House_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# House Price Prediction End to End Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Shape:  (1460, 81)\n",
      "Test Data Shape:  (1459, 80)\n"
     ]
    }
   ],
   "source": [
    "df_train = pd.read_csv('train 2.csv')\n",
    "df_test= pd.read_csv('test.csv')\n",
    "print(\"Train Data Shape: \",df_train.shape)\n",
    "print(\"Test Data Shape: \",df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to see each column and row\n",
    "# pd.set_option('display.max_columns', None)\n",
    "# pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "4       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 81)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining train and test data into 1\n",
    "# concatination of data\n",
    "df = pd.concat([df_train, df_test])\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500.0</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>181500.0</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>223500.0</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>140000.0</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>250000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal   208500.0  \n",
       "1   2007        WD         Normal   181500.0  \n",
       "2   2008        WD         Normal   223500.0  \n",
       "3   2006        WD        Abnorml   140000.0  \n",
       "4   2008        WD         Normal   250000.0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</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>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</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>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</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>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</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>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</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",
       "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "1454         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  MnPrv        Shed     700   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "1454      6   2006        WD         Normal        NaN  \n",
       "1455      4   2006        WD        Abnorml        NaN  \n",
       "1456      9   2006        WD        Abnorml        NaN  \n",
       "1457      7   2006        WD         Normal        NaN  \n",
       "1458     11   2006        WD         Normal        NaN  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2919 entries, 0 to 1458\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             2919 non-null   int64  \n",
      " 1   MSSubClass     2919 non-null   int64  \n",
      " 2   MSZoning       2915 non-null   object \n",
      " 3   LotFrontage    2433 non-null   float64\n",
      " 4   LotArea        2919 non-null   int64  \n",
      " 5   Street         2919 non-null   object \n",
      " 6   Alley          198 non-null    object \n",
      " 7   LotShape       2919 non-null   object \n",
      " 8   LandContour    2919 non-null   object \n",
      " 9   Utilities      2917 non-null   object \n",
      " 10  LotConfig      2919 non-null   object \n",
      " 11  LandSlope      2919 non-null   object \n",
      " 12  Neighborhood   2919 non-null   object \n",
      " 13  Condition1     2919 non-null   object \n",
      " 14  Condition2     2919 non-null   object \n",
      " 15  BldgType       2919 non-null   object \n",
      " 16  HouseStyle     2919 non-null   object \n",
      " 17  OverallQual    2919 non-null   int64  \n",
      " 18  OverallCond    2919 non-null   int64  \n",
      " 19  YearBuilt      2919 non-null   int64  \n",
      " 20  YearRemodAdd   2919 non-null   int64  \n",
      " 21  RoofStyle      2919 non-null   object \n",
      " 22  RoofMatl       2919 non-null   object \n",
      " 23  Exterior1st    2918 non-null   object \n",
      " 24  Exterior2nd    2918 non-null   object \n",
      " 25  MasVnrType     1153 non-null   object \n",
      " 26  MasVnrArea     2896 non-null   float64\n",
      " 27  ExterQual      2919 non-null   object \n",
      " 28  ExterCond      2919 non-null   object \n",
      " 29  Foundation     2919 non-null   object \n",
      " 30  BsmtQual       2838 non-null   object \n",
      " 31  BsmtCond       2837 non-null   object \n",
      " 32  BsmtExposure   2837 non-null   object \n",
      " 33  BsmtFinType1   2840 non-null   object \n",
      " 34  BsmtFinSF1     2918 non-null   float64\n",
      " 35  BsmtFinType2   2839 non-null   object \n",
      " 36  BsmtFinSF2     2918 non-null   float64\n",
      " 37  BsmtUnfSF      2918 non-null   float64\n",
      " 38  TotalBsmtSF    2918 non-null   float64\n",
      " 39  Heating        2919 non-null   object \n",
      " 40  HeatingQC      2919 non-null   object \n",
      " 41  CentralAir     2919 non-null   object \n",
      " 42  Electrical     2918 non-null   object \n",
      " 43  1stFlrSF       2919 non-null   int64  \n",
      " 44  2ndFlrSF       2919 non-null   int64  \n",
      " 45  LowQualFinSF   2919 non-null   int64  \n",
      " 46  GrLivArea      2919 non-null   int64  \n",
      " 47  BsmtFullBath   2917 non-null   float64\n",
      " 48  BsmtHalfBath   2917 non-null   float64\n",
      " 49  FullBath       2919 non-null   int64  \n",
      " 50  HalfBath       2919 non-null   int64  \n",
      " 51  BedroomAbvGr   2919 non-null   int64  \n",
      " 52  KitchenAbvGr   2919 non-null   int64  \n",
      " 53  KitchenQual    2918 non-null   object \n",
      " 54  TotRmsAbvGrd   2919 non-null   int64  \n",
      " 55  Functional     2917 non-null   object \n",
      " 56  Fireplaces     2919 non-null   int64  \n",
      " 57  FireplaceQu    1499 non-null   object \n",
      " 58  GarageType     2762 non-null   object \n",
      " 59  GarageYrBlt    2760 non-null   float64\n",
      " 60  GarageFinish   2760 non-null   object \n",
      " 61  GarageCars     2918 non-null   float64\n",
      " 62  GarageArea     2918 non-null   float64\n",
      " 63  GarageQual     2760 non-null   object \n",
      " 64  GarageCond     2760 non-null   object \n",
      " 65  PavedDrive     2919 non-null   object \n",
      " 66  WoodDeckSF     2919 non-null   int64  \n",
      " 67  OpenPorchSF    2919 non-null   int64  \n",
      " 68  EnclosedPorch  2919 non-null   int64  \n",
      " 69  3SsnPorch      2919 non-null   int64  \n",
      " 70  ScreenPorch    2919 non-null   int64  \n",
      " 71  PoolArea       2919 non-null   int64  \n",
      " 72  PoolQC         10 non-null     object \n",
      " 73  Fence          571 non-null    object \n",
      " 74  MiscFeature    105 non-null    object \n",
      " 75  MiscVal        2919 non-null   int64  \n",
      " 76  MoSold         2919 non-null   int64  \n",
      " 77  YrSold         2919 non-null   int64  \n",
      " 78  SaleType       2918 non-null   object \n",
      " 79  SaleCondition  2919 non-null   object \n",
      " 80  SalePrice      1460 non-null   float64\n",
      "dtypes: float64(12), int64(26), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index(\"Id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int Feature Size:  25\n",
      "Int Feature Names:  ['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set of integer features\n",
    "\n",
    "int_feature = df.select_dtypes(include=['int64']).columns\n",
    "print(\"Int Feature Size: \", int_feature.shape[0])\n",
    "print(\"Int Feature Names: \", int_feature.tolist())\n",
    "type(int_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Float Feature Size:  12\n",
      "Float Features Name:  ['LotFrontage', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'SalePrice']\n"
     ]
    }
   ],
   "source": [
    "# set of float features\n",
    "\n",
    "float_feature = df.select_dtypes(include=['float64']).columns\n",
    "print(\"Float Feature Size: \",float_feature.shape[0])\n",
    "print(\"Float Features Name: \",float_feature.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Feature Size:  43\n",
      "Categorical Features Name:  ['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n"
     ]
    }
   ],
   "source": [
    "# set of categorical features\n",
    "\n",
    "categorical_feature = df.select_dtypes(include=['object']).columns\n",
    "print(\"Categorical Feature Size: \",categorical_feature.shape[0])\n",
    "print(\"Categorical Features Name: \",categorical_feature.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</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>2919.000000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2896.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.137718</td>\n",
       "      <td>69.305795</td>\n",
       "      <td>10168.114080</td>\n",
       "      <td>6.089072</td>\n",
       "      <td>5.564577</td>\n",
       "      <td>1971.312778</td>\n",
       "      <td>1984.264474</td>\n",
       "      <td>102.201312</td>\n",
       "      <td>441.423235</td>\n",
       "      <td>49.582248</td>\n",
       "      <td>...</td>\n",
       "      <td>93.709832</td>\n",
       "      <td>47.486811</td>\n",
       "      <td>23.098321</td>\n",
       "      <td>2.602261</td>\n",
       "      <td>16.062350</td>\n",
       "      <td>2.251799</td>\n",
       "      <td>50.825968</td>\n",
       "      <td>6.213087</td>\n",
       "      <td>2007.792737</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.517628</td>\n",
       "      <td>23.344905</td>\n",
       "      <td>7886.996359</td>\n",
       "      <td>1.409947</td>\n",
       "      <td>1.113131</td>\n",
       "      <td>30.291442</td>\n",
       "      <td>20.894344</td>\n",
       "      <td>179.334253</td>\n",
       "      <td>455.610826</td>\n",
       "      <td>169.205611</td>\n",
       "      <td>...</td>\n",
       "      <td>126.526589</td>\n",
       "      <td>67.575493</td>\n",
       "      <td>64.244246</td>\n",
       "      <td>25.188169</td>\n",
       "      <td>56.184365</td>\n",
       "      <td>35.663946</td>\n",
       "      <td>567.402211</td>\n",
       "      <td>2.714762</td>\n",
       "      <td>1.314964</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>59.000000</td>\n",
       "      <td>7478.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1953.500000</td>\n",
       "      <td>1965.000000</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>4.000000</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>68.000000</td>\n",
       "      <td>9453.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</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>80.000000</td>\n",
       "      <td>11570.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>733.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>70.000000</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>1526.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1424.000000</td>\n",
       "      <td>742.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>17000.000000</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  2919.000000  2433.000000    2919.000000  2919.000000  2919.000000   \n",
       "mean     57.137718    69.305795   10168.114080     6.089072     5.564577   \n",
       "std      42.517628    23.344905    7886.996359     1.409947     1.113131   \n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    59.000000    7478.000000     5.000000     5.000000   \n",
       "50%      50.000000    68.000000    9453.000000     6.000000     5.000000   \n",
       "75%      70.000000    80.000000   11570.000000     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  2919.000000   2919.000000  2896.000000  2918.000000  2918.000000  ...   \n",
       "mean   1971.312778   1984.264474   102.201312   441.423235    49.582248  ...   \n",
       "std      30.291442     20.894344   179.334253   455.610826   169.205611  ...   \n",
       "min    1872.000000   1950.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%    1953.500000   1965.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%    1973.000000   1993.000000     0.000000   368.500000     0.000000  ...   \n",
       "75%    2001.000000   2004.000000   164.000000   733.000000     0.000000  ...   \n",
       "max    2010.000000   2010.000000  1600.000000  5644.000000  1526.000000  ...   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
       "count  2919.000000  2919.000000    2919.000000  2919.000000  2919.000000   \n",
       "mean     93.709832    47.486811      23.098321     2.602261    16.062350   \n",
       "std     126.526589    67.575493      64.244246    25.188169    56.184365   \n",
       "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "50%       0.000000    26.000000       0.000000     0.000000     0.000000   \n",
       "75%     168.000000    70.000000       0.000000     0.000000     0.000000   \n",
       "max    1424.000000   742.000000    1012.000000   508.000000   576.000000   \n",
       "\n",
       "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
       "count  2919.000000   2919.000000  2919.000000  2919.000000    1460.000000  \n",
       "mean      2.251799     50.825968     6.213087  2007.792737  180921.195890  \n",
       "std      35.663946    567.402211     2.714762     1.314964   79442.502883  \n",
       "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
       "25%       0.000000      0.000000     4.000000  2007.000000  129975.000000  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
       "max     800.000000  17000.000000    12.000000  2010.000000  755000.000000  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical Insignts\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Id'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass          0\n",
       "MSZoning            4\n",
       "LotFrontage       486\n",
       "LotArea             0\n",
       "Street              0\n",
       "                 ... \n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            1\n",
       "SaleCondition       0\n",
       "SalePrice        1459\n",
       "Length: 80, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_count = df.isnull().sum()\n",
    "null_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass        0.000000\n",
       "MSZoning          0.137033\n",
       "LotFrontage      16.649538\n",
       "LotArea           0.000000\n",
       "Street            0.000000\n",
       "                   ...    \n",
       "MoSold            0.000000\n",
       "YrSold            0.000000\n",
       "SaleType          0.034258\n",
       "SaleCondition     0.000000\n",
       "SalePrice        49.982871\n",
       "Length: 80, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# null values in feature percentage calculation\n",
    "\n",
    "null_percentage = df.isnull().sum()/df.shape[0]*100 # null values rows total / total rows * 100\n",
    "null_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alley          93.216855\n",
      "MasVnrType     60.500171\n",
      "PoolQC         99.657417\n",
      "Fence          80.438506\n",
      "MiscFeature    96.402878\n",
      "dtype: float64\n",
      "Index(['Alley', 'MasVnrType', 'PoolQC', 'Fence', 'MiscFeature'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# If the null values in a perticular feature will be more than 50% we will deleate that feature -> threshold > 50%\n",
    "a = null_percentage > 50 # returns true and false for each feature\n",
    "threshold_features = null_percentage[null_percentage>50]    # returns columns with true condition \n",
    "print(threshold_features)\n",
    "print(threshold_features.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But these values aren't null values they hold meaning as menthioned in the data description file\n",
    "\n",
    "Hence there is no feature that need's to be dropped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total missing values:  35\n"
     ]
    }
   ],
   "source": [
    "missing_value_feat = null_percentage[null_percentage>0]\n",
    "print(\"Total missing values: \",len(missing_value_feat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " we will handle only Int and Float null values because Object Null values doesn't mean value is unknow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorical NA values:  23\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MSZoning         0.137033\n",
       "Alley           93.216855\n",
       "Utilities        0.068517\n",
       "Exterior1st      0.034258\n",
       "Exterior2nd      0.034258\n",
       "MasVnrType      60.500171\n",
       "BsmtQual         2.774923\n",
       "BsmtCond         2.809181\n",
       "BsmtExposure     2.809181\n",
       "BsmtFinType1     2.706406\n",
       "BsmtFinType2     2.740665\n",
       "Electrical       0.034258\n",
       "KitchenQual      0.034258\n",
       "Functional       0.068517\n",
       "FireplaceQu     48.646797\n",
       "GarageType       5.378554\n",
       "GarageFinish     5.447071\n",
       "GarageQual       5.447071\n",
       "GarageCond       5.447071\n",
       "PoolQC          99.657417\n",
       "Fence           80.438506\n",
       "MiscFeature     96.402878\n",
       "SaleType         0.034258\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will give us categorical features with NA value\n",
    "cat_na_feature = missing_value_feat[missing_value_feat.keys().isin(categorical_feature)]\n",
    "print(\"categorical NA values: \", len(cat_na_feature))\n",
    "cat_na_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer missing values:  0\n"
     ]
    }
   ],
   "source": [
    "# This will give us int features with missing value\n",
    "\n",
    "int_na_feature = missing_value_feat[missing_value_feat.keys().isin(int_feature)]\n",
    "print(\"Integer missing values: \", len(int_na_feature))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Float missing values:  12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LotFrontage     16.649538\n",
       "MasVnrArea       0.787941\n",
       "BsmtFinSF1       0.034258\n",
       "BsmtFinSF2       0.034258\n",
       "BsmtUnfSF        0.034258\n",
       "TotalBsmtSF      0.034258\n",
       "BsmtFullBath     0.068517\n",
       "BsmtHalfBath     0.068517\n",
       "GarageYrBlt      5.447071\n",
       "GarageCars       0.034258\n",
       "GarageArea       0.034258\n",
       "SalePrice       49.982871\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will give us float features with missing value\n",
    "\n",
    "float_na_feature = missing_value_feat[missing_value_feat.keys().isin(float_feature)]\n",
    "print(\"Float missing values: \",len(float_na_feature))\n",
    "float_na_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling MSZoning feature's missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning\n",
       "RL         2265\n",
       "RM          460\n",
       "FV          139\n",
       "RH           26\n",
       "C (all)      25\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MSZoning\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='MSZoning', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df[\"MSZoning\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mvi = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In categorical feature we impute mode value instead of missing value\n",
    "\n",
    "mszoning_mode=df['MSZoning'].mode()[0]\n",
    "df_mvi['MSZoning'].replace(np.nan, mszoning_mode, inplace=True)\n",
    "df_mvi[\"MSZoning\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compareCountPlot(dfOld, dfNew, feature):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.title(\"Old Graph\")\n",
    "    sns.countplot(x=df[feature])\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.title(\"New Graph\")\n",
    "    sns.countplot(x=dfNew[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compareCountPlot(df,df_mvi, 'MSZoning')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Alley feature's missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alley_const = \"NA\"\n",
    "df_mvi['Alley'].replace(np.nan, alley_const, inplace = True)\n",
    "df_mvi['Alley'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compareCountPlot(df,df_mvi, 'Alley')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Utilities feature's missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utilities\n",
       "AllPub    2916\n",
       "NoSeWa       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Utilities'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utilities_mode = df['Utilities'].mode()[0]\n",
    "df_mvi['Utilities'].replace(np.nan, utilities_mode, inplace = True)\n",
    "df_mvi['Utilities'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Exterior1 & Exterior2 feature's missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E1 :  0\n",
      "E2 :  0\n"
     ]
    }
   ],
   "source": [
    "exterior1_mode = df['Exterior1st'].mode()[0]\n",
    "df_mvi['Exterior1st'].replace(np.nan, exterior1_mode, inplace=True)\n",
    "exterior2_mode = df['Exterior2nd'].mode()[0]\n",
    "df_mvi['Exterior2nd'].replace(np.nan, exterior2_mode, inplace=True)\n",
    "print(\"E1 : \",df_mvi['Exterior1st'].isnull().sum())\n",
    "print(\"E2 : \",df_mvi['Exterior2nd'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling MasVnrType & MasVnrArea feature's missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MasVnrType_mode = df['MasVnrType'].mode()[0]\n",
    "df_mvi['MasVnrType'].replace(np.nan, MasVnrType_mode, inplace=True)\n",
    "df_mvi['MasVnrType'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MasVnrArea_mode = 0\n",
    "df_mvi['MasVnrArea'].replace(np.nan, MasVnrArea_mode, inplace=True)\n",
    "df_mvi['MasVnrArea'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling Bsmt feature's missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Id'>"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_bsmt_feat = [\"BsmtQual\", \"BsmtCond\", \"BsmtExposure\", \"BsmtFinType1\", \"BsmtFinType2\"]\n",
    "sns.heatmap(df[category_bsmt_feat].isnull())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Count of BsmtQual : BsmtQual\n",
      "TA    1283\n",
      "Gd    1209\n",
      "Ex     258\n",
      "Fa      88\n",
      "Name: count, dtype: int64\n",
      "Value Count of BsmtCond : BsmtCond\n",
      "TA    2606\n",
      "Gd     122\n",
      "Fa     104\n",
      "Po       5\n",
      "Name: count, dtype: int64\n",
      "Value Count of BsmtExposure : BsmtExposure\n",
      "No    1904\n",
      "Av     418\n",
      "Gd     276\n",
      "Mn     239\n",
      "Name: count, dtype: int64\n",
      "Value Count of BsmtFinType1 : BsmtFinType1\n",
      "Unf    851\n",
      "GLQ    849\n",
      "ALQ    429\n",
      "Rec    288\n",
      "BLQ    269\n",
      "LwQ    154\n",
      "Name: count, dtype: int64\n",
      "Value Count of BsmtFinType2 : BsmtFinType2\n",
      "Unf    2493\n",
      "Rec     105\n",
      "LwQ      87\n",
      "BLQ      68\n",
      "ALQ      52\n",
      "GLQ      34\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in category_bsmt_feat:\n",
    "    print(f\"Value Count of {i} : {df[i].value_counts()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BsmtQual :  0\n",
      "BsmtCond :  0\n",
      "BsmtExposure :  0\n",
      "BsmtFinType1 :  0\n",
      "BsmtFinType2 :  0\n"
     ]
    }
   ],
   "source": [
    "bsmt_na = \"NA\"\n",
    "for i in category_bsmt_feat:\n",
    "    df_mvi[i].replace(np.nan, bsmt_na, inplace = True)\n",
    "    print(f\"{i} : \",df_mvi[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Functional\n",
       "Typ     2717\n",
       "Min2      70\n",
       "Min1      65\n",
       "Mod       35\n",
       "Maj1      19\n",
       "Maj2       9\n",
       "Sev        2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Functional'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_mode = df['Functional'].mode()[0]\n",
    "df_mvi['Functional'].replace(np.nan, function_mode, inplace = True)\n",
    "df_mvi['Functional'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SaleType\n",
       "WD       2525\n",
       "New       239\n",
       "COD        87\n",
       "ConLD      26\n",
       "CWD        12\n",
       "ConLI       9\n",
       "ConLw       8\n",
       "Oth         7\n",
       "Con         5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SaleType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saletype_mode = df['SaleType'].mode()[0]\n",
    "df_mvi['SaleType'].replace(np.nan, saletype_mode, inplace = True)\n",
    "df_mvi['SaleType'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "remain_cat_feat =[\"FireplaceQu\", \"PoolQC\", \"Fence\", \"MiscFeature\"]\n",
    "other_mode = 'NA'\n",
    "for i in remain_cat_feat:\n",
    "    df_mvi[i].replace(np.nan, other_mode, inplace=True)\n",
    "    print(df_mvi[i].isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "garage_cat=[\"GarageType\", \"GarageFinish\", \"GarageQual\", \"GarageCond\"]      \n",
    "garage_mode = 'NA'\n",
    "for i in garage_cat:\n",
    "    df_mvi[i].replace(np.nan, garage_mode, inplace=True)\n",
    "    print(df_mvi[i].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Float Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling LotFrontage feature's missing value\n",
    "\n",
    "We will replace Null value with mean or median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to see the distribution of the feature \n",
    "\n",
    "def boxdist(df):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.title(\"Box Plot\")\n",
    "    sns.boxplot(x=df)\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.title(\"DistPlot\")\n",
    "    sns.distplot(x=df, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rq/r_ft24217fz3fhtg64fs6l7c0000gn/T/ipykernel_13952/2456860723.py:10: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(x=df, kde = True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxdist(df['LotFrontage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 657,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotfrontage_median = df['LotFrontage'].median()\n",
    "df_mvi['LotFrontage'].replace(np.nan, lotfrontage_median, inplace = True)\n",
    "df_mvi['LotFrontage'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to compare the distribution of feature with new and old data\n",
    "\n",
    "def compareBoxDist(df, df_mvi, feature):\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    plt.subplot(2,2,1)\n",
    "    plt.title(\"Old Distribution\")\n",
    "    sns.boxplot(x=df[feature])\n",
    "    plt.subplot(2,2,2)\n",
    "    plt.title(\"Old Distribution\")\n",
    "    sns.distplot(df[feature])\n",
    "    plt.subplot(2,2,3)\n",
    "    plt.title(\"New Distribution\")\n",
    "    sns.boxplot(x=df_mvi[feature])\n",
    "    plt.subplot(2,2,4)\n",
    "    plt.title(\"New Distribution\")\n",
    "    sns.distplot(df_mvi[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rq/r_ft24217fz3fhtg64fs6l7c0000gn/T/ipykernel_13952/2070408562.py:10: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df[feature])\n",
      "/var/folders/rq/r_ft24217fz3fhtg64fs6l7c0000gn/T/ipykernel_13952/2070408562.py:16: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df_mvi[feature])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compareBoxDist(df, df_mvi, 'LotFrontage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "float_bsmt_feat = [\"BsmtFinSF1\", \"BsmtFinSF2\", \"BsmtUnfSF\", \"TotalBsmtSF\", \"BsmtFullBath\", \"BsmtHalfBath\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Id'>"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hJCQkoGPHjjAzM4OlpSXatm2LJ0+eKJT5/fff4e3tDRMTE1hbW6NXr14V+BREREQqxlxC4vPuu+/izz//FD7r6/+v2QkJCejSpQvCwsLwww8/QF9fH6dPn4ZU+r9YbNu2bRgxYgS++eYbdOzYEc+ePcO5c+cq9BmIiIhUivuwiI++vj4cHByUXgsJCcG4ceMU9lVxd3cX/v3s2TOMHz8e8+fPx7Bhw4TzDRo0UF+DiYiISKVEPyQEAJcvX4aTkxNq1qyJgQMHIjU1FQBw9+5dHDt2DHZ2dmjZsiXs7e3Rrl07HDlyRPhuUlISbt++DalUCg8PDzg6OiIgIIA9LEREpN00OCS0dOlSuLq6wtjYGN7e3jh+/PhLyy9evBju7u4wMTGBs7MzQkJC8PTp09e6p+gDFm9vb0RFRSE6OhrLly/HtWvX0KZNGzx69AhXr14FUDzPZcSIEYiOjkazZs3QqVMnXL58GQAUykybNg179+6FtbU12rdvj8zMzDLvm5+fj5ycHIWDe+wREZFoyOWqO17Dpk2bEBoaivDwcCQlJaFJkybw9/fH3bt3lZbfsGEDpkyZgvDwcJw/fx6rV6/Gpk2bMHXq1Ne6r+gDloCAAPTp0weNGzeGv78/9u3bh6ysLGzevFlIYvjpp59i6NCh8PDwwKJFi+Du7o41a9YA+F+iwy+//BK9e/eGp6cnIiMjIZFIsGXLljLvGxERASsrK4VDLnuk/gcmIiISsYULF2LEiBEYOnQoGjRogBUrVsDU1FT4vfuif/75B61atcKAAQPg6uoKPz8/9O/f/5W9Mi8SfcDyoipVqqBu3bpISUkRMjK/OB+lfv36wrCRsjJGRkaoWbOmUEaZsLAwZGdnKxwSqYWqH4eIiOjNqHBISNmoQn5+fqlbFhQUIDExEb6+vsI5qVQKX19fJCQkKG1my5YtkZiYKAQoV69exb59+9C1a9fXelytC1hyc3Nx5coVODo6wtXVFU5OTrh48aJCmUuXLsHFxQUA4OnpCSMjI4UyhYWFuH79ulBGGSMjI1haWioczNZMRESiocKARdmoQkRERKlb3r9/H0VFRbC3t1c4b29vj/T0dKXNHDBgAGbPno3WrVvDwMAAtWrVQvv27V97SEj0q4QmTpyI7t27w8XFBWlpaQgPD4eenh769+8PiUSCSZMmITw8HE2aNEHTpk2xdu1aXLhwAVu3bgUAWFpaYtSoUQgPD4ezszNcXFwwf/58AECfPn00+WhERESiEBYWhtDQUIVzRkZGKqk7Li4O33zzDZYtWwZvb2+kpKRg/Pjx+OqrrzB9+vRy1yP6gOXWrVvo378/Hjx4AFtbW7Ru3RpHjx6Fra0tAGDChAl4+vQpQkJCkJmZiSZNmiAmJga1atUS6pg/fz709fUxePBgPHnyBN7e3oiNjYW1tbWmHouIiOjtqHAfFiMjo3IFKNWqVYOenh4yMjIUzmdkZJS5/cj06dMxePBgDB8+HADQqFEj5OXlYeTIkfjyyy8V9k17GdEHLBs3bnxlmSlTpijsw/IiAwMDLFiwAAsWLFBl04iIiDRGLqv4lauGhobw9PTEwYMHhR3jZTIZDh48iDFjxij9zuPHj0sFJXp6egDwWqtvRR+wEBERkXiEhoYiMDAQzZs3x3vvvYfFixcjLy8PQ4cOBQAMGTIE1atXF+bAdO/eHQsXLoSHh4cwJDR9+nR0795dCFzKgwELERGRNtJQDqB+/frh3r17mDFjBtLT09G0aVNER0cLE3FTU1MVelSmTZsGiUSCadOm4fbt27C1tUX37t3x9ddfv9Z9JXLuhlZu+obVNd0EIiLSEs8Kbqu1/sfLx6qsLtPRP6isLnXRimXNt2/fxqBBg1C1alWYmJigUaNGOHnypHD9xWzOJUfJaiCgeIv+zp07o0qVKqhatSpGjhyJ3NxcTTwOERHR25PJVXdoAdEHLA8fPkSrVq1gYGCAP/74A//99x++++47hRU+d+7cUTjWrFkDiUSC3r17AwDS0tLg6+uL2rVr49ixY4iOjsa///6LoKAgDT0VERERvQ7Rz2GZN28enJ2dERkZKZxzc3NTKPPiUqpdu3ahQ4cOqFmzJgBg7969MDAwwNKlS4VxtRUrVqBx48ZISUlB7dq11fwUREREKqahOSyaIvoelt27d6N58+bo06cP7Ozs4OHhgVWrVpVZPiMjA7///juGDRsmnMvPz4ehoaHCJCATExMAUMjsTEREpDU0mK1ZE0QfsFy9ehXLly9HnTp1sH//fowePRrjxo3D2rVrlZZfu3YtLCws8OGHHwrnOnbsiPT0dMyfPx8FBQV4+PChsG/LnTt3lNbDbM1ERETiIfqARSaToVmzZvjmm2/g4eGBkSNHYsSIEVixYoXS8mvWrMHAgQNhbGwsnHv33Xexdu1afPfddzA1NYWDgwPc3Nxgb29f5g57zNZMRESiJper7tACog9YHB0dX5qN+XmHDx/GxYsXhe1/nzdgwACkp6fj9u3bePDgAWbOnIl79+4J81xexGzNREQkajo2JCT6SbetWrV6aTbm561evRqenp5o0qRJmfWVbGyzZs0aGBsbo3PnzkrLKcurwGzNREREmiH6gCUkJAQtW7bEN998g759++L48eNYuXIlVq5cqVAuJycHW7ZswXfffae0nh9//BEtW7aEubk5YmJiMGnSJMydOxdVqlSpgKcgIiJSMS3ZP0VVRB+weHl5YceOHQgLC8Ps2bPh5uaGxYsXY+DAgQrlNm7cCLlcjv79+yut5/jx4wgPD0dubi7q1auHn376CYMHD66IRyAiIlI9FWZr1gbcmv81cGt+IiIqL7VvzT//E5XVZTppjcrqUhfR97AQERGREhwSIiIiIrGTa8nqHlVhwEJERKSNdKyHRfT7sLi6uirNxBwcHAwAuHLlCj744APY2trC0tISffv2RUZGhvD969evY9iwYXBzc4OJiQlq1aqF8PBwFBQUaOqRiIiI6DWJPmA5ceKEQibmmJgYAECfPn2Ql5cHPz8/SCQSxMbG4u+//0ZBQQG6d+8O2f93lV24cAEymQw//fQT/v33XyxatAgrVqzA1KlTNflYREREb0cuU92hBbRuldCECROwd+9eXL58GTExMQgICMDDhw9haWkJAMjOzoa1tTUOHDgAX19fpXXMnz8fy5cvx9WrV1/r3lwlRERE5aXuVUJ5swe+ulA5mc34VWV1qYvoe1ieV1BQgPXr1+OTTz6BRCJBfn4+JBKJwo60xsbGkEqlL83CnJ2dDRsbm4poMhEREamAVgUsO3fuRFZWFoKCggAALVq0gJmZGSZPnozHjx8jLy8PEydORFFRUZlZmFNSUvDDDz/g008/rcCWExERqZiO5RLSqoBl9erVCAgIgJOTEwDA1tYWW7ZswZ49e2Bubg4rKytkZWWhWbNmSrMw3759G126dEGfPn0wYsSIl94rPz8fOTk5CoeWjZ4REVFlJpOr7tACWrOs+caNG/jzzz+xfft2hfN+fn64cuUK7t+/D319fVSpUgUODg6lsjCnpaWhQ4cOaNmyZak8RMpERERg1qxZCuckUnNI9Czf/mGIiIjotWhND0tkZCTs7OzQrVs3pderVauGKlWqIDY2Fnfv3kWPHj2Ea7dv30b79u3h6emJyMhIpb0vLwoLC0N2drbCIZFaqOx5iIiI3oqOrRLSih4WmUyGyMhIBAYGQl9fscmRkZGoX78+bG1tkZCQgPHjxyMkJATu7u4A/hesuLi4YMGCBbh3757wXQcHhzLvaWRkpDCZFwAkEokKn4qIiOgtaMlQjqpoRcDy559/IjU1FZ98UjrR08WLFxEWFobMzEy4urriyy+/REhIiHA9JiYGKSkpSElJwTvvvKPwXc5JISIi0g5atw+LJnEfFiIiKi9178OSG9ZbZXWZR2xTWV3qohU9LERERPQCDgkRERGR6OlYwKI1q4SIiIhId2lFwPLo0SNMmDABLi4uMDExQcuWLXHixAnhulwux4wZM+Do6AgTExP4+vri8uXLCnUoy/o8d+7cin4UIiIi1dCxZc1aEbAMHz4cMTExWLduHc6ePQs/Pz/4+vri9u3iCU3ffvstlixZghUrVuDYsWMwMzODv78/nj59qlDP7NmzFTI/jx07VhOPQ0RE9PZ0bKdb0QcsT548wbZt2/Dtt9+ibdu2qF27NmbOnInatWtj+fLlkMvlWLx4MaZNm4aePXuicePG+OWXX5CWloadO3cq1GVhYQEHBwfhMDMz08xDERER0WsRfcDy7NkzFBUVwdjYWOG8iYkJjhw5gmvXriE9PR2+vr7CNSsrK3h7eyMhIUHhO3PnzkXVqlXh4eGB+fPn49mzZxXyDERERKoml8lVdmgD0a8SsrCwgI+PD7766ivUr18f9vb2+O2335CQkIDatWsjPT0dAGBvb6/wPXt7e+EaAIwbNw7NmjWDjY0N/vnnH4SFheHOnTtYuHBhhT4PERGRSmhJoKEqog9YAGDdunX45JNPUL16dejp6aFZs2bo378/EhMTy11HaGio8O/GjRvD0NAQn376KSIiIkptwQ8UZ2vOz89XOCeXy7k9PxERkQaIfkgIAGrVqoX4+Hjk5ubi5s2bOH78OAoLC1GzZk0hH1BGRobCdzIyMl6aK8jb2xvPnj3D9evXlV6PiIiAlZWVwiGXPVLZMxEREb0VmUx1hxbQioClhJmZGRwdHfHw4UPs378fPXv2hJubGxwcHHDw4EGhXE5ODo4dOwYfH58y60pOToZUKoWdnZ3S68zWTEREoqZjq4S0Ykho//79kMvlcHd3R0pKCiZNmoR69eph6NChkEgkmDBhAubMmYM6derAzc0N06dPh5OTE3r16gUASEhIwLFjx9ChQwdYWFggISEBISEhGDRoEKytrZXek9maiYhI1LQk0FAVrQhYsrOzERYWhlu3bsHGxga9e/fG119/DQMDAwDAF198gby8PIwcORJZWVlo3bo1oqOjhZVFRkZG2LhxI2bOnIn8/Hy4ubkhJCREYV4LERERiRezNb8GZmsmIqLyUne25pxP/VVWl+VP+1VWl7poRQ8LERERvUDHhoS0atItERER6Sb2sBAREWkjHethYcBCRESkhbRlS31V0eiQ0KFDh9C9e3c4OTlBIpGUSlYol8sxY8YMODo6wsTEBL6+vrh8+bJCma+//hotW7aEqakpqlSpovQ+J06cQKdOnVClShVYW1vD398fp0+fVtNTERERkappNGDJy8tDkyZNsHTpUqXXv/32WyxZsgQrVqzAsWPHYGZmBn9/fzx9+lQoU1BQgD59+mD06NFK68jNzUWXLl1Qo0YNHDt2DEeOHIGFhQX8/f1RWFioluciIiJSOx3bOE40y5olEgl27NghbPYml8vh5OSEzz//HBMnTgRQvB+Lvb09oqKi8PHHHyt8PyoqChMmTEBWVpbC+ZMnT8LLywupqalwdnYGAJw9exaNGzfG5cuXUbt27XK3kcuaiYiovNS9rDl7cCeV1WW17uCrC2mYaFcJXbt2Denp6fD19RXOWVlZwdvbGwkJCeWux93dHVWrVsXq1atRUFCAJ0+eYPXq1ahfvz5cXV3V0HIiIiJSNdFOuk1PTwcA2NvbK5y3t7cXrpWHhYUF4uLi0KtXL3z11VcAgDp16mD//v3Q1y/78ZmtmYiIxIyTbiuZJ0+eYNiwYWjVqhWOHj2Kv//+Gw0bNkS3bt3w5MmTMr/HbM1ERCRqOjaHRbQBi4ODAwAgIyND4XxGRoZwrTw2bNiA69evIzIyEl5eXmjRogU2bNiAa9euYdeuXWV+j9maiYhI1GQqPLSAaAMWNzc3ODg44ODB/00EysnJwbFjx+Dj41Pueh4/fgypVKowlFPyWSYr+/9KRkZGsLS0VDg4HERERKQZGg1YcnNzkZycjOTkZADFE22Tk5ORmpoKiUSCCRMmYM6cOdi9ezfOnj2LIUOGwMnJSVhJBACpqanCd4qKioT6cnNzAQCdO3fGw4cPERwcjPPnz+Pff//F0KFDoa+vjw4dOmjgqYmIiN6eXCZX2aENNDrp9uTJkwpBQ2hoKAAgMDAQUVFR+OKLL5CXl4eRI0ciKysLrVu3RnR0NIyNjYXvzJgxA2vXrhU+e3h4AAD++usvtG/fHvXq1cOePXswa9Ys+Pj4QCqVwsPDA9HR0XB0dKygJyUiIlIxLRnKURXR7MOiDbgPCxERlZe692F52Lu9yuqy3hansrrURbTLmomIiKhs2jKUoyoMWIiIiLSRjg0JiXaVEBEREVEJUWdr3r59O/z8/FC1alVIJBJhNdHzPv30U9SqVQsmJiawtbVFz549ceHCBYUy48aNg6enJ4yMjNC0aVP1PRAREVEFkctUd2gDUWdrzsvLQ+vWrTFv3rwy6/D09ERkZCTOnz+P/fv3Qy6Xw8/PD0VFRQrlPvnkE/Tr10+l7SciItIYHds4TqNzWAICAhAQEFDm9cGDBwMArl+/XmaZkSNHCv92dXXFnDlz0KRJE1y/fh21atUCACxZsgQAcO/ePZw5c0YFLSciIqKKVKkm3ebl5SEyMhJubm5wdnbWdHOIiIjURluGclSlUky6XbZsGczNzWFubo4//vgDMTExMDQ01HSziIiI1EfHhoQqRcAycOBAnDp1CvHx8ahbty769u2Lp0+fvlWd+fn5yMnJUTi4xx4REYkFJ91qISsrK9SpUwdt27bF1q1bceHCBezYseOt6oyIiICVlZXCIZc9UlGLiYiItNfSpUvh6uoKY2NjeHt74/jx4y8tn5WVheDgYDg6OsLIyAh169bFvn37XuuelSJgeZ5cLodcLkd+fv5b1RMWFobs7GyFQyK1UFEriYiI3o6melg2bdqE0NBQhIeHIykpCU2aNIG/vz/u3r2rtHxBQQE6d+6M69evY+vWrbh48SJWrVqF6tVfL92NRifd5ubmIiUlRfhckq3ZxsYGNWrUQGZmJlJTU5GWlgYAuHjxIgDAwcEBDg4OuHr1KjZt2gQ/Pz/Y2tri1q1bmDt3LkxMTNC1a1eh3pSUFOTm5iI9PR1PnjwR9nNp0KBBmXNdjIyMYGRkpHBOIpGo8vGJiIjemKaGchYuXIgRI0Zg6NChAIAVK1bg999/x5o1azBlypRS5desWYPMzEz8888/MDAwAFC8qvd1aTT5YVxcnEK25hIl2ZqjoqKEF/K88PBwzJw5E2lpaRg+fDgSExPx8OFD2Nvbo23btpgxYwbc3d2F8u3bt0d8fHypeq5du/ZaL43JD4mIqLzUnfwwo0M7ldVVJfpAqZEJZX+4FxQUwNTUFFu3bkWvXr2E84GBgcjKysKuXbtK1d21a1fY2NjA1NQUu3btgq2tLQYMGIDJkydDT0+v3G3UaA9L+/btXzqRNSgoCEFBQWVed3JyKtcYWFxc3Bu0joiISMTkquv1j4iIwKxZsxTOlXQOPO/+/fsoKiqCvb29wnl7e/tSu8yXuHr1KmJjYzFw4EDs27cPKSkp+Oyzz1BYWIjw8PByt7FS7cNCRESkK1Q5JBQWFobQ0FCFcy/2rrwpmUwGOzs7rFy5Enp6evD09MTt27cxf/58BixERERUfsqGf5SpVq0a9PT0kJGRoXA+IyMDDg4OSr/j6OgIAwMDheGf+vXrIz09HQUFBeXeN63SrRIiIiLSBXKZRGVHeRkaGsLT0xMHDx4UzslkMhw8eBA+Pj5Kv9OqVSukpKRAJvtfl9ClS5fg6Oj4Wpu8ijZbc2FhISZPnoxGjRrBzMwMTk5OGDJkiLBi6EX5+flo2rRpmVmdgeLVQhYWFqhSpYrqH4aIiKgCaWpZc2hoKFatWoW1a9fi/PnzGD16NPLy8oRFMkOGDEFYWJhQfvTo0cjMzMT48eNx6dIl/P777/jmm28QHBz8WvcVbbbmx48fIykpCdOnT0dSUhK2b9+OixcvokePHkrr+uKLL+Dk5FTmvQoLC9G/f3+0adNGZe0nIiLSNf369cOCBQswY8YMNG3aFMnJyYiOjhYm4qampuLOnTtCeWdnZ+zfvx8nTpxA48aNMW7cOIwfP17pEuiX0eiy5udJJBLs2LFDYZnUi06cOIH33nsPN27cQI0aNYTzf/zxB0JDQ7Ft2za8++67OHXqFJo2barw3cmTJyMtLQ2dOnXChAkTkJWV9dpt5LJmIiIqL3Uva77t01FldVVPiFVZXeqiVZNus7OzIZFIFIZ0MjIyMGLECOzcuROmpqZKvxcbG4stW7YgOTkZ27dvr6DWEhERqY+25ABSFa2ZdPv06VNMnjwZ/fv3h6WlJYDibfiDgoIwatQoNG/eXOn3Hjx4gKCgIERFRQnfIyIi0naamHSrSVrRw1JYWIi+fftCLpdj+fLlwvkffvgBjx49Upjc86IRI0ZgwIABaNu27WvdMz8/v9Suf3K5nNvzExERaYDoe1hKgpUbN24gJiZGoZckNjYWCQkJMDIygr6+PmrXrg0AaN68OQIDA4UyCxYsgL6+PvT19TFs2DBkZ2dDX18fa9asKfO+zNZMRERiJper7tAGop50WxKsXL58GX/99RdsbW0VvpOamoqcnBzhc1paGvz9/bF161Z4e3vjnXfewfnz51FUVCSU2bVrF+bNm4d//vkH1atXh7W1tdL2KOthsa5ajz0sRERULuqedHujma/K6nJJ+lNldamLaLM1Ozo64qOPPkJSUhL27t2LoqIipKenAwBsbGxgaGiosFIIAMzNzQEAtWrVwjvvvAOgeDe95508eRJSqRQNGzZ8aduYrZmIiEg8NBqwnDx5UiFbc0keg8DAQMycORO7d+8GgFJLlP/66y+0b9++oppJREQkOtoyWVZVRDMkpA24DwsREZWXuoeErjXprLK63E7HqKwudRH9pFsiIiIirVjWTERERIp0bUiIAQsREZEWkst1K2DhkBARERGJnkYDlkOHDqF79+5wcnKCRCLBzp07Fa7PnDkT9erVg5mZGaytreHr64tjx44plHF1dYVEIlE45s6dq1DHi9clEgnMzMwq4hGJiIjUQi5T3aENNBqw5OXloUmTJli6dKnS63Xr1sWPP/6Is2fP4siRI3B1dYWfnx/u3bunUG727Nm4c+eOcIwdO1a4NnHiRIVrd+7cQYMGDdCnTx+1PhsREZE6yeQSlR3aQKNzWAICAhAQEFDm9QEDBih8XrhwIVavXo0zZ86gU6dOwnkLCws4ODgorcPc3FzYUA4ATp8+jf/++w8rVqx4y9YTERFpDuewiFRBQQFWrlwJKysrNGnSROHa3LlzUbVqVXh4eGD+/Pl49uxZmfX8/PPPqFu3Ltq0aaPuJhMREZGKiH6V0N69e/Hxxx/j8ePHcHR0RExMDKpVqyZcHzduHJo1awYbGxv8888/CAsLw507d7Bw4cJSdT19+hS//vorpkyZ8sr7MlszERGJGZc1i0yHDh2QnJyM+/fvY9WqVejbty+OHTsGOzs7AP/bzh8AGjduDENDQ3z66aeIiIgolQtox44dePTokZDJ+WUiIiIwa9YshXMSqTkkepZlfIOIiKji6No+9aIfEjIzM0Pt2rXRokULrF69Gvr6+li9enWZ5b29vfHs2TNcv3691LWff/4Z77//Puzt7V9537CwMGRnZyscEqnF2zwKERERvSHR97C8SCaTlRqqeV5ycjKkUqnQA1Pi2rVr+Ouvv4SEiq/CbM1ERCRmHBKqQLm5uUhJSRE+X7t2DcnJybCxsUHVqlXx9ddfo0ePHnB0dMT9+/exdOlS3L59W1iSnJCQgGPHjqFDhw6wsLBAQkICQkJCMGjQIFhbWyvca82aNXB0dHzpqiQiIiJtoS3LkVVFowHLyZMn0aFDB+FzyXyUwMBArFixAhcuXMDatWtx//59VK1aFV5eXjh8+DDeffddAMW9IBs3bsTMmTORn58PNzc3hISEKMxrAYp7ZaKiohAUFAQ9Pb2Ke0AiIiJSCYlcrmvTdt6cvmF1TTeBiIi0xLOC22qt/6xbd5XV1ejaHpXVpS5aN4eFiIiIuEqIiIiISHTYw0JERKSFdG3SraizNT9v1KhRkEgkWLx4sXAuLi5OaSZmiUSCEydOCOXOnDmDNm3awNjYGM7Ozvj222/V+FRERETqJ5dLVHZoA1Fnay6xY8cOHD16FE5OTgrnW7ZsWSoT8/Dhw+Hm5obmzZsDAHJycuDn5wcXFxckJiZi/vz5mDlzJlauXKm25yIiIlI3uVx1hzYo95DQi0uFX0ZZHh9lXpWtGQBu376NsWPHYv/+/ejWrZvCNUNDQ4UszYWFhdi1axfGjh0rbPL266+/oqCgAGvWrIGhoSHeffddJCcnY+HChRg5cmS5n4mIiIg0p9wBy6lTpxQ+JyUl4dmzZ3B3dwcAXLp0CXp6evD09FRZ42QyGQYPHoxJkyYJe6+8zO7du/HgwQMMHTpUOJeQkIC2bdvC0NBQOOfv74958+bh4cOHpTaYIyIi0ga6Noel3AHLX3/9Jfx74cKFsLCwwNq1a4Vf+A8fPsTQoUPRpk0blTVu3rx50NfXx7hx48pVfvXq1fD398c777wjnEtPT4ebm5tCuZJcQunp6QxYiIhIK2nL3BNVeaNVQt999x0OHDig8Mve2toac+bMgZ+fHz7//PO3blhiYiK+//57JCUllSuHz61bt7B//35s3rz5re8NAPn5+aVyFsnlcuYTIiIi0oA3mnSbk5ODe/fulTp/7949PHr06K0bBQCHDx/G3bt3UaNGDejr60NfXx83btzA559/DldX11LlIyMjUbVqVfTo0UPhvIODAzIyMhTOlXx+fv7LiyIiImBlZaVwyGWqeTYiIqK3JZNLVHZogzcKWD744AMMHToU27dvx61bt3Dr1i1s27YNw4YNw4cffqiShg0ePBhnzpxBcnKycDg5OWHSpEnYv3+/Qlm5XI7IyEgMGTIEBgYGCtd8fHxw6NAhFBYWCudiYmLg7u7+0uGgsLAwZGdnKxwSqYVKno2IiOhtyVV4aIM3GhJasWIFJk6ciAEDBqCwsBByuRwGBgYYNmwY5s+fX+56XpatuUaNGqhatapCeQMDAzg4OAgTfUvExsbi2rVrGD58eKl7DBgwALNmzcKwYcMwefJknDt3Dt9//z0WLVr00rYZGRnByMhI4RyHg4iIiDTjjQIWU1NTLFu2DPPnz8eVK1cAALVq1YKZmdlr1fOybM1RUVHlrmf16tVo2bIl6tWrV+qalZUVDhw4gODgYHh6eqJatWqYMWMGlzQTEZFW05ahHFV5rWzN5R3u2b59+xs3SMyYrZmIiMpL3dma/3b4SGV1tUrfqrK61OW1elisrKzU1Q4iIiKiMr1WwBIZGamudhAREdFrkGm6ARWM2ZqJiIi0kBy6NYdF1Nmag4KCSmVh7tKli0KZS5cuoWfPnqhWrRosLS3RunVrhV15Hzx4gC5dusDJyQlGRkZwdnbGmDFjkJOTUxGPSEREpBYyueoObSD6bM1dunRRyMb822+/KVx///338ezZM8TGxiIxMRFNmjTB+++/j/T0dACAVCpFz549sXv3bly6dAlRUVH4888/MWrUKLU+GxEREamORoeEypOt2cjIqMwdae/fv4/Lly9j9erVaNy4MQBg7ty5WLZsGc6dOwcHBwdYW1tj9OjRwndcXFzw2WefvdZ+MURERGIj45CQuMTFxcHOzg7u7u4YPXo0Hjx4IFyrWrUq3N3d8csvvyAvLw/Pnj3DTz/9BDs7uzKzRqelpWH79u1o165dRT0CERGRyskhUdmhDUQdsHTp0gW//PILDh48iHnz5iE+Ph4BAQEoKioCULzz7J9//olTp07BwsICxsbGWLhwIaKjo0ttu9+/f3+YmpqievXqsLS0xM8//6yJRyIiIqI3IOqA5eOPP0aPHj3QqFEj9OrVC3v37sWJEycQFxcHoDiHUHBwMOzs7HD48GEcP34cvXr1Qvfu3XHnzh2FuhYtWoSkpCTs2rULV65cEXbVLUt+fj5ycnIUjtfYY4+IiEitZCo8tIGoA5YX1axZE9WqVRPyD8XGxmLv3r3YuHEjWrVqhWbNmmHZsmUwMTHB2rVrFb7r4OCAevXqoUePHvjpp5+wfPnyUkHN85itmYiIxIxDQiJ269YtPHjwAI6OjgCAx48fAyheCfQ8qVQKmazsmLHkWn5+fpllmK2ZiIhIPDS6Suhl2ZptbGwwa9Ys9O7dGw4ODrhy5Qq++OIL1K5dG/7+/gAAHx8fWFtbIzAwEDNmzICJiQlWrVqFa9euoVu3bgCAffv2ISMjA15eXjA3N8e///6LSZMmoVWrVnB1dS2zbczWTEREYqYtQzmqotEelpMnT8LDwwMeHh4AirM1e3h4YMaMGdDT08OZM2fQo0cP1K1bF8OGDYOnpycOHz4sBBLVqlVDdHQ0cnNz0bFjRzRv3hxHjhzBrl270KRJEwAQgpjWrVujfv36CAkJQY8ePbB3716NPTcREdHb0rU5LK+VrVnXMVszERGVl7qzNe+z/1hldXXN2KiyutSFuYSIiIi0kLZMllUVBixERERaSKZb8QoDFiIiIm3ErfmJiIiIREajAcuhQ4fQvXt3ODk5QSKRYOfOnaXKnD9/Hj169ICVlRXMzMzg5eWF1NRU4fqVK1fwwQcfwNbWFpaWlujbty8yMjJK1fP777/D29sbJiYmsLa2Rq9evdT4ZEREROolV+GhDTQasOTl5aFJkyZYunSp0utXrlxB69atUa9ePcTFxeHMmTOYPn06jI2Nhe/7+flBIpEgNjYWf//9NwoKCtC9e3eFjeO2bduGwYMHY+jQoTh9+jT+/vtvDBgwoEKekYiISB24rFlDJBIJduzYodDz8fHHH8PAwADr1q1T+p0DBw4gICAADx8+hKWlJQAgOzsb1tbWOHDgAHx9ffHs2TO4urpi1qxZGDZs2Fu1kcuaiYiovNS9rHm7g+r+8P4wfYPK6lIX0c5hkclk+P3331G3bl34+/vDzs4O3t7eCsNG+fn5kEgkCjvSGhsbQyqV4siRIwCApKQk3L59G1KpFB4eHnB0dERAQADOnTtX0Y9ERESkMjKJRGWHNhBtwHL37l3k5uZi7ty56NKlCw4cOIAPPvgAH374IeLj4wEALVq0gJmZGSZPnozHjx8jLy8PEydORFFRkZDY8OrVqwCAmTNnYtq0adi7dy+sra3Rvn17ZGZmlnl/ZmsmIiIx4xwWkSiZg9KzZ0+EhISgadOmmDJlCt5//32sWLECAGBra4stW7Zgz549MDc3h5WVFbKystCsWTMhIWJJPV9++SV69+4NT09PREZGQiKRYMuWLWXen9maiYiIxEO0+7BUq1YN+vr6aNCggcL5+vXrC8M9AODn54crV67g/v370NfXR5UqVeDg4ICaNWsCgJDZ+fl6jIyMULNmTYXVRi8KCwtDaGiowjnrqvXe+rmIiIhUQVsmy6qKaAMWQ0NDeHl54eLFiwrnL126BBcXl1Llq1WrBgCIjY3F3bt30aNHDwCAp6cnjIyMcPHiRbRu3RoAUFhYiOvXryutpwSzNRMRkZjp2k63Gh0Sys3NRXJyMpKTkwEA165dQ3JystDzMWnSJGzatAmrVq1CSkoKfvzxR+zZswefffaZUEdkZCSOHj2KK1euYP369ejTpw9CQkLg7u4OALC0tMSoUaMQHh6OAwcO4OLFixg9ejQAoE+fPhX7wERERJXA0qVL4erqCmNjY3h7e+P48ePl+t7GjRshkUjeaC80jfawnDx5Eh06dBA+lwzBBAYGIioqCh988AFWrFiBiIgIjBs3Du7u7ti2bZvQUwIAFy9eRFhYGDIzM+Hq6oovv/wSISEhCveZP38+9PX1MXjwYDx58gTe3t6IjY2FtbV1xTwoERGRimlqa/5NmzYhNDQUK1asgLe3NxYvXgx/f39cvHgRdnZ2ZX7v+vXrmDhxItq0afNG9xXNPizagPuwEBFReal7H5b1ToNUVtegtPXlLuvt7Q0vLy/8+OOPAIoXtzg7O2Ps2LGYMmWK0u8UFRWhbdu2+OSTT3D48GFkZWUp3d3+ZUS7SoiIiIjKJpOo7lC2lUd+fn6pexYUFCAxMRG+vr7COalUCl9fXyQkJJTZ1tmzZ8POzu6tNnBlwEJERKTjlG3lERERUarc/fv3UVRUBHt7e4Xz9vb2SE9PV1r3kSNHsHr1aqxateqt2ijaVUJERERUNlUua1a2lceLK2XfxKNHjzB48GCsWrVKWM37pkSdrTk3NxdjxozBO++8AxMTEzRo0EDYNK7E06dPERwcjKpVq8Lc3By9e/cula354MGDaNmyJSwsLODg4IDJkyfj2bNn6n48IiIitVHlTrdGRkawtLRUOJQFLNWqVYOenl6p37MZGRlwcHAoVf7KlSu4fv06unfvDn19fejr6+OXX37B7t27oa+vjytXrpT7eUWdrTk0NBTR0dFYv349zp8/jwkTJmDMmDHYvXu3UCYkJAR79uzBli1bEB8fj7S0NHz44YfC9dOnT6Nr167o0qULTp06hU2bNmH37t1lTgwiIiIi5QwNDeHp6YmDBw8K52QyGQ4ePAgfH59S5evVq4ezZ88KW5gkJyejR48e6NChA5KTk+Hs7Fzue4tmlZCybM0NGzZEv379MH36dOGcp6cnAgICMGfOHGRnZ8PW1hYbNmzARx99BAC4cOEC6tevj4SEBLRo0QJTp05FTEwMTpw4IdSxZ88e9O3bF3fv3oWFhUW528hVQkREVF7qXiW0+h3VrRIadqv8q4Q2bdqEwMBA/PTTT3jvvfewePFibN68GRcuXIC9vT2GDBmC6tWrK50DAwBBQUGVb5VQy5YtsXv3bty+fRtyuRx//fUXLl26BD8/PwBAYmIiCgsLFWYr16tXDzVq1BBmK+fn58PY2FihXhMTEzx9+hSJiYkV9zBEREQqJFPh8Tr69euHBQsWYMaMGWjatCmSk5MRHR0tTMRNTU0VEhCrkqgn3f7www8YOXIk3nnnHejr60MqlWLVqlVo27YtACA9PR2GhoaoUqWKwveen63s7++PxYsX47fffkPfvn2Rnp6O2bNnA4BaXigREVFlN2bMGIwZM0bptbi4uJd+Nyoq6o3uKeoelh9++AFHjx7F7t27kZiYiO+++w7BwcH4888/y12Hn58f5s+fj1GjRsHIyAh169ZF165dAUDI6KyMsjXpIhk9IyIi0lgPi6aINmB58uQJpk6dioULF6J79+5o3LgxxowZI3RFAYCDgwMKCgqQlZWl8N0XZyuHhoYiKysLqampuH//Pnr27AkAQkZnZZStSZfLHqn+QYmIiN6AXKK6QxuINmApLCxEYWFhqV4QPT09yGTF8aCnpycMDAwUZitfvHgRqamppWYrSyQSODk5wcTEBL/99hucnZ3RrFmzMu8fFhaG7OxshUMiLf8EXSIiIlIdjc5hyc3NRUpKivC5JFuzjY0NatSogXbt2mHSpEkwMTGBi4sL4uPj8csvv2DhwoUAACsrKwwbNgyhoaGwsbGBpaUlxo4dCx8fH7Ro0UKod/78+ejSpQukUim2b9+OuXPnYvPmzdDT0yuzbUZGRqXWoEskWhKGEhFRpactQzmqotFlzXFxcQrZmkuUZGtOT09HWFgYDhw4gMzMTLi4uGDkyJEICQkRgoenT5/i888/x2+//Yb8/Hz4+/tj2bJlCkNCHTt2RFJSEvLz89GkSROEh4cjICDgtdvLZc1ERFRe6l7W/KOz6pY1j7lZ/mXNmiKafVi0AQMWIiIqL3UHLD+oMGAZqwUBi2jnsBARERGVEPU+LERERKScTMemVTJgISIi0kK6NulW1NmaMzIyEBQUBCcnJ5iamqJLly64fPmycD0zMxNjx46Fu7s7TExMUKNGDYwbNw7Z2dlCmdOnT6N///5wdnaGiYkJ6tevj++//76iHpGIiIhUQLTZmuVyOXr16oWrV69i165dOHXqFFxcXODr64u8vDwAQFpaGtLS0rBgwQKcO3cOUVFRiI6OxrBhw4R6EhMTYWdnh/Xr1+Pff//Fl19+ibCwMPz4448V9pxERESqpms73YpmldCL2ZovXboEd3d3nDt3Du+++y6A4hTWDg4O+OabbzB8+HCl9WzZsgWDBg1CXl4e9PWVj3gFBwfj/PnziI2Nfa02cpUQERGVl7pXCS2oobpVQhNTuUrojeXn5wOAQqZlqVQKIyMjHDlypMzvZWdnw9LSssxgpaSMjY2N6hpLREREaiXagKVevXqoUaMGwsLC8PDhQxQUFGDevHm4detWmVmW79+/j6+++gojR44ss95//vkHmzZtemkZIiIisZNJVHdoA9GuEjIwMMD27dsxbNgw2NjYQE9PD76+vggICFCaNTknJwfdunVDgwYNMHPmTKV1njt3Dj179kR4eDj8/Pxeev/8/Hyhl6eEXC7n9vxERCQK2jL3RFVE28MCFCc3TE5ORlZWFu7cuYPo6Gg8ePCgVJblR48eoUuXLrCwsMCOHTtgYGBQqq7//vsPnTp1wsiRIzFt2rRX3pvZmomIiMRD1AFLCSsrK9ja2uLy5cs4efIkevbsKVzLycmBn58fDA0NsXv3boU5LyX+/fdfdOjQAYGBgfj666/LdU9mayYiIjGTq/DQBqLO1rxlyxbY2tqiRo0aOHv2LMaPH49evXoJwzklwcrjx4+xfv165OTkICcnBwBga2sLPT09nDt3Dh07doS/vz9CQ0ORnp4OANDT04OtrW2ZbWO2ZiIiEjOZ1oQaqqHRgOXkyZMK2ZpDQ0MB/C9b8507dxAaGoqMjAw4OjpiyJAhmD59ulA+KSkJx44dAwDUrl1boe5r167B1dUVW7duxb1797B+/XqsX/+/ZVsuLi64fv26Gp+OiIhIfXRtDoto9mHRBtyHhYiIykvd+7B85TJQZXVNv/GryupSF9GuEiIiIqKy6VpvAwMWIiIiLaRrQ0JasUqIiIiIdBt7WIiIiLSQtuxQqyoa7WGJiIiAl5cXLCwsYGdnh169euHixYsKZZ4+fYrg4GBUrVoV5ubm6N27NzIyMoTrDx48QJcuXeDk5AQjIyM4OztjzJgxwvJmAIiLi4NEIil1lCxxJiIi0jYyyFV2aAONBizx8fEIDg7G0aNHERMTg8LCQvj5+SEvL08oExISgj179mDLli2Ij49HWloaPvzwQ+G6VCpFz549sXv3bly6dAlRUVH4888/MWrUqFL3u3jxIu7cuSMcdnZ2FfKcRERE9HZEtaz53r17sLOzQ3x8PNq2bYvs7GzY2tpiw4YN+OijjwAAFy5cQP369ZGQkIAWLVoorWfJkiWYP38+bt68CaC4h6VDhw54+PAhqlSp8sbt47JmIiIqL3Uva/7SdYDK6vr6+gaV1aUuopp0m52dDQCwsbEBACQmJqKwsBC+vr5CmZIszgkJCUrrSEtLw/bt29GuXbtS15o2bQpHR0d07twZf//9txqegIiIqGLIVHhoA9EELDKZDBMmTECrVq3QsGFDAEB6ejoMDQ1L9YrY29uXmn/Sv39/mJqaonr16rC0tMTPP/8sXHN0dMSKFSuwbds2bNu2Dc7Ozmjfvj2SkpLKbE9+fr6w1X/JIaLOKCIiIp0imoAlODgY586dw8aNG9/o+4sWLUJSUhJ27dqFK1euCNv8A4C7uzs+/fRTeHp6omXLllizZg1atmyJRYsWlVkfszUTEZGYcdKtBowZMwZ79+7FX3/9hXfeeUc47+DggIKCAmRlZSmUz8jIgIODg8I5BwcH1KtXDz169MBPP/2E5cuX486dO2Xe87333lNIvPgiZmsmIiIx07VszRoNWORyOcaMGYMdO3YgNjYWbm5uCtc9PT1hYGCAgwcPCucuXryI1NRU+Pj4lFmvTFY8Ipefn19mmeTkZDg6OpZ53cjICJaWlgoHszUTEZFY6NocFo1uHBccHIwNGzZg165dsLCwEOalWFlZwcTEBFZWVhg2bBhCQ0NhY2MDS0tLjB07Fj4+PsIKoX379iEjIwNeXl4wNzfHv//+i0mTJqFVq1ZwdXUFACxevBhubm5499138fTpU/z888+IjY3FgQMHNPXoRERE9Bo0GrAsX74cANC+fXuF85GRkQgKCgJQPDdFKpWid+/eyM/Ph7+/P5YtWyaUNTExwapVqxASEoL8/Hw4Ozvjww8/xJQpU4QyBQUF+Pzzz3H79m2YmpqicePG+PPPP9GhQwe1PyMREZE6aMvcE1UR1T4sYsd9WIiIqLzUvQ9LiOvHKqtr0fU3W/BSkUQx6ZaIiIjoZZj8kIiISAtpy2RZVWHAQkREpIXkOjaHRfTZmleuXIn27dsLy4pf3JPlefn5+WjatCkkEgmSk5MVru3fvx8tWrSAhYUFbG1t0bt3b1y/fl31D0VEREQqJ/pszY8fP0aXLl0wderUV9b3xRdfwMnJqdT5a9euoWfPnujYsSOSk5Oxf/9+3L9/XyHrMxERkTbhPiwVKDo6WuFzVFQU7OzskJiYiLZt2wIAJkyYAKA44/LL/PHHHzhw4AC2bduGP/74Q+FaYmIiioqKMGfOHEilxTHaxIkT0bNnTxQWFsLAwEA1D0RERFRBdG1Zs6hWCb2Yrbm8MjIyMGLECKxbtw6mpqalrnt6ekIqlSIyMhJFRUXIzs7GunXr4Ovry2CFiIhIC4gmYFGWrbk85HI5goKCMGrUKDRv3lxpGTc3Nxw4cABTp06FkZERqlSpglu3bmHz5s1l1stszUREJGbMJaQhb5qt+YcffsCjR48QFhZWZpn09HSMGDECgYGBOHHiBOLj42FoaIiPPvqozCCE2ZqJiEjMmK1ZA8rK1lwesbGxSEhIgJGREfT19VG7dm0AQPPmzREYGAgAWLp0KaysrPDtt9/Cw8MDbdu2xfr163Hw4EEcO3ZMab3M1kxERGLGSbcVSC6XY+zYsdixYwfi4uJKZWsujyVLlmDOnDnC57S0NPj7+2PTpk3w9vYGULzSqGSybQk9PT0A/8vs/CIjIyMYGRkpnGO2ZiIiIs0QdbZmoHg4Jz09HSkpKQCAs2fPwsLCAjVq1ICNjQ1q1KihUKe5uTkAoFatWkJvTbdu3bBo0SLMnj0b/fv3x6NHjzB16lS4uLjAw8Ojoh6XiIhIZbhxXAVavnw5srOz0b59ezg6OgrHpk2bhDIrVqyAh4cHRowYAQBo27YtPDw8sHv37nLfp2PHjtiwYQN27twJDw8PdOnSBUZGRoiOjhYCIyIiIm2ia0NCzNb8GpitmYiIykvd2Zo/cf1IZXWtub5VZXWpC3MJERERaSFdGxJiwEJERKSFtGUoR1VEsayZiIiI6GVEna05MzMTY8eOhbu7O0xMTFCjRg2MGzdO2ML/eVFRUWjcuDGMjY1hZ2eH4OBg4drTp08RFBSERo0aQV9fH7169aqIxyMiIlIbmVyuskMbaHRIqCRbs5eXF549e4apU6fCz88P//33H8zMzJCWloa0tDQsWLAADRo0wI0bNzBq1CikpaVh69b/TRBauHAhvvvuO8yfPx/e3t7Iy8vD9evXhetFRUUwMTHBuHHjsG3bNg08KRERkWppR5ihOqJaJXTv3j3Y2dkhPj5eyNb8oi1btmDQoEHIy8uDvr4+Hj58iOrVq2PPnj3o1KnTK+8RFBSErKws7Ny587Xbx1VCRERUXupeJTTI5UOV1bX+xnaV1aUuoprDUp5szdnZ2bC0tIS+fnHnUExMDGQyGW7fvo369evjnXfeQd++fXHz5s0KaTMREZEmMJeQhpQnW/P9+/fx1VdfYeTIkcK5q1evQiaT4ZtvvsHixYuxdetWZGZmonPnzigoKKio5hMREVUouQr/pw1Es6y5JFvzkSNHlF7PyclBt27d0KBBA8ycOVM4L5PJUFhYiCVLlsDPzw8A8Ntvv8HBwQF//fUX/P3936g9+fn5yM/PVzgnl8uZT4iIiESBy5o14FXZmh89eoQuXbrAwsICO3bsgIGBgXDN0dERANCgQQPhnK2tLapVq4bU1NQ3blNERASsrKwUDrns0RvXR0RERG9OowGLXC7HmDFjsGPHDsTGxirN1pyTkwM/Pz8YGhpi9+7dMDY2VrjeqlUrACi1HPr+/ftwcXF547aFhYUhOztb4ZBILd64PiIiIlXStTksos7WXBKsPH78GOvXr0dOTg5ycnIAFPei6OnpoW7duujZsyfGjx+PlStXwtLSEmFhYahXrx46dOgg3Ou///5DQUEBMjMz8ejRIyQnJwMAmjZtqrRtRkZGMDIyUjjH4SAiIhILbZl7oioaXdZcVgAQGRmJoKAgxMXFKQQdz7t27RpcXV0BFPfChISEYPv27ZBKpWjXrh2+//57ODs7C+VdXV1x48aNUvW8zuNzWTMREZWXupc1f+TSQ2V1bb2xW2V1qYuo9mEROwYsRERUXuoOWD5UYcCyXQsCFlFMuiUiIqLXI5fLVXa8rqVLl8LV1RXGxsbw9vbG8ePHyyy7atUqtGnTBtbW1rC2toavr+9Ly5eFAQsRERGV26ZNmxAaGorw8HAkJSWhSZMm8Pf3x927d5WWj4uLQ//+/fHXX38hISEBzs7O8PPzw+3br9cDxSGh18AhISIiKi91Dwn1rPG+yuralbq33GW9vb3h5eWFH3/8EUDxfmjOzs4YO3YspkyZ8srvFxUVwdraGj/++COGDBlS7vuKOlszAHz66aeoVasWTExMYGtri549e+LChQvC9aioKEgkEqWHsmjv77//hr6+fpmrg4iIiLSBTIVHfn6+sBK35Hhx81QAKCgoQGJiInx9fYVzUqkUvr6+SEhIKFe7Hz9+jMLCwpem4VFGowFLSbbmo0ePIiYmBoWFhfDz80NeXp5QxtPTE5GRkTh//jz2798PuVwOPz8/FBUVAQD69euHO3fuKBz+/v5o164d7OzsFO6XlZWFIUOGlCtJIhERka5QtllqREREqXL3799HUVER7O3tFc7b29sLW5O8yuTJk+Hk5KQQ9JSHRvdhiY6OVvgcFRUFOzs7JCYmCtman88b5Orqijlz5qBJkya4fv260PNiYmIilLl37x5iY2OxevXqUvcbNWoUBgwYAD09vTfK1kxERCQWqtyHJSwsDKGhoQrnXtyLTBXmzp2LjRs3Ii4urtRGsK8iqkm3r8rWnJeXh8jISLi5uSnssfK8X375Baampvjoo48UzkdGRuLq1asIDw9XbaOJiIg0QJU73RoZGcHS0lLhUBawVKtWDXp6esjIyFA4n5GRAQcHh5e2d8GCBZg7dy4OHDiAxo0bv/bziiZgeVm25mXLlsHc3Bzm5ub4448/EBMTA0NDQ6X1rF69GgMGDFDodbl8+TKmTJmC9evXQ19fNPkeiYiI3pgmljUbGhrC09MTBw8eFM7JZDIcPHgQPj4+ZX7v22+/xVdffYXo6Gg0b978jZ5XNAFLSbbmjRs3lro2cOBAnDp1CvHx8ahbty769u2Lp0+fliqXkJCA8+fPY9iwYcK5oqIiDBgwALNmzULdunXL3R5lE5C4oIqIiHRdaGgoVq1ahbVr1+L8+fMYPXo08vLyMHToUADAkCFDEBYWJpSfN28epk+fjjVr1sDV1RXp6elIT09Hbm7ua91XFMuax4wZg127duHQoUNKEyA+r6CgANbW1vj555/Rv39/hWvDhg1DUlISTp06JZzLysqCtbU19PT0hHMymQxyuRx6eno4cOAAOnbsWOo+M2fOxKxZsxTOSaTmkOpZvskjEhGRjlH3smZ/5wCV1bX/5h+vVf7HH3/E/PnzkZ6ejqZNm2LJkiXw9vYGALRv3x6urq6IiooCUHZqnPDwcMycObPc99RowCKXyzF27Fjs2LEDcXFxqFOnziu/k5+fD2trayxbtgxBQUHC+dzcXDg6OiIiIgJjxowRzstkMvz3338KdSxbtgyxsbHYunUr3NzcYGZmpvQ+Ly7psq5ajwkQiYioXNQdsPg5d1FZXQduRr+6kIaJOlvz1atXsWnTJvj5+cHW1ha3bt3C3LlzYWJigq5duyrUtWnTJjx79gyDBg1SOC+VSkvNibGzs4OxsXGp889jtmYiIiLx0OgcluXLlyM7Oxvt27eHo6OjcGzatAkAYGxsjMOHD6Nr166oXbs2+vXrBwsLC/zzzz+l9lhZvXo1PvzwQ1SpUkUDT0JERFSxVLlKSBuIYg6LtuDW/EREVF7qHhLq9I6fyuo6eOuAyupSF9GsEiIiIiIqCzclISIi0kLaMpSjKgxYiIiItJAqt+bXBhwSIiIiItHTaMASEREBLy8vWFhYwM7ODr169cLFixeVlpXL5QgICIBEIimVuPDEiRPo1KkTqlSpAmtra/j7++P06dPC9ZkzZ0IikZQ6lO2/QkREpA1kcrnKDm2g0YAlPj4ewcHBOHr0KGJiYlBYWAg/Pz/k5eWVKrt48WKl+6Dk5uaiS5cuqFGjBo4dO4YjR47AwsIC/v7+KCwsBABMnDgRd+7cUTgaNGiAPn36qP0ZiYiI1EGuwkMbiGpZ871792BnZ4f4+Hi0bdtWOJ+cnIz3338fJ0+ehKOjI3bs2IFevXoBAE6ePAkvLy+kpqYKGZzPnj2Lxo0b4/Lly6hdu3ap+5w+fRpNmzbFoUOH0KZNm3K3j8uaiYiovNS9rLlV9dJpZd7U37djVVaXuohqDkt2djYAwMbGRjj3+PFjDBgwAEuXLlWautrd3R1Vq1bF6tWrUVBQgCdPnmD16tWoX78+XF1dld7n559/Rt26dV8rWCEiIiLNEU3AIpPJMGHCBLRq1Uphy/yQkBC0bNkSPXv2VPo9CwsLxMXFYf369TAxMYG5uTmio6Pxxx9/QF+/9CKop0+f4tdff1XI6KwMszUTEZGY6dpOt6IJWIKDg3Hu3Dls3LhROLd7927ExsZi8eLFZX7vyZMnGDZsGFq1aoWjR4/i77//RsOGDdGtWzc8efKkVPkdO3bg0aNHCAwMfGl7IiIiYGVlpXDIZY/e+PmIiIhUSS6Xq+zQBqKYwzJmzBjs2rULhw4dgpubm3B+woQJWLJkCaTS/8VVRUVFkEqlaNOmDeLi4rB69WpMnToVd+7cEcoVFBTA2toaq1evxscff6xwr06dOsHS0hI7dux4aZuYrZmIiN6GuuewtHBqr7K6jqbFqawuddHoxnFyuRxjx47Fjh07EBcXpxCsAMCUKVMwfPhwhXONGjXCokWL0L17dwDFc1ykUqlCIFHyWSaTKXz32rVr+Ouvv7B79+5Xto3ZmomISMy0ZShHVTQasAQHB2PDhg3YtWsXLCwskJ6eDgCwsrKCiYkJHBwclE60rVGjhhDcdO7cGZMmTUJwcDDGjh0LmUyGuXPnQl9fHx06dFD43po1a+Do6IiAgAD1PxwREZEacafbCrR8+XJkZ2ejffv2cHR0FI5NmzaVu4569ephz549OHPmDHx8fNCmTRukpaUhOjoajo6OQjmZTIaoqCgEBQVBT09PHY9DREREaiKKOSzagvuwEBFReal7DktzR9VtzXHyzmGV1aUuTH5IRESkhXRtDotoljUTERERlYU9LERERFpI12Z0aEW25oSEBHTs2BFmZmawtLRE27ZtFTaFy8zMxMCBA2FpaYkqVapg2LBhyM3NVajjzJkzaNOmDYyNjeHs7Ixvv/1W7c9HRESkLtzptgKVJ1tzQkICunTpAj8/Pxw/fhwnTpzAmDFjFDaTGzhwIP7991/ExMRg7969OHToEEaOHClcz8nJgZ+fH1xcXJCYmIj58+dj5syZWLlyZYU+LxERkarIVfg/bSCqVULKsjW3aNECnTt3xldffaX0O+fPn0eDBg1w4sQJNG/eHAAQHR2Nrl274tatW3BycsLy5cvx5ZdfIj09HYaGhgCKN6XbuXMnLly4UO72cZUQERGVl7pXCTV28FFZXWfSE1RWl7qIatLti9ma7969i2PHjsHOzg4tW7aEvb092rVrhyNHjgjfSUhIQJUqVYRgBQB8fX0hlUpx7NgxoUzbtm2FYAUA/P39cfHiRTx8+LAiHo2IiEilZHK5yg5tIJqARVm25qtXrwIAZs6ciREjRiA6OhrNmjVDp06dcPnyZQBAeno67OzsFOrS19eHjY2NsHNueno67O3tFcqUfC4pQ0REpE10bUhINKuESrI1P997UpIL6NNPP8XQoUMBAB4eHjh48CDWrFmDiIgItbVHWfJDuVzOfEJEREQaIIoeljFjxmDv3r3466+/8M477wjnS7bWb9CggUL5+vXrIzU1FQDg4OCAu3fvKlx/9uwZMjMzhTxEDg4OyMjIUChT8llZriKgeAWTlZWVwiGXPXqLpyQiIlIdDglVILlcjjFjxmDHjh2IjY0tla3Z1dUVTk5OpZY6X7p0CS4uLgAAHx8fZGVlITExUbgeGxsLmUwGb29vocyhQ4dQWFgolImJiYG7uzusra2Vti0sLAzZ2dkKh0RqoZLnJiIielu6NiSk0YAlODgY69evx4YNG4Rszenp6cIeKxKJBJMmTcKSJUuwdetWpKSkYPr06bhw4QKGDRsGoLi3pUuXLhgxYgSOHz+Ov//+G2PGjMHHH38MJycnAMCAAQNgaGiIYcOG4d9//8WmTZvw/fffIzQ0tMy2GRkZwdLSUuHgcBAREZFmaHRZc1kBQGRkJIKCgoTPc+fOxdKlS5GZmYkmTZrg22+/RevWrYXrmZmZGDNmDPbs2QOpVIrevXtjyZIlMDc3F8qcOXMGwcHBOHHiBKpVq4axY8di8uTJr9VeLmsmIqLyUvey5rq2zV9dqJwu3TupsrrURVT7sIgdAxYiIiovdQcsdWw9VVbX5XuJry6kYaKYdEtERET0MqJZ1kxERETlpy2re1SFAQsREZEW0pbVPaoi6mzN169fh0QiUXps2bJFKDdu3Dh4enrCyMgITZs2LXWfixcvokOHDrC3t4exsTFq1qyJadOmKSxzJiIi0iZyuUxlhzbQaA9LSbZmLy8vPHv2DFOnToWfnx/+++8/mJmZwdnZGXfu3FH4zsqVKzF//nwEBAQonP/kk09w7NgxnDlzptR9DAwMMGTIEDRr1gxVqlTB6dOnMWLECMhkMnzzzTdqfUYiIiJ6exoNWKKjoxU+R0VFwc7ODomJiWjbti309PRK7US7Y8cO9O3bV2HJ8pIlSwAUZ3tWFrDUrFkTNWvWFD67uLggLi4Ohw8fVuXjEBERVRgZh4Q058VszS9KTExEcnKysGncm0pJSUF0dDTatWv3VvUQERFpilwuV9mhDUQTsCjL1vyi1atXo379+mjZsuUb3aNly5YwNjZGnTp10KZNG8yePfttmkxEREQVRDQBS0m25o0bNyq9/uTJE2zYsOGtelc2bdqEpKQkbNiwAb///jsWLFhQZtn8/Hzk5OQoHNoShRIRUeUng1xlhzYQxbLmkmzNhw4dUsjW/LytW7fi8ePHGDJkyBvfx9nZGUBx9ueioiKMHDkSn3/+OfT09EqVjYiIwKxZsxTOSaTmkOhZvvH9iYiIVEXX/ogWdbbm561evRo9evSAra2tSu4tk8lQWFgImUz5ci5mayYiIhIPjfawBAcHY8OGDdi1a5eQrRkArKysYGJiIpRLSUnBoUOHsG/fPqX1pKSkIDc3V8j0nJycDKC4J8XQ0BC//vorDAwM0KhRIxgZGeHkyZMICwtDv379YGBgoLROIyMjGBkZKZxjtmYiIhILXdvpViuyNU+dOhXr16/H9evXIZWW7hRq37494uPjS52/du0aXF1dsWnTJnz77be4dOkS5HI5XFxcMGjQIISEhMDY2Ljc7WXyQyIiKi91Jz90qFJfZXWlZ51XWV3qwmzNr4EBCxERlZe6AxZ7q3oqqysj+4LK6lIX0awSIiIiIiqLKFYJERER0evRluXIqsKAhYiISAvp2owODgkRERGR6Gk0YImIiICXlxcsLCxgZ2eHXr164eLFiwpl0tPTMXjwYDg4OMDMzAzNmjXDtm3bhOvXr1/HsGHD4ObmBhMTE9SqVQvh4eEoKChQqEcul2PBggWoW7cujIyMUL16dXz99dcV8pxERESqJpPLVXZoA40OCcXHxyM4OBheXl549uwZpk6dCj8/P/z3338wMzMDAAwZMgRZWVnYvXs3qlWrhg0bNqBv3744efIkPDw8cOHCBchkMvz000+oXbs2zp07hxEjRiAvL09h6/3x48fjwIEDWLBgARo1aoTMzExkZmZq6tGJiIjeiq4NCYlqWfO9e/dgZ2eH+Ph4tG3bFgBgbm6O5cuXY/DgwUK5qlWrYt68eRg+fLjSeubPn4/ly5fj6tWrAIDz58+jcePGOHfuHNzd3d+4fVzWTERE5aXuZc3W5rVVVtfD3BSV1aUuoprDkp2dDQCwsbERzrVs2RKbNm1CZmYmZDIZNm7ciKdPn6J9+/Yvref5Ovbs2YOaNWti7969cHNzg6urK4YPH84eFiIi0lpMfqghMpkMEyZMQKtWrdCwYUPh/ObNm9GvXz9UrVoV+vr6MDU1xY4dO1C7tvLIMiUlBT/88IPCcNDVq1dx48YNbNmyBb/88guKiooQEhKCjz76CLGxsUrryc/PR35+vsI5uVzO7fmJiEgURDRAUiFEE7AEBwfj3LlzOHLkiML56dOnIysrC3/++SeqVauGnTt3om/fvjh8+DAaNWqkUPb27dvo0qUL+vTpgxEjRgjnZTIZ8vPz8csvv6Bu3boAipMpenp64uLFi0qHiZitmYiISDxEMYdlzJgx2LVrFw4dOqSQsfnKlSvCRNp3331XOO/r64vatWtjxYoVwrm0tDS0b98eLVq0QFRUlELOofDwcHzzzTcoLCwUzj158gSmpqY4cOAAOnfuXKpNynpYrKvWYw8LERGVi7rnsJibur26UDnlPr6msrrURaM9LHK5HGPHjsWOHTsQFxenEKwAwOPHjwGgVMJDPT09yGQy4fPt27fRoUMHeHp6IjIyslT5Vq1a4dmzZ7hy5Qpq1aoFALh06RIAwMXFRWnbmK2ZiIjETK4lc09URaM9LJ999hk2bNiAXbt2KQzLWFlZwcTEBIWFhWjQoAEcHR2xYMECVK1aFTt37sSkSZOwd+9edO3aFbdv30b79u3h4uKCtWvXQk9PT6jHwcEBQPGQkJeXF8zNzbF48WLIZDIEBwfD0tISBw4cKHd7uUqIiIjKS909LCYmyv/gfhNPntxQWV3qotGApawei8jISAQFBQEALl++jClTpuDIkSPIzc1F7dq1MXHiRGGZc1RUFIYOHaq0nucfLS0tDWPHjsWBAwdgZmaGgIAAfPfddwqriV6FAQsREZUXAxbVEsUcFm3BgIWIiMpL3QGLsXENldX19GmqyupSF9GsEiIiIqLy07U5LKLaOI6IiIhIGQYsREREWkgul6vseF1Lly6Fq6srjI2N4e3tjePHj7+0/JYtW1CvXj0YGxujUaNG2Ldv32vfkwELERGRFtJUwLJp0yaEhoYiPDwcSUlJaNKkCfz9/XH37l2l5f/55x/0798fw4YNw6lTp9CrVy/06tUL586de637ctLta+CkWyIiKi91T7o1UOHvpMLXaKu3tze8vLzw448/AijeOsTZ2Rljx47FlClTSpXv168f8vLysHfvXuFcixYt0LRpU4UNYF+FPSxERERaSK7CIz8/Hzk5OQrHi7u9A0BBQQESExPh6+srnJNKpfD19UVCQoLSdiYkJCiUBwB/f/8yy5eFq4Reg7qj5VfJz89HREQEwsLCSu3Cq0v4HorxPRTjeyjG9/A/uvIuVPk7aebMmaXy54WHh2PmzJkK5+7fv4+ioiLY29srnLe3t8eFCxeU1p2enq60fHp6+mu1kT0sWiQ/Px+zZs1SGvXqEr6HYnwPxfgeivE9/A/fxesLCwtDdna2whEWFqbpZilgDwsREZGOU5Y/T5lq1apBT08PGRkZCuczMjKEdDgvcnBweK3yZWEPCxEREZWLoaEhPD09cfDgQeGcTCbDwYMH4ePjo/Q7Pj4+CuUBICYmpszyZWEPCxEREZVbaGgoAgMD0bx5c7z33ntYvHgx8vLyhLx+Q4YMQfXq1REREQEAGD9+PNq1a4fvvvsO3bp1w8aNG3Hy5EmsXLnyte7LgEWLGBkZITw8vFJPIisPvodifA/F+B6K8T38D9+FevXr1w/37t3DjBkzkJ6ejqZNmyI6OlqYWJuamgqp9H8DOC1btsSGDRswbdo0TJ06FXXq1MHOnTvRsGHD17ov92EhIiIi0eMcFiIiIhI9BixEREQkegxYiIiISPQYsBAREZHoMWAhIq0QGxuLZ8+eaboZRKQhDFiISCt07twZmZmZwucWLVrg9m3N5vcioorDgIWItMKLOzD8+++/zBVDpEMYsGipK1euoGPHjppuRoW4c+cO1q9fj3379qGgoEDhWl5eHmbPnq2hllWcGTNm4PHjx8Lnhw8farA1JCZ5eXmYPn06WrZsidq1a6NmzZoKh67IyMjA4MGD4eTkBH19fejp6SkcpP24cZyWOn36NJo1a4aioiJNN0WtTpw4AT8/P8hkMhQWFqJ69erYuXMn3n33XQDFP6ScnJwq/XvQ09PDnTt3YGdnBwCwtLREcnKyTv1C0tPTQ3p6OmxtbQEUv4PTp0/Dzc1Nwy3TrP79+yM+Ph6DBw+Go6MjJBKJwvXx48drqGUVKyAgAKmpqRgzZozS99CzZ08NtYxUhVvzi9SSJUteel1Xxu6nTp2KDz74AD///DPy8vIwefJktGvXDjExMfDw8NB08yrMi39X6OLfGXK5HJ06dYK+fvGPrcePH6N79+4wNDRUKJeUlKSJ5mnMH3/8gd9//x2tWrXSdFM06siRIzh8+DCaNm2q6aaQmjBgEakJEybA0dGx1A/jEi8OjVRWiYmJWLp0KaRSKSwsLLBs2TLUqFEDnTp1wv79+1GjRg1NN5EqSHh4uMJn/sVczNraGjY2NppuhsY5OzvrZCCvSxiwiJSLiwvmzZuHvn37Kr2enJwMT0/PCm6VZjx9+lTh85QpU6Cvrw8/Pz+sWbNGQ62qWBKJBI8ePYKxsTHkcjkkEglyc3ORk5OjUM7S0lJDLVS/FwMWKvbVV19hxowZWLt2LUxNTTXdHI1ZvHgxpkyZgp9++gmurq6abg6pAQMWkfL09ERiYmKZAYtEItGJvyYaNmyIf/75B40bN1Y4P3HiRMhkMvTv319DLatYcrkcdevWVfj8/JBYSRBT2efyKBMfH4+8vDz4+PjA2tpa082pEB4eHgpzNFJSUmBvbw9XV1cYGBgolK3MQ2TW1tYK7yEvLw+1atWCqalpqffw/JJ40k4MWERq9uzZCqtCXtSgQQNcu3atAlukGUOGDEF8fDxGjRpV6toXX3wBuVyOFStWaKBlFeuvv/7SdBM0bt68ecjNzcVXX30FoDhICwgIwIEDBwAAdnZ2OHjwoDAhuzLr1auXppsgCosXL9Z0E6gCcZUQEWmFZs2aYfLkyejXrx8AYMuWLQgMDERMTAzq16+PIUOGwNTUFJs3b9ZwS4lIHbgPi0hxG/JifA/Fnj17VmqTtIyMDMyaNQtffPEFjhw5oqGWVZxr164pDA3u27cPH330EVq1agUbGxtMmzYNCQkJGmyhZtSsWRMPHjwodT4rK0vnlr3fvXu31PkHDx5wH5ZKggGLSHEb8mJ8D8VGjBiBcePGCZ8fPXoELy8vLF26FPv370eHDh2wb98+DbZQ/Z49ewYjIyPhc0JCAlq2bCl8dnJywv379zXRNI26fv260rlL+fn5uHXrlgZapBllDRbk5+eXudqStAvnsIgUtyEvxvdQ7O+//8aPP/4ofP7ll19QVFSEy5cvw8rKCpMnT8b8+fPRtWtXDbZSvWrVqoVDhw6hZs2aSE1NxaVLl9C2bVvh+q1bt1C1alUNtrBi7d69W/j3/v37YWVlJXwuKirCwYMHdWJTvZI9qyQSCX7++WeYm5sL14qKinDo0CHUq1dPU80jFWLAQqQFbt++jTp16gifDx48iN69ewu/pAIDAxEZGamp5lWI4OBgjBkzBocPH8bRo0fh4+ODBg0aCNdjY2N1ajPBkom3EokEgYGBCtcMDAzg6uqK7777TgMtq1iLFi0CAGEC/vPDP4aGhnB1ddWJifm6gAGLSEkkEoXlei9+1hV8D8WMjY3x5MkT4fPRo0cxf/58heu5ubmaaFqFGTFiBPT09LBnzx60bdu21L4saWlpGDp0qIZaV/FkMhkAwM3NDSdOnEC1atU03CLNKFkt2aFDB2zfvl1nlrbrIq4SEimpVIqGDRsK25CfOXMG9erV07ltyPkeinXq1AnvvfceIiIicPjwYbRv3x63bt2Co6MjACAmJgajR49GSkqKhltKRKQe7GERKW5DXozvodiMGTMQEBCAzZs3486dOwgKChKCFQDYsWOHzuSSeTERZIkHDx7Azs5OJzfPy8vLQ3x8PFJTU0ul7Xh+snZld+vWLezevVvpe1i4cKGGWkWqwh4WIi1x/vx5HDhwAA4ODujTpw+k0v8t8lu5ciXee+89nUj8JpVKkZ6eXipgSUtLQ61atRSGznTBqVOn0LVrVzx+/Bh5eXmwsbHB/fv3YWpqCjs7O1y9elXTTawQBw8eRI8ePVCzZk1cuHABDRs2xPXr1yGXy9GsWTPExsZquon0ltjDomV0cRtyZXTpPTRr1gwHDx5E/fr1sWXLFowYMUIhWAGAkSNHaqh1FYerQZQLCQlB9+7dsWLFClhZWeHo0aMwMDDAoEGDMH78eE03r8KEhYVh4sSJmDVrFiwsLLBt2zbY2dlh4MCB6NKli6abR6ogJ1GaO3eufNq0acJnmUwm9/f3l0skErlEIpHb29vLz507p8EWVgy+B7nc2NhYfvPmTblcLpdLpVL53bt3NdwizXB1dZW7urrKJRKJ3NnZWfjs6uoqr1u3rtzPz09+9OhRTTezwllZWckvXLgg/Pu///6Ty+Vy+dGjR+Xu7u6abFqFMjc3l6ekpMjlcrm8SpUqws+F5ORkuYuLiwZbRqrCHhaR2rRpEyZPnix83rp1Kw4dOoTDhw8L25DPmjWr0m9DzvcANG3aFEOHDkXr1q0hl8sxf/58hd6F582YMaOCW1dxuBpEOQMDA6HHzc7ODqmpqahfvz6srKxw8+ZNDbeu4piZmQnzVhwdHXHlyhUhr5QubihYGTFgEamXbUMOANOmTUOfPn001bwKw/cAREVFITw8HHv37oVEIsEff/whrJp6nkQiqdQBSwkmglTk4eGBEydOoE6dOmjXrh1mzJiB+/fvY926dWjYsKGmm1dhWrRogSNHjqB+/fro2rUrPv/8c5w9exbbt29HixYtNN08UgEGLCKlbBvyCRMmCJ91ZRtyvgfA3d0dGzduBFA84fTgwYOlJpzqErlcjq1bt+Kvv/7C3bt3hf1ISmzfvl1DLdOMb775Bo8ePQIAfP311xgyZAhGjx6NOnXqYM2aNRpuXcVZuHChsBfRrFmzkJubi02bNqFOnTpcIVRJMGARKW5DXozvQdGLv5x10YQJE/DTTz+hQ4cOsLe318mNBJ/XvHlz4d92dnaIjo7WYGs05/lEj2ZmZtzdthJiwCJS3Ia8GN9DaZcvXy6zd0EXhoTWrVuH7du3V+q8SW+joKAABQUFZc5z0hVXr17FkydPUL9+/VKr6kg7MWARKW5DXozvQdGqVaswevRoVKtWDQ4ODqXSFuhCwGJlZaXw17Qui4yMRFJSElq0aIGBAwciLCwMCxcuxLNnz9CxY0ds3Lix0vdAFhYWYs6cOcJ7mDJlCgYNGiRMxHd3d8e+ffvg6uqq2YbSW+PGcURaxMXFBZ999pnCyilds3btWkRHR2PNmjUwMTHRdHM05uuvv8bXX3+NVq1aISkpCX379sXOnTsxYcIESKVSLFmyBO+//z6WL1+u6aaq1eeff45169ahZ8+eiI2NRcOGDXHx4kXMmjULUqkUX331FRo1aoRff/1V002lt8QeFi0gk8mQkpKidAjg+fkclR3fA/Dw4cNKvyrqVfr27YvffvsNdnZ2cHV1hYGBgcL1yp5XqkRUVBRWr16N/v374+TJk/D29sbmzZvRu3dvAEDDhg0xatQoDbdS/bZu3YqoqCh07doVly5dQr169fD7778jICAAAITN40j7MWARuaNHj2LAgAG4ceMGXuwMk0gkOpM3he+hWJ8+fXDgwAGd+EVUlsDAQCQmJmLQoEE6Pek2NTUVrVu3BlA88VZfX19hGXPjxo1x584dTTWvwqSlpaFJkyYAgLp168LIyAi1a9cWrtetWxfp6emaah6pEAMWkRs1ahSaN2+O33//HY6Ojjr7w5nvoVjt2rUxffp0HD16FI0aNSrVu6ALie5+//137N+/X/hlrasKCwsVlvwbGhoq/Pegr6+vE4F8UVFRqefW09MTPkul0lJ/5JB2YsAicpcvX8bWrVsV/mLQRXwPxVauXAlzc3PEx8cjPj5e4ZpEItGJgMXZ2RmWlpaaboYo/Pfff0LvgVwux4ULF4S9SCr7/kTP279/P6ysrAAUDx0fPHgQ586dAwBkZWVpsGWkSpx0K3IdO3bEF198ofPJu/geqMTvv/+OH374AStWrNDplR9SqRQSiURp70HJeV0YLi3PkmVdeA+6gD0sIjd27Fh8/vnnSE9PVzoE8Py29ZUZ3wOVGDRoEB4/foxatWrB1NS01H8LmZmZGmpZxSrJraTruJmi7mAPi8gp++tBl/56KqHr7+HDDz9Uet7Kygp169bF8OHDYWtrW8Gt0oy1a9e+9HpgYGAFtYQ0rVmzZjh48CCsra0xe/ZsTJw4EaampppuFqkJAxaRu3Hjxkuvu7i4VFBLNEvX30NZm+NlZWXh9OnTyMrKwqFDh3Qq2Z0uO3PmTLnLVubeRxMTE1y+fBnvvPMO9PT0cOfOHZ3Os1XZMWAh0nIymQwjRozA3bt3sWfPHk03R+2SkpJgYGCARo0aAQB27dqFyMhINGjQADNnzoShoaGGW6h+L5u/8rzK3vvo4+MDc3NztG7dGrNmzcLEiRPLTEmgC7tAV3YMWERo9+7dCAgIgIGBAXbv3v3Ssj169KigVlU8vofyO336NAICApCWlqbppqidl5cXpkyZgt69e+Pq1ato0KABPvzwQ5w4cQLdunXD4sWLNd1EtXtVj+PzKnPv48WLFxEeHo4rV64gKSkJDRo0gL5+6amZEolEZzYUrMwYsIiQVCpFeno67OzsXjoDvrL/9cT3UH4pKSlo3ry5TizhtLKyQlJSEmrVqoV58+YhNjYW+/fvx99//42PP/4YN2/e1HQTSQOe/3lBlRNXCYnQ87PedXkGPN9D+cXExKBu3bqabkaFkMvlwn8Pf/75J95//30Axfuz6MreI6/qcXyervQ+8mdE5ceARaRq1KiBU6dOCZlWf/zxRwwZMkTnNszieyhW1i+o7OxsJCYm4ueff8bPP/9cwa3SjObNm2POnDnw9fVFfHy8kNzv2rVrsLe313DrKkavXr3KVa6y9z4ycNMtHBISqRe7Ny0tLZGcnIyaNWtquGUVi++hWFlDYhYWFnB3d0doaCg+/vjjCm6VZpw5cwYDBw5EamoqQkNDER4eDqB4r54HDx5gw4YNGm4hVZTybBoHVP7ATVewh0VLMK4spqvvgd3d/9O4cWOcPXu21Pn58+cr5JChyo//f6FbGLAQaZFffvkF/fr1U0h6BwAFBQXYuHEjhgwZoqGWac7Vq1fx5MkT1K9fv9x/cVcms2fPful1LuelyoJDQiIllUoxZ84cYU+ByZMnY9KkSahWrZpCucqe7I7vQVFZm2M9ePAAdnZ2lbrbu7CwEHPmzEFSUhJatGiBKVOmYNCgQdi8eTMAwN3dHfv27dO5/EIeHh4KnwsLC3Ht2jXo6+ujVq1aOrWcNy8vD/Hx8UhNTUVBQYHCNV35GVGZMWARKVdXV0gkkpeWkUgkuHr1agW1SDP4HhRJpVJkZGSU2ob/9OnT6NChQ6XOo/P5559j3bp16NmzJ2JjY9GwYUNcvHgRs2bNglQqxVdffYVGjRrh119/1XRTNS4nJwdBQUH44IMPMHjwYE03p0KcOnUKXbt2xePHj5GXlwcbGxvcv38fpqamsLOz05mfEZUZAxYiLeDh4QGJRILTp0/j3XffVdgcq6ioCNeuXUOXLl2E3obKyMXFBcuXL0fXrl1x6dIl1KtXD7///jsCAgIAAPHx8Rg4cCBu3bql4ZaKw9mzZ9G9e3dcv35d002pEO3bt0fdunWxYsUKWFlZ4fTp0zAwMMCgQYMwfvz4MvNxkfbgHBaR45yFYrr+HkqWsSYnJ8Pf319h+3FDQ0O4urqid+/eGmpdxUhLS0OTJk0AAHXr1oWRkRFq164tXK9bty7S09M11TzRyc7ORnZ2tqabUWGSk5Px008/QSqVQk9PD/n5+ahZsya+/fZbBAYGMmCpBNjDInK6PGfheXwPxdauXYt+/frB2NhY002pcC8ucbewsMDp06eFJe4ZGRlwcnLSmf8WSixZskThs1wux507d7Bu3Tq0a9dOZ5Z529ra4p9//kGdOnVQt25d/PDDD/D398eFCxfg6emJvLw8TTeR3hJ7WEROLpcrncNx69YtWFlZaaBFmsH3UCwwMFD4d25ubqllnZV9Q739+/cL//eWyWQ4ePAgzp07BwA6kZZAmUWLFil8lkqlsLW1RWBgIMLCwjTUqorn4eGBEydOoE6dOmjXrh1mzJiB+/fvY926dcxiXkmwh0WkOGehGN+DomvXrmHMmDGIi4vD06dPhfMlAV1l7l0oz5Llyv4OqGwnT57Eo0eP0KFDB9y9exdDhgwRelzWrFkjDCeS9mIPi0hxzkIxvgdFgwYNglwux5o1a2Bvb//KFVSVCTcJU8S0FYqaN28u/NvOzg7R0dEabA2pA3tYRE6X5yw8j++hmLm5ORITE+Hu7q7pppCGMW0F6Rr2sIicrs9ZKMH3UMzLyws3b97UuYCFSe5eTVf/9iwZNn4VXdpAr7JiwCJyujxn4Xl8D8V+/vlnjBo1Crdv30bDhg1hYGCgcL1x48Yaapl6MTsxleX5/zbkcjkiIiIwatQo2NjYaK5RpBYcEhK5Vq1aQS6XY/z48UrnLLRr105DLatYfA/Fjh49igEDBihsBiaRSHQucCOmrSjLi8vdqfJgwCJynLNQjO+hWIMGDVC/fn188cUXSgM3FxcXDbWMKhrTVijHgKXy4pCQyOnqnIUX8T0Uu3HjBnbv3q2ww6suYpI76MyW+0QlGLCInK7OWXgR30Oxjh074vTp0zodsLwqyZ2uBCwldD1tBekODgmJHOcsFON7KLZy5UrMmTMHn3zyCRo1alQqcNOFFTJMcqdI19NWvJiagHN5Ki8GLCLHOQvF+B6KvWy3V10J3KpUqYJjx47B3d0dVapUQUJCAurXr49jx44hMDAQFy5c0HQTK5RUKkVGRgZsbW0Vzp8+fRodOnRAZmamhlpWMdzc3F5ZRhfn8lRGHBISOc5ZKMb3UIy7vQIGBgZC4GZnZ4fU1FTUr18fVlZWuHnzpoZbV3FK9h+RSCTo1KlTmWkrKrtr165puglUQRiwiBznLBTjeyhbVlYWqlSpoulmVBgmuSvGtBWKOJen8uOQkMhxzkIxvodi8+bNg6urK/r16wcA6NOnD7Zt2wZHR0fs27dPJxK8vSzJ3erVq9G0aVNNN7FCMW1FMV2fy6MLGLCIHOcsFON7KObm5oZff/0VLVu2RExMDPr27YtNmzZh8+bNSE1NxYEDBzTdRNIgXU5boetzeXQBh4REjnMWivE9FEtPT4ezszMAYO/evejbty/8/Pzg6uoKb29vDbeuYnTs2BHbt28vNQyWk5ODXr16ITY2VjMN0xBdT1vBuTy6gwGLFtK1OQtl0cX3YG1tjZs3b8LZ2RnR0dGYM2cOgOJfTpX9F1OJuLi4UpvFAcDTp09x+PBhDbRIswYNGgS5XI41a9YoXUFX2XEuj+5gwCJynLNQjO+h2IcffogBAwagTp06ePDgAQICAgAUb6ZW2ScknzlzRvj3f//9h/T0dOFzUVERoqOjUb16dU00TaNOnz6t02krwsPDAUD4+aDrc3kqM85hETnOWSjG91CssLAQ33//PW7evImgoCB4eHgAABYtWgQLCwsMHz5cwy1UH6lUKvQeKPuxZWJigh9++AGffPJJRTdNozp06IAvv/wSvr6+mm6KaOjyXJ7KjAGLyJmYmODSpUtwdnbG+PHj8fTpU/z000+4dOkSvL298fDhQ003sULwPRR78OABqlatCgC4efMmVq1ahSdPnqB79+5o27athlunXjdu3IBcLkfNmjVx/PhxhcmVhoaGsLOzg56engZbqBlXrlzBqFGjMGjQIJ1OW6Hrc3l0AYeERI5zForp+ns4e/Ysunfvjps3b6JOnTrYuHEjunTpgry8PEilUixatAhbt24VxvMro5LdjDkBW9G9e/dw5coVDB06VDini2krdH0ujy5gwCJyujxn4Xm6/h6++OILNGrUCL/++ivWrVuH999/H926dcOqVasAAGPHjsXcuXMrdcDyvCtXrmDx4sU4f/48gOLUDePHj0etWrU03LKK98knn8DDwwO//fabTv+i1vW5PLqAAYvILVq0CK6urrh58ya+/fZbYQb8nTt38Nlnn2m4dRVH19/DiRMnEBsbi8aNG6NJkyZYuXIlPvvsM2F/mrFjx6JFixYabmXF2L9/P3r06IGmTZuiVatWAIC///4b7777Lvbs2YPOnTtruIUVi2krinl5eeHmzZsMWCoxzmEROV2es/A8XX8PUqkU6enpwi6eFhYWOH36NGrWrAkAyMjIgJOTk050/3t4eMDf3x9z585VOD9lyhQcOHAASUlJGmqZZnTv3h1BQUE6v3SXc3kqPwYsIvWqOQt5eXmVfs4CwPdQ4sVdPC0sLHDmzBkhU60uBSzGxsY4e/Ys6tSpo3D+0qVLaNy4scKES13AtBXFjh49igEDBuD69evCOV2cy1OZMWARqYCAAOjr62PKlClYt24d9u7dC39/f4U5C4mJiTh69KiGW6pefA/FpFIpAgIChMRue/bsQceOHWFmZgYAyM/PR3R0tE78UHZ2dsbChQvRp08fhfObN2/GxIkTkZqaqqGWaQbTVhRr0KAB6tevjy+++ELpXJ6SSdukvRiwiFS1atWEOQu5ubmwtLTEiRMn4OnpCQC4cOECWrRogaysLM02VM34Hoo9vwLkZSIjI9XcEs2ZPXs2Jk6ciAULFmDRokWYMmUKWrZsCaB4Dsu8efMQGhqK6dOna7ilpAlmZmbM6F7JMWARKc5ZKMb3QCVKsvHa2tpi8eLF+O6775CWlgYAcHJywqRJkzBu3DidXSXzPF1MW8G5PJUfVwmJ2Is/eHX1BzHfAwH/291WIpEgJCQEISEhePToEYDiQFZXMW1Fse7duyMkJARnz57V6bk8lRl7WESKcxaK8T1QiRcnHlMxpq0oxrk8lR8DFpHinIVifA9UQiqVwsrK6pU9bJmZmRXUInFg2grSFRwSEin+Ai7G90DPmzVrFqysrDTdDFHR9bQVL6OLc3kqMwYsRKQ1Pv74Y2ECNhXT9bQVJTiXp/Ire9CPiEhEONlauUWLFmHMmDFo0KABYmJidC5tRYkVK1bA2dkZABATE4M///wT0dHRCAgIwKRJkzTcOlIFzmEhIq3w4hJ3KqbraStKcC5P5cceFiLSCjKZjMHKc86ePQtXV1fY2dmhXr16SE5OhpeXFxYtWoSVK1eiY8eO2Llzp6abWWFK5vIAQHR0NHx9fQFwLk9lwoCFiEgLffHFF2jUqBEOHTqE9u3b4/3330e3bt2QnZ2Nhw8f4tNPPy2VILIyK5nL07lzZ52ey1OZcUiIiEgLMW2FosLCQnz//fe4efMmgoKC4OHhAaB4jo+FhQWGDx+u4RbS22LAQkSkhZi2QhHn8lR+HBIiItJSTFvBuTy6hD0sRERaiGkrigUEBEBfXx9TpkzBunXrsHfvXvj7+2PVqlUAgLFjxyIxMRFHjx7VcEvpbTFgISLSQkxbUYxzeXQHAxYiItJanMujOziHhYiItBrn8ugG5hIiIiKtFhQUJMzlefr0KUaNGqUwl4cqBw4JERGR1uJcHt3BgIWIiIhEj3NYiIiISPQYsBAREZHoMWAhIiIi0WPAQkRERKLHgIWIiIhEjwELERERiR4DFiIiIhI9BixEREQkev8Hz8KWr02ymNsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[float_bsmt_feat].isnull())\n",
    "\n",
    "# heatmap shows no missing values but they are present "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2805</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2825</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2905</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2  BsmtFinSF1  \\\n",
       "Id                                                                          \n",
       "18        NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "40        NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "91        NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "103       NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "157       NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "...       ...      ...          ...          ...          ...         ...   \n",
       "2804      NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "2805      NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "2825      NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "2892      NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "2905      NaN      NaN          NaN          NaN          NaN         0.0   \n",
       "\n",
       "      BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  BsmtFullBath  BsmtHalfBath  \n",
       "Id                                                                    \n",
       "18           0.0        0.0          0.0           0.0           0.0  \n",
       "40           0.0        0.0          0.0           0.0           0.0  \n",
       "91           0.0        0.0          0.0           0.0           0.0  \n",
       "103          0.0        0.0          0.0           0.0           0.0  \n",
       "157          0.0        0.0          0.0           0.0           0.0  \n",
       "...          ...        ...          ...           ...           ...  \n",
       "2804         0.0        0.0          0.0           0.0           0.0  \n",
       "2805         0.0        0.0          0.0           0.0           0.0  \n",
       "2825         0.0        0.0          0.0           0.0           0.0  \n",
       "2892         0.0        0.0          0.0           0.0           0.0  \n",
       "2905         0.0        0.0          0.0           0.0           0.0  \n",
       "\n",
       "[88 rows x 11 columns]"
      ]
     },
     "execution_count": 662,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bsmt = df[category_bsmt_feat + float_bsmt_feat]\n",
    "df_bsmt[df_bsmt.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtFinSF1      0\n",
       "BsmtFinSF2      0\n",
       "BsmtUnfSF       0\n",
       "TotalBsmtSF     0\n",
       "BsmtFullBath    0\n",
       "BsmtHalfBath    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bsmt_float = 0.00\n",
    "\n",
    "for i in float_bsmt_feat:\n",
    "    df_mvi[i].replace(np.nan, bsmt_float, inplace=True)\n",
    "\n",
    "df_mvi[float_bsmt_feat].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Electrical</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>SBrkr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Electrical KitchenQual  KitchenAbvGr\n",
       "Id                                       \n",
       "1380        NaN          Gd             1\n",
       "1556      SBrkr         NaN             1"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Electrical\", \"KitchenQual\", \"KitchenAbvGr\"]][df[[\"Electrical\", \"KitchenQual\", \"KitchenAbvGr\"]].isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Electrical</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>SBrkr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Electrical KitchenQual  KitchenAbvGr\n",
       "Id                                       \n",
       "1380        NaN          Gd             1\n",
       "1556      SBrkr         NaN             1"
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ek = df[['Electrical', 'KitchenQual', 'KitchenAbvGr']]\n",
    "df_ek[df_ek.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrical_mode = df[\"Electrical\"].mode()[0]\n",
    "df_mvi[\"Electrical\"].replace(np.nan, electrical_mode, inplace=True)\n",
    "df_mvi[\"Electrical\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 667,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KitchenQual_mode = df[\"KitchenQual\"].mode()[0]\n",
    "df_mvi[\"KitchenQual\"].replace(np.nan, KitchenQual_mode, inplace=True)\n",
    "df_mvi[\"KitchenQual\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageYrBlt    0\n",
       "GarageCars     0\n",
       "GarageArea     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "garage_float = ['GarageYrBlt', 'GarageCars', 'GarageArea']\n",
    "garage_float_mode = 0.00\n",
    "\n",
    "for i in garage_float:\n",
    "    df_mvi[i].replace(np.nan, garage_float_mode, inplace=True)\n",
    "df_mvi[garage_float].isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting Numeric feature to categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass : int64\n",
      "YearBuilt : int64\n",
      "YearRemodAdd : int64\n",
      "GarageYrBlt : float64\n",
      "YrSold : int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MSSubClass      object\n",
       "YearBuilt       object\n",
       "YearRemodAdd    object\n",
       "GarageYrBlt     object\n",
       "YrSold          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 669,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_to_cat = [\"MSSubClass\", \"YearBuilt\", \"YearRemodAdd\", \"GarageYrBlt\", \"YrSold\"]\n",
    "for feat in num_to_cat:\n",
    "    print(f\"{feat} : {df[feat].dtype}\")\n",
    "    df_mvi[feat]=df_mvi[feat].astype(str)\n",
    "df_mvi[num_to_cat].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id\n",
       "1       Feb\n",
       "2       May\n",
       "3       Sep\n",
       "4       Feb\n",
       "5       Dec\n",
       "       ... \n",
       "2915    Jun\n",
       "2916    Apr\n",
       "2917    Sep\n",
       "2918    Jul\n",
       "2919    Nov\n",
       "Name: MoSold, Length: 2919, dtype: object"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import calendar\n",
    "\n",
    "df_mvi['MoSold']=df_mvi[\"MoSold\"].apply(lambda x:calendar.month_abbr[x])\n",
    "df_mvi['MoSold']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting categorical to numberic feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ordinal Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.api.types import CategoricalDtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mvi['BsmtCond'] = df['BsmtCond'].astype(CategoricalDtype(categories=['NA', 'Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['BsmtFinType1'] = df['BsmtFinType1'].astype(CategoricalDtype(categories=['NA', 'Unf', 'LwQ', 'Rec', 'BLQ','ALQ', 'GLQ'], ordered = True)).cat.codes\n",
    "df_mvi['BsmtFinType2'] = df['BsmtFinType2'].astype(CategoricalDtype(categories=['NA', 'Unf', 'LwQ', 'Rec', 'BLQ','ALQ', 'GLQ'], ordered = True)).cat.codes\n",
    "df_mvi[\"BsmtExposure\"] = df[\"BsmtExposure\"].astype(CategoricalDtype(categories=['NA', 'No', 'Mn', 'Av', 'Gd'], ordered=True)).cat.codes\n",
    "df_mvi['BsmtQual'] = df['BsmtQual'].astype(CategoricalDtype(categories=['NA', 'Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['ExterQual'] = df['ExterQual'].astype(CategoricalDtype(categories=['Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['ExterCond'] = df['ExterCond'].astype(CategoricalDtype(categories=['Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['Functional'] = df['Functional'].astype(CategoricalDtype(categories=['Sal', 'Sev', 'Maj2', 'Maj1', 'Mod','Min2','Min1', 'Typ'], ordered = True)).cat.codes\n",
    "df_mvi['GarageCond'] = df['GarageCond'].astype(CategoricalDtype(categories=['NA', 'Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['GarageQual'] = df['GarageQual'].astype(CategoricalDtype(categories=['NA', 'Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['GarageFinish'] = df['GarageFinish'].astype(CategoricalDtype(categories=['NA', 'Unf', 'RFn', 'Fin'], ordered = True)).cat.codes\n",
    "df_mvi['FireplaceQu'] = df['FireplaceQu'].astype(CategoricalDtype(categories=['Na', 'Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered=True)).cat.codes\n",
    "df_mvi['PoolQC'] = df['PoolQC'].astype(CategoricalDtype(categories=['NA', 'Fa', 'TA', 'Gd', 'Ex'], ordered=True)).cat.codes\n",
    "df_mvi['HeatingQC'] = df['HeatingQC'].astype(CategoricalDtype(categories=['Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['KitchenQual'] = df['KitchenQual'].astype(CategoricalDtype(categories=['Po', 'Fa', 'TA', 'Gd', 'Ex'], ordered = True)).cat.codes\n",
    "df_mvi['PavedDrive'] = df['PavedDrive'].astype(CategoricalDtype(categories=['N', 'P', 'Y'], ordered = True)).cat.codes\n",
    "df_mvi['Utilities'] = df['Utilities'].astype(CategoricalDtype(categories=['ELO', 'NASeWa', 'NASeWr', 'AllPub'], ordered = True)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2919 entries, 1 to 2919\n",
      "Data columns (total 80 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   MSSubClass     2919 non-null   object \n",
      " 1   MSZoning       2919 non-null   object \n",
      " 2   LotFrontage    2919 non-null   float64\n",
      " 3   LotArea        2919 non-null   int64  \n",
      " 4   Street         2919 non-null   object \n",
      " 5   Alley          2919 non-null   object \n",
      " 6   LotShape       2919 non-null   object \n",
      " 7   LandContour    2919 non-null   object \n",
      " 8   Utilities      2919 non-null   int8   \n",
      " 9   LotConfig      2919 non-null   object \n",
      " 10  LandSlope      2919 non-null   object \n",
      " 11  Neighborhood   2919 non-null   object \n",
      " 12  Condition1     2919 non-null   object \n",
      " 13  Condition2     2919 non-null   object \n",
      " 14  BldgType       2919 non-null   object \n",
      " 15  HouseStyle     2919 non-null   object \n",
      " 16  OverallQual    2919 non-null   int64  \n",
      " 17  OverallCond    2919 non-null   int64  \n",
      " 18  YearBuilt      2919 non-null   object \n",
      " 19  YearRemodAdd   2919 non-null   object \n",
      " 20  RoofStyle      2919 non-null   object \n",
      " 21  RoofMatl       2919 non-null   object \n",
      " 22  Exterior1st    2919 non-null   object \n",
      " 23  Exterior2nd    2919 non-null   object \n",
      " 24  MasVnrType     2919 non-null   object \n",
      " 25  MasVnrArea     2919 non-null   float64\n",
      " 26  ExterQual      2919 non-null   int8   \n",
      " 27  ExterCond      2919 non-null   int8   \n",
      " 28  Foundation     2919 non-null   object \n",
      " 29  BsmtQual       2919 non-null   int8   \n",
      " 30  BsmtCond       2919 non-null   int8   \n",
      " 31  BsmtExposure   2919 non-null   int8   \n",
      " 32  BsmtFinType1   2919 non-null   int8   \n",
      " 33  BsmtFinSF1     2919 non-null   float64\n",
      " 34  BsmtFinType2   2919 non-null   int8   \n",
      " 35  BsmtFinSF2     2919 non-null   float64\n",
      " 36  BsmtUnfSF      2919 non-null   float64\n",
      " 37  TotalBsmtSF    2919 non-null   float64\n",
      " 38  Heating        2919 non-null   object \n",
      " 39  HeatingQC      2919 non-null   int8   \n",
      " 40  CentralAir     2919 non-null   object \n",
      " 41  Electrical     2919 non-null   object \n",
      " 42  1stFlrSF       2919 non-null   int64  \n",
      " 43  2ndFlrSF       2919 non-null   int64  \n",
      " 44  LowQualFinSF   2919 non-null   int64  \n",
      " 45  GrLivArea      2919 non-null   int64  \n",
      " 46  BsmtFullBath   2919 non-null   float64\n",
      " 47  BsmtHalfBath   2919 non-null   float64\n",
      " 48  FullBath       2919 non-null   int64  \n",
      " 49  HalfBath       2919 non-null   int64  \n",
      " 50  BedroomAbvGr   2919 non-null   int64  \n",
      " 51  KitchenAbvGr   2919 non-null   int64  \n",
      " 52  KitchenQual    2919 non-null   int8   \n",
      " 53  TotRmsAbvGrd   2919 non-null   int64  \n",
      " 54  Functional     2919 non-null   int8   \n",
      " 55  Fireplaces     2919 non-null   int64  \n",
      " 56  FireplaceQu    2919 non-null   int8   \n",
      " 57  GarageType     2919 non-null   object \n",
      " 58  GarageYrBlt    2919 non-null   object \n",
      " 59  GarageFinish   2919 non-null   int8   \n",
      " 60  GarageCars     2919 non-null   float64\n",
      " 61  GarageArea     2919 non-null   float64\n",
      " 62  GarageQual     2919 non-null   int8   \n",
      " 63  GarageCond     2919 non-null   int8   \n",
      " 64  PavedDrive     2919 non-null   int8   \n",
      " 65  WoodDeckSF     2919 non-null   int64  \n",
      " 66  OpenPorchSF    2919 non-null   int64  \n",
      " 67  EnclosedPorch  2919 non-null   int64  \n",
      " 68  3SsnPorch      2919 non-null   int64  \n",
      " 69  ScreenPorch    2919 non-null   int64  \n",
      " 70  PoolArea       2919 non-null   int64  \n",
      " 71  PoolQC         2919 non-null   int8   \n",
      " 72  Fence          2919 non-null   object \n",
      " 73  MiscFeature    2919 non-null   object \n",
      " 74  MiscVal        2919 non-null   int64  \n",
      " 75  MoSold         2919 non-null   object \n",
      " 76  YrSold         2919 non-null   object \n",
      " 77  SaleType       2919 non-null   object \n",
      " 78  SaleCondition  2919 non-null   object \n",
      " 79  SalePrice      1460 non-null   float64\n",
      "dtypes: float64(11), int64(20), int8(17), object(32)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_mvi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encode = df_mvi.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "object_features = df_encode.select_dtypes(include=\"object\").columns.tolist()\n",
    "print(len(object_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (2919, 80)\n",
      "shpae:  (2919, 512)\n"
     ]
    }
   ],
   "source": [
    "print(\"shape: \", df_encode.shape)\n",
    "df_encode = pd.get_dummies(df_encode, columns=object_features, drop_first=True, prefix=object_features)\n",
    "print(\"shpae: \", df_encode.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bol_to_int = df_encode.select_dtypes(include='boolean').columns.tolist()\n",
    "\n",
    "df[bol_to_int]=df[bol_to_int].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split train test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 512)"
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encode.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460"
      ]
     },
     "execution_count": 691,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_train = df_train.shape[0]\n",
    "len_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>196.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 511 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LotFrontage  LotArea  Utilities  OverallQual  OverallCond  MasVnrArea  \\\n",
       "Id                                                                          \n",
       "1          65.0     8450          3            7            5       196.0   \n",
       "2          80.0     9600          3            6            8         0.0   \n",
       "3          68.0    11250          3            7            5       162.0   \n",
       "\n",
       "    ExterQual  ExterCond  BsmtQual  BsmtCond  ...  SaleType_ConLI  \\\n",
       "Id                                            ...                   \n",
       "1           3          2         4         3  ...               0   \n",
       "2           2          2         4         3  ...               0   \n",
       "3           3          2         4         3  ...               0   \n",
       "\n",
       "    SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "Id                                                            \n",
       "1                0             0             0            1   \n",
       "2                0             0             0            1   \n",
       "3                0             0             0            1   \n",
       "\n",
       "    SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "Id                                                                      \n",
       "1                       0                     0                     0   \n",
       "2                       0                     0                     0   \n",
       "3                       0                     0                     0   \n",
       "\n",
       "    SaleCondition_Normal  SaleCondition_Partial  \n",
       "Id                                               \n",
       "1                      1                      0  \n",
       "2                      1                      0  \n",
       "3                      1                      0  \n",
       "\n",
       "[3 rows x 511 columns]"
      ]
     },
     "execution_count": 699,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = df_encode[:len_train].drop(\"SalePrice\", axis=1)\n",
    "y_train = df_encode['SalePrice'][:len_train]\n",
    "\n",
    "X_test = df_encode[len_train:].drop(\"SalePrice\", axis =1)\n",
    "\n",
    "X_train[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc =StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "z = (x - u)/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.fit(X_train)\n",
    "\n",
    "X_train = sc.transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.21271975, -0.20714171,  0.02618016, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503],\n",
       "       [ 0.46815755, -0.09188637,  0.02618016, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503],\n",
       "       [-0.07654429,  0.07347998,  0.02618016, ..., -0.11785113,\n",
       "         0.4676514 , -0.30599503]])"
      ]
     },
     "execution_count": 702,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR()\n",
    "lr = LinearRegression()\n",
    "sgdr = SGDRegressor()\n",
    "knr = KNeighborsRegressor()\n",
    "dtr = DecisionTreeRegressor()\n",
    "gbr = GradientBoostingRegressor()\n",
    "rfr = RandomForestRegressor()\n",
    "xgbr = XGBRegressor()\n",
    "mlpr = MLPRegressor()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"a\" : ['LinearRegression', lr],\n",
    "    \"b\" : [\"SVR\", svr],\n",
    "    \"c\" : [\"SGDRegressor\", sgdr],\n",
    "    \"d\" : [\"KNeighborsRegressor\", knr],\n",
    "    \"e\" : [\"GassianProcessRegressor\", gpr],\n",
    "    \"f\" : [\"DecisionTreeRegressor\", dtr],\n",
    "    \"h\" : [\"RandomForestRegressor\", rfr],\n",
    "    \"i\" : [\"XGBRegressor\", xgbr],\n",
    "    \"j\" : [\"MLPRegressor\", mlpr],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.metrics import make_scorer, r2_score\n",
    "\n",
    "def test_mode(model, X_train = X_train, y_train = y_train):\n",
    "\n",
    "    cv = KFold(n_splits= 7, shuffle=True, random_state=45)\n",
    "    r2 = make_scorer(r2_score)\n",
    "    r2_val_score = cross_val_score(model, X_train, y_train, cv=cv, scoring=r2)\n",
    "    score = [r2_val_score.mean()]\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Model :  LinearRegression\n",
      "Score of the model :  [-3.930124059912763e+25]\n",
      "Training Model :  SVR\n",
      "Score of the model :  [-0.05213873772439759]\n",
      "Training Model :  SGDRegressor\n",
      "Score of the model :  [-1277.0466122077064]\n",
      "Training Model :  KNeighborsRegressor\n",
      "Score of the model :  [0.5549355281479554]\n",
      "Training Model :  GassianProcessRegressor\n",
      "Score of the model :  [-5.3986443353742075]\n",
      "Training Model :  DecisionTreeRegressor\n",
      "Score of the model :  [0.657267724311274]\n",
      "Training Model :  GradientBoostingRegressor\n",
      "Score of the model :  [0.8703746406552929]\n",
      "Training Model :  RandomForestRegressor\n",
      "Score of the model :  [0.8438063705075676]\n",
      "Training Model :  XGBRegressor\n",
      "Score of the model :  [0.8608858607692882]\n",
      "Training Model :  MLPRegressor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of the model :  [-4.823858780422973]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model_score =[]\n",
    "for model in models:\n",
    "    print(\"Training Model : \", models[model][0])\n",
    "    score = test_mode(models[model][1], X_train, y_train)\n",
    "    print(\"Score of the model : \",score)\n",
    "    model_score.append([models[model][0], score[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['LinearRegression', -3.930124059912763e+25],\n",
       " ['SVR', -0.05213873772439759],\n",
       " ['SGDRegressor', -1277.0466122077064],\n",
       " ['KNeighborsRegressor', 0.5549355281479554],\n",
       " ['GassianProcessRegressor', -5.3986443353742075],\n",
       " ['DecisionTreeRegressor', 0.657267724311274],\n",
       " ['GradientBoostingRegressor', 0.8703746406552929],\n",
       " ['RandomForestRegressor', 0.8438063705075676],\n",
       " ['XGBRegressor', 0.8608858607692882],\n",
       " ['MLPRegressor', -4.823858780422973]]"
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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