFwBvB14GvBRPX2gFvwO4/kVBEXEW8F+AhcCpwBDgOc0FVJ/b1wOrqY0yrwX2F4w07iyapSaJiJdTG8l5b2beUDpPu4qIX4iIcwAyczdwC7XzBjX+LgJeXY2mfQh4XUT8eeFMbSkieqpzy0d08LMFwTS+ngTuzMynMnMP8A/A+YUztbWImE7tPNp/LJ2lzf0acFdmbs7MfdSKtmVFE7WpiDgOuDkzz8rMZdQWA3ukbKrx5erZUhNExOnAV4CLMvNfS+dpc2cCV0dED7XVs5cD15WN1J4y86fTfyPiMmBZZv73conaWjfwoYhYTO08wUuBd5eN1Lb+GbghIrqBncBrqH1+qJyzgAcz0/Uvyvoh8LGImA3sBn4T+G7ZSG3rDOBvIuI8aueYv736ahuONEvNcSUwE7g2In5QffkHaQGZeTtwG/B9YD2wLjO/VDaVVFZm/jM//764LjO/WTZVe6pWLf8YtemOPwZ6gf9TNJTOpHYOrQrKzK8CN1H7HXUftQN8Hy0aqk1l5n3AzdT2w3eAv8jMb5RNNb46hoeHS2eQJEmSJKklOdIsSZIkSVIdFs2SJEmSJNVh0SxJkiRJUh0WzZIkSZIk1WHRLEmSJElSHV6nWZKkwqpL0v1XapdUGQa+B7wvMzc2+ft+EJiXmVdExAbgTZl5b0R0Au8BLqb2t8J04J+AP8rMfQ363lcC/ykzL2tEf5IkNYsjzZIkFRQRfwZcCLw2M18E/BLwNeCbEXFaoVifAl4GvCozzwFeAgTw+UJ5JEkqxpFmSZIKqYridwOnZ+Z2gMw8CPxNRJwLfC0iBjPzl6rtu4FHgTOB5wB/DSygNkL9pcz8SEQsAtYADwCLgKXA24DlwCxgNnBlZv5DnUyLgBXA/MzcUWXaVY2Gv7zapgv4BHAOtZHxO4A/zMzBiNgLfBR4NTAf+FhmfioipgEfB34V2Az8B9D/bF9DSZKazZFmSZLKeSnwwEjBfIg7gf3AnIg4r2p7C3Bbtf3fAtdl5rnA+cAFEfHmarvTgD/OzBdQm1p9AbAsM88C3gd86DCZzgXuHymYR2Tmk5l5c3Xz48BWaqPi5wFnA1dW980AtmTmYuBNwJ9HxEzgcuAFwIuoFc4LDv/SSJLUGiyaJUkqa1qd9hnURnGvAy6r2t4GfC4iZlMbQf7jiPgB8C1qReg51XaDwDcBMrMXuARYEREfpTayPecweQ5y5L8PXgP8dWYOV+c4f7pqG3Fr9e/3qucxm1rh/sXM3J+Zu4BVR/gekiS1BItmSZLK+Rbw/Ig4ZYz7Xgmso1Y0/1ZEnAN0Z+Y9QCfQASzOzHOq845/BfhI9dh9mTkIEBG/TK2Angt8FfjT6rH1fBt4YUQcN7oxIp4bEbdFxCxqfz8Mj7p7Cj9f/O8ByMyRbToO+Rdqhb0kSS3PolmSpEIy83FqU51viojnjrRHxNuoLQ72p9U23wE+Q7UQVzV1+lvA/6i27wa+Qe285UO9Arg3M68F7gFeT63orpfpCWqjwNdFxNyq/7nAJ4GtmbkH+BfgiojoiIgZwLuoLV52OHcAl0TEzGq69kVH2F6SpJZg0SxJUkGZuRK4Ebg1Iv4tIh6iNpX5ZdXUaoDPAS8Gbhj10IuBX4mIH1EbHb4pM8ea8nwTMC8iHgB+DAwAJxw6knyIy6tt11XTv79d3X5Hdf/vAScDP6q+EvjwEZ7qZ4B7gX+jVrw/eoTtJUlqCR3Dw8NH3kqSJEmSpDbkSLMkSZIkSXVYNEuSJEmSVIdFsyRJkiRJdVg0S5IkSZJUh0WzJEmSJEl1WDRLkiRJklSHRbMkSZIkSXX8f2KZAWNDccljAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.boxplot(y='SalePrice', x='OverallCond', data=train_data)\n",
    "plt.title('Sales Price comaparison with Overall condition of house')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.scatterplot(y='SalePrice', x='LotArea', data=train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "sns.barplot(y='SalePrice', x='YearBuilt', data=train_data)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Sales Price distribution against Year Built')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2    ...      WoodDeckSF  \\\n",
       "0          2003       196.0         706           0    ...               0   \n",
       "1          1976         0.0         978           0    ...             298   \n",
       "2          2002       162.0         486           0    ...               0   \n",
       "3          1970         0.0         216           0    ...               0   \n",
       "4          2000       350.0         655           0    ...             192   \n",
       "\n",
       "   OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
       "0           61              0          0            0         0        0   \n",
       "1            0              0          0            0         0        0   \n",
       "2           42              0          0            0         0        0   \n",
       "3           35            272          0            0         0        0   \n",
       "4           84              0          0            0         0        0   \n",
       "\n",
       "   MoSold  YrSold  SalePrice  \n",
       "0       2    2008     208500  \n",
       "1       5    2007     181500  \n",
       "2       9    2008     223500  \n",
       "3       2    2006     140000  \n",
       "4      12    2008     250000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Numerical data distribution\n",
    "df_num= train_data.select_dtypes(include = ['float64', 'int64'])\n",
    "\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr = train_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.heatmap(df_corr, cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OverallQual     0.790982\n",
       "GrLivArea       0.708624\n",
       "GarageCars      0.640409\n",
       "GarageArea      0.623431\n",
       "TotalBsmtSF     0.613581\n",
       "1stFlrSF        0.605852\n",
       "FullBath        0.560664\n",
       "TotRmsAbvGrd    0.533723\n",
       "YearBuilt       0.522897\n",
       "YearRemodAdd    0.507101\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num_corr = df_num.corr()['SalePrice'][:-1] \n",
    "# -1 because the latest row is SalePrice\n",
    "golden_features_list = df_num_corr[abs(df_num_corr) > 0.5].sort_values(ascending=False)\n",
    "golden_features_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   KitchenAbvGr: -0.13920069217785566\n",
      "       HalfBath: -0.08439171127179887\n",
      "     MSSubClass: -0.08428413512659523\n",
      "    OverallCond: -0.0778558940486776\n",
      "         YrSold: -0.028922585168730426\n",
      "   BsmtHalfBath: -0.028834567185481712\n",
      "       PoolArea: -0.014091521506356928\n",
      "   BsmtFullBath: 0.011439163340408634\n",
      "         MoSold: 0.04643224522381936\n",
      "      3SsnPorch: 0.06393243256889079\n",
      "    OpenPorchSF: 0.08645298857147708\n",
      "        MiscVal: 0.08896338917298924\n",
      "     Fireplaces: 0.1216605842136395\n",
      "      BsmtUnfSF: 0.16926100049514192\n",
      "   BedroomAbvGr: 0.18093669310849045\n",
      "     WoodDeckSF: 0.19370601237520677\n",
      "     BsmtFinSF2: 0.19895609430836586\n",
      "  EnclosedPorch: 0.2412788363011751\n",
      "    ScreenPorch: 0.25543007954878405\n",
      "        LotArea: 0.2638433538714063\n",
      "   LowQualFinSF: 0.3000750165550133\n",
      "    LotFrontage: 0.33477085313976035\n",
      "     MasVnrArea: 0.4340902197568926\n",
      "    GarageYrBlt: 0.4667536523633402\n",
      "     BsmtFinSF1: 0.4716904265235731\n",
      "   YearRemodAdd: 0.5071009671113867\n",
      "      YearBuilt: 0.5228973328794967\n",
      "   TotRmsAbvGrd: 0.5337231555820238\n",
      "       FullBath: 0.5745626737760816\n",
      "       1stFlrSF: 0.6058521846919166\n",
      "     GarageArea: 0.6084052829168343\n",
      "    TotalBsmtSF: 0.6096808188074366\n",
      "     GarageCars: 0.6370954062078953\n",
      "       2ndFlrSF: 0.6733048324568383\n",
      "      GrLivArea: 0.7086244776126511\n",
      "    OverallQual: 0.7909816005838047\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "individual_features_df = []\n",
    "for i in range(0, len(df_num.columns) - 1): # -1 because the last column is SalePrice\n",
    "    tmpDf = df_num[[df_num.columns[i], 'SalePrice']]\n",
    "    tmpDf = tmpDf[tmpDf[df_num.columns[i]] != 0]\n",
    "    individual_features_df.append(tmpDf)\n",
    "\n",
    "all_correlations = {feature.columns[0]: feature.corr()['SalePrice'][0] for feature in individual_features_df}\n",
    "all_correlations = sorted(all_correlations.items(), key=operator.itemgetter(1))\n",
    "for (key, value) in all_correlations:\n",
    "    print(\"{:>15}: {:>15}\".format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_num.drop('SalePrice', axis=1).corr()\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "sns.heatmap(corr[(corr >= 0.5) | (corr <= -0.4)], \n",
    "            cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
    "            annot=True, annot_kws={\"size\": 8}, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Library for Encoding Data\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#Defining instance for Label Encoder\n",
    "encode_data = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining Function For encoding All Categorical columns\n",
    "def CaTorigical_data(data):\n",
    "    for c in cat_cols:\n",
    "        data[c] = encode_data.fit_transform(data[c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  LotShape  LandContour  \\\n",
       "0          60         3         65.0     8450       1         3            3   \n",
       "1          20         3         80.0     9600       1         3            3   \n",
       "2          60         3         68.0    11250       1         0            3   \n",
       "3          70         3         60.0     9550       1         0            3   \n",
       "4          60         3         84.0    14260       1         0            3   \n",
       "\n",
       "   Utilities  LotConfig  LandSlope    ...      EnclosedPorch  3SsnPorch  \\\n",
       "0          0          4          0    ...                  0          0   \n",
       "1          0          2          0    ...                  0          0   \n",
       "2          0          4          0    ...                  0          0   \n",
       "3          0          0          0    ...                272          0   \n",
       "4          0          2          0    ...                  0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  SaleType  SaleCondition  \\\n",
       "0            0         0        0       2    2008         8              4   \n",
       "1            0         0        0       5    2007         8              4   \n",
       "2            0         0        0       9    2008         8              4   \n",
       "3            0         0        0       2    2006         8              0   \n",
       "4            0         0        0      12    2008         8              4   \n",
       "\n",
       "   SalePrice  \n",
       "0     208500  \n",
       "1     181500  \n",
       "2     223500  \n",
       "3     140000  \n",
       "4     250000  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Encoding function to train data\n",
    "CaTorigical_data(train_data)\n",
    "\n",
    "#Validating encoding\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
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       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
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       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
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       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
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       "      <th>SaleCondition</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3</td>\n",
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       "      <td>14267</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>12500</td>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  LotShape  LandContour  \\\n",
       "0          20         2         80.0    11622       1         3            3   \n",
       "1          20         3         81.0    14267       1         0            3   \n",
       "2          60         3         74.0    13830       1         0            3   \n",
       "3          60         3         78.0     9978       1         0            3   \n",
       "4         120         3         43.0     5005       1         0            1   \n",
       "\n",
       "   Utilities  LotConfig  LandSlope      ...        OpenPorchSF  EnclosedPorch  \\\n",
       "0          0          4          0      ...                  0              0   \n",
       "1          0          0          0      ...                 36              0   \n",
       "2          0          4          0      ...                 34              0   \n",
       "3          0          4          0      ...                 36              0   \n",
       "4          0          4          0      ...                 82              0   \n",
       "\n",
       "   3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  SaleType  \\\n",
       "0          0          120         0        0       6    2010         8   \n",
       "1          0            0         0    12500       6    2010         8   \n",
       "2          0            0         0        0       3    2010         8   \n",
       "3          0            0         0        0       6    2010         8   \n",
       "4          0          144         0        0       1    2010         8   \n",
       "\n",
       "   SaleCondition  \n",
       "0              4  \n",
       "1              4  \n",
       "2              4  \n",
       "3              4  \n",
       "4              4  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Encoding function to test data\n",
    "CaTorigical_data(test_data)\n",
    "\n",
    "#Validating encoding\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appying Machine Learning Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= train_data.drop('SalePrice', axis=1)\n",
    "ytrain = train_data['SalePrice']\n",
    "x = test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
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       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
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       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
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       "      <th>MiscVal</th>\n",
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       "      <td>-0.087688</td>\n",
       "      <td>-1.599111</td>\n",
       "      <td>0.138777</td>\n",
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       "      <td>-0.872563</td>\n",
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       "      <td>-0.091886</td>\n",
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       "      <td>0.750731</td>\n",
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       "      <td>-0.02618</td>\n",
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       "      <td>...</td>\n",
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       "      <td>-0.087688</td>\n",
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       "      <td>-0.045532</td>\n",
       "      <td>-0.084636</td>\n",
       "      <td>0.073480</td>\n",
       "      <td>0.064238</td>\n",
       "      <td>-1.378933</td>\n",
       "      <td>0.314667</td>\n",
       "      <td>-0.02618</td>\n",
       "      <td>0.604670</td>\n",
       "      <td>-0.225716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070361</td>\n",
       "      <td>-0.359325</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>0.990891</td>\n",
       "      <td>0.138777</td>\n",
       "      <td>0.313867</td>\n",
       "      <td>0.208502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.309859</td>\n",
       "      <td>-0.045532</td>\n",
       "      <td>-0.447940</td>\n",
       "      <td>-0.096897</td>\n",
       "      <td>0.064238</td>\n",
       "      <td>-1.378933</td>\n",
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       "      <td>-0.02618</td>\n",
       "      <td>-1.861302</td>\n",
       "      <td>-0.225716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.176048</td>\n",
       "      <td>4.092524</td>\n",
       "      <td>-0.116339</td>\n",
       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
       "      <td>-1.599111</td>\n",
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       "      <td>0.313867</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.073375</td>\n",
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       "      <td>0.641972</td>\n",
       "      <td>0.375148</td>\n",
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       "      <td>0.314667</td>\n",
       "      <td>-0.02618</td>\n",
       "      <td>-0.628316</td>\n",
       "      <td>-0.225716</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.270208</td>\n",
       "      <td>-0.068692</td>\n",
       "      <td>-0.087688</td>\n",
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       "      <td>0.208502</td>\n",
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      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage   LotArea    Street  LotShape  \\\n",
       "0    0.073375 -0.045532    -0.220875 -0.207142  0.064238  0.750731   \n",
       "1   -0.872563 -0.045532     0.460320 -0.091886  0.064238  0.750731   \n",
       "2    0.073375 -0.045532    -0.084636  0.073480  0.064238 -1.378933   \n",
       "3    0.309859 -0.045532    -0.447940 -0.096897  0.064238 -1.378933   \n",
       "4    0.073375 -0.045532     0.641972  0.375148  0.064238 -1.378933   \n",
       "\n",
       "   LandContour  Utilities  LotConfig  LandSlope      ...        OpenPorchSF  \\\n",
       "0     0.314667   -0.02618   0.604670  -0.225716      ...           0.216503   \n",
       "1     0.314667   -0.02618  -0.628316  -0.225716      ...          -0.704483   \n",
       "2     0.314667   -0.02618   0.604670  -0.225716      ...          -0.070361   \n",
       "3     0.314667   -0.02618  -1.861302  -0.225716      ...          -0.176048   \n",
       "4     0.314667   -0.02618  -0.628316  -0.225716      ...           0.563760   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea   MiscVal    MoSold  \\\n",
       "0      -0.359325  -0.116339    -0.270208 -0.068692 -0.087688 -1.599111   \n",
       "1      -0.359325  -0.116339    -0.270208 -0.068692 -0.087688 -0.489110   \n",
       "2      -0.359325  -0.116339    -0.270208 -0.068692 -0.087688  0.990891   \n",
       "3       4.092524  -0.116339    -0.270208 -0.068692 -0.087688 -1.599111   \n",
       "4      -0.359325  -0.116339    -0.270208 -0.068692 -0.087688  2.100892   \n",
       "\n",
       "     YrSold  SaleType  SaleCondition  \n",
       "0  0.138777  0.313867       0.208502  \n",
       "1 -0.614439  0.313867       0.208502  \n",
       "2  0.138777  0.313867       0.208502  \n",
       "3 -1.367655  0.313867      -3.426284  \n",
       "4  0.138777  0.313867       0.208502  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scalling data with standard scaler\n",
    "sc = StandardScaler()\n",
    "X_ = sc.fit_transform(X)\n",
    "x_= sc.fit_transform(x)\n",
    "\n",
    "X = pd.DataFrame(data=X_, columns = X.columns)\n",
    "x = pd.DataFrame(data=x_, columns = x.columns)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.874711</td>\n",
       "      <td>-1.501777</td>\n",
       "      <td>0.567330</td>\n",
       "      <td>0.363929</td>\n",
       "      <td>0.06426</td>\n",
       "      <td>0.742620</td>\n",
       "      <td>0.31870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.572445</td>\n",
       "      <td>-0.207992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.701628</td>\n",
       "      <td>-0.360738</td>\n",
       "      <td>-0.088827</td>\n",
       "      <td>1.818960</td>\n",
       "      <td>-0.057227</td>\n",
       "      <td>-0.092244</td>\n",
       "      <td>-0.038281</td>\n",
       "      <td>1.713905</td>\n",
       "      <td>0.32475</td>\n",
       "      <td>0.201383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.874711</td>\n",
       "      <td>-0.039098</td>\n",
       "      <td>0.615963</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>0.06426</td>\n",
       "      <td>-1.384637</td>\n",
       "      <td>0.31870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.950811</td>\n",
       "      <td>-0.207992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178826</td>\n",
       "      <td>-0.360738</td>\n",
       "      <td>-0.088827</td>\n",
       "      <td>-0.301543</td>\n",
       "      <td>-0.057227</td>\n",
       "      <td>19.730438</td>\n",
       "      <td>-0.038281</td>\n",
       "      <td>1.713905</td>\n",
       "      <td>0.32475</td>\n",
       "      <td>0.201383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.061351</td>\n",
       "      <td>-0.039098</td>\n",
       "      <td>0.275532</td>\n",
       "      <td>0.809646</td>\n",
       "      <td>0.06426</td>\n",
       "      <td>-1.384637</td>\n",
       "      <td>0.31870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.572445</td>\n",
       "      <td>-0.207992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.207871</td>\n",
       "      <td>-0.360738</td>\n",
       "      <td>-0.088827</td>\n",
       "      <td>-0.301543</td>\n",
       "      <td>-0.057227</td>\n",
       "      <td>-0.092244</td>\n",
       "      <td>-1.140614</td>\n",
       "      <td>1.713905</td>\n",
       "      <td>0.32475</td>\n",
       "      <td>0.201383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.061351</td>\n",
       "      <td>-0.039098</td>\n",
       "      <td>0.470064</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>0.06426</td>\n",
       "      <td>-1.384637</td>\n",
       "      <td>0.31870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.572445</td>\n",
       "      <td>-0.207992</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178826</td>\n",
       "      <td>-0.360738</td>\n",
       "      <td>-0.088827</td>\n",
       "      <td>-0.301543</td>\n",
       "      <td>-0.057227</td>\n",
       "      <td>-0.092244</td>\n",
       "      <td>-0.038281</td>\n",
       "      <td>1.713905</td>\n",
       "      <td>0.32475</td>\n",
       "      <td>0.201383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.465443</td>\n",
       "      <td>-0.039098</td>\n",
       "      <td>-1.232092</td>\n",
       "      <td>-0.971808</td>\n",
       "      <td>0.06426</td>\n",
       "      <td>-1.384637</td>\n",
       "      <td>-2.53396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.572445</td>\n",
       "      <td>-0.207992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489198</td>\n",
       "      <td>-0.360738</td>\n",
       "      <td>-0.088827</td>\n",
       "      <td>2.243060</td>\n",
       "      <td>-0.057227</td>\n",
       "      <td>-0.092244</td>\n",
       "      <td>-1.875504</td>\n",
       "      <td>1.713905</td>\n",
       "      <td>0.32475</td>\n",
       "      <td>0.201383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage   LotArea   Street  LotShape  \\\n",
       "0   -0.874711 -1.501777     0.567330  0.363929  0.06426  0.742620   \n",
       "1   -0.874711 -0.039098     0.615963  0.897861  0.06426 -1.384637   \n",
       "2    0.061351 -0.039098     0.275532  0.809646  0.06426 -1.384637   \n",
       "3    0.061351 -0.039098     0.470064  0.032064  0.06426 -1.384637   \n",
       "4    1.465443 -0.039098    -1.232092 -0.971808  0.06426 -1.384637   \n",
       "\n",
       "   LandContour  Utilities  LotConfig  LandSlope      ...        OpenPorchSF  \\\n",
       "0      0.31870        0.0   0.572445  -0.207992      ...          -0.701628   \n",
       "1      0.31870        0.0  -1.950811  -0.207992      ...          -0.178826   \n",
       "2      0.31870        0.0   0.572445  -0.207992      ...          -0.207871   \n",
       "3      0.31870        0.0   0.572445  -0.207992      ...          -0.178826   \n",
       "4     -2.53396        0.0   0.572445  -0.207992      ...           0.489198   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea    MiscVal    MoSold  \\\n",
       "0      -0.360738  -0.088827     1.818960 -0.057227  -0.092244 -0.038281   \n",
       "1      -0.360738  -0.088827    -0.301543 -0.057227  19.730438 -0.038281   \n",
       "2      -0.360738  -0.088827    -0.301543 -0.057227  -0.092244 -1.140614   \n",
       "3      -0.360738  -0.088827    -0.301543 -0.057227  -0.092244 -0.038281   \n",
       "4      -0.360738  -0.088827     2.243060 -0.057227  -0.092244 -1.875504   \n",
       "\n",
       "     YrSold  SaleType  SaleCondition  \n",
       "0  1.713905   0.32475       0.201383  \n",
       "1  1.713905   0.32475       0.201383  \n",
       "2  1.713905   0.32475       0.201383  \n",
       "3  1.713905   0.32475       0.201383  \n",
       "4  1.713905   0.32475       0.201383  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 75) (1460,) (1459, 75)\n"
     ]
    }
   ],
   "source": [
    "xtrain = X\n",
    "xtest= x\n",
    "print(xtrain.shape, ytrain.shape, xtest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forrest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_regr = RandomForestRegressor(max_depth=10, random_state=0, n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_regr.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_pred = rf_regr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975943684219905"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_regr.score (xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_sp = rf_regr.predict(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975943684219905"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytrain, rf_sp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submission Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tunning Random Forrests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_tuple=list(zip(X.columns,rf_regr.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_imp=pd.DataFrame(features_tuple,columns=[\"Feature Names\",\"Importance\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_imp=feature_imp.sort_values(\"Importance\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "sns.barplot(x=\"Feature Names\",y=\"Importance\", data=feature_imp, color='g')\n",
    "plt.xlabel(\"House Price Features\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Random Forest Regressor - Features Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 20, 'min_samples_leaf': 1, 'n_estimators': 54},\n",
       " 0.8608520246886728)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid1 = {\"n_estimators\" : [9, 18, 27, 36, 45, 54, 63, 72, 81, 90],\n",
    "           \"max_depth\" : [1, 5, 10, 15, 20, 25, 30],\n",
    "           \"min_samples_leaf\" : [1, 2, 4, 6, 8, 10]}\n",
    "\n",
    "RF = RandomForestRegressor(random_state=0)\n",
    "# Instantiate the GridSearchCV object: logreg_cv\n",
    "RF_cv1 = GridSearchCV(RF, param_grid1, cv=5,scoring='r2',n_jobs=4)\n",
    "\n",
    "# Fit it to the data\n",
    "RF_cv1.fit(xtrain,ytrain)\n",
    "\n",
    "#RF_cv1.cv_results_, \n",
    "RF_cv1.best_params_, RF_cv1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 17, 'min_samples_leaf': 1, 'n_estimators': 54},\n",
       " 0.862145488123257)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid2 = {\"n_estimators\" : [45,48,51,54,57,60,63],\n",
    "           \"max_depth\" : [16,17,18,19,20,21,22,23,24],\n",
    "           \"min_samples_leaf\" : [1,2,3,4]} \n",
    "\n",
    "RF = RandomForestRegressor(random_state=0)\n",
    "# Instantiate the GridSearchCV object: logreg_cv\n",
    "RF_cv2 = GridSearchCV(RF, param_grid2, cv=5,scoring='r2',n_jobs=4)\n",
    "\n",
    "# Fit it to the data\n",
    "RF_cv2.fit(xtrain,ytrain)\n",
    "\n",
    "#RF_cv2.grid_scores_, \n",
    "RF_cv2.best_params_, RF_cv2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tuned Random Forrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_tuned = RF_cv2.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=17,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=54, n_jobs=None,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_tuned.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_tpred = RF_tuned.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9815376283213944"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_tuned.score(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = RF_tuned.predict(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9815376283213944"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytrain, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sub_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sub_df['Id'] =range(1461, 2920) \n",
    "Sub_df['SalePrice'] = RF_tpred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sub_df.to_csv('Submission.csv', index=False, header=True, columns=['Id','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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