House Price Prediction Using PCA- Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# House Price Prediction - Regression and Others\n",
    "\n",
    "### With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Required Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"~/Downloads/Excel and CSVs/housetrain.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/Excel and CSVs/housetest.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>...</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</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  ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  \\\n",
       "0   1          60            0         0    NaN   NaN         NaN        0   \n",
       "1   2          20            0         0    NaN   NaN         NaN        0   \n",
       "2   3          60            0         0    NaN   NaN         NaN        0   \n",
       "3   4          70            0         0    NaN   NaN         NaN        0   \n",
       "4   5          60            0         0    NaN   NaN         NaN        0   \n",
       "\n",
       "  GarageType  GarageYrBlt  ... Street  Alley  LotShape MSZoning LotFrontage  \\\n",
       "0     Attchd       2003.0  ...   Pave    NaN       Reg       RL        65.0   \n",
       "1     Attchd       1976.0  ...   Pave    NaN       Reg       RL        80.0   \n",
       "2     Attchd       2001.0  ...   Pave    NaN       IR1       RL        68.0   \n",
       "3     Detchd       1998.0  ...   Pave    NaN       IR1       RL        60.0   \n",
       "4     Attchd       2000.0  ...   Pave    NaN       IR1       RL        84.0   \n",
       "\n",
       "  MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2    2008        WD         Normal     208500  \n",
       "1      5    2007        WD         Normal     181500  \n",
       "2      9    2008        WD         Normal     223500  \n",
       "3      2    2006        WD        Abnorml     140000  \n",
       "4     12    2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have features with different scales and hence we need to apply scaling on the data so that it can eliminate the affect of the scale\n",
    "\n",
    "#### import the data > Try Looking the Missing Values > PCA > Make Prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "MSSubClass          0\n",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "PoolQC           1453\n",
       "Fence            1179\n",
       "MiscFeature      1406\n",
       "MiscVal             0\n",
       "GarageType         81\n",
       "GarageYrBlt        81\n",
       "GarageFinish       81\n",
       "GarageCars          0\n",
       "GarageArea          0\n",
       "GarageQual         81\n",
       "GarageCond         81\n",
       "PavedDrive          0\n",
       "WoodDeckSF          0\n",
       "OpenPorchSF         0\n",
       "EnclosedPorch       0\n",
       "3SsnPorch           0\n",
       "BsmtFullBath        0\n",
       "BsmtHalfBath        0\n",
       "FullBath            0\n",
       "HalfBath            0\n",
       "BedroomAbvGr        0\n",
       "KitchenAbvGr        0\n",
       "KitchenQual         0\n",
       "TotRmsAbvGrd        0\n",
       "Functional          0\n",
       "Fireplaces          0\n",
       "                 ... \n",
       "ExterQual           0\n",
       "ExterCond           0\n",
       "Foundation          0\n",
       "YearBuilt           0\n",
       "YearRemodAdd        0\n",
       "RoofStyle           0\n",
       "RoofMatl            0\n",
       "Exterior1st         0\n",
       "BldgType            0\n",
       "HouseStyle          0\n",
       "OverallQual         0\n",
       "OverallCond         0\n",
       "Neighborhood        0\n",
       "Condition1          0\n",
       "Condition2          0\n",
       "LandContour         0\n",
       "Utilities           0\n",
       "LotConfig           0\n",
       "LandSlope           0\n",
       "LotArea             0\n",
       "Street              0\n",
       "Alley            1369\n",
       "LotShape            0\n",
       "MSZoning            0\n",
       "LotFrontage       259\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            0\n",
       "SaleCondition       0\n",
       "SalePrice           0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the Missing Values\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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HEtKJTqjbuPHMwVaPzOERKemXhHgjv451LgBOBbD9H2APgLhY+fe4JRKJRH9Qg5l2rz0idwF+Q4j0ByG+yF8BJC3eiK0NfAK4JgryRA3IOgsNisNQYuSp21jxLFfexoqRsNM+BjhD0meA5wkCGuD1wM8lzQTuJkT6SyQSif6hBjPtnght21cDV8fPzxAsSprrXAdM6EV/iUSiHtRNp03/q7QH3yMyJfZNJMaOupn8eUb/S+1u7bQfBJ4jeD7OiBYjRwFbEZ5ZTwK7234s1t+YYCY4H/CU7cKY2r0iCereUKcbL5HomP6X2T2ZaW9i+6nM/nG2vwwgaX/gcGAfSYsDPwI2s/1wxlOyb8jmgiz63G77Ku3qwiBeU2Jkqds4mStijzTTZBGyMEOZaz4KnG/74Viv72JqZwdY0edO2g8Kg3hNicQwajDT7jaetoHLJd0cvRgBkHS0pEeAnQkzbYDVgCUkXR3r71p0UEl7S5osafKsWS90eYqJRGKsKFNP9qMJ6dxg8reB7ceiquMKSffavsb2YcBhkg4F9gWOiH29hRAkakHgOknX2/5r80F76RGZFiJ7Q3JjT/SavhxDNZhpdyW0GwuMtp+UdAGwHnBNpsqZwO8IQnsaYfHxBeAFSdcA6xCdb0aKJKh7Q1/eYIm+p27jxrmBNfqLjtUjkhaWtGjjM/A+YIqkrC32lsC98fOFwIaS5pW0ELA+IaRrIpEYUOo2afKs6ttY0c1MexngghBGhHmBM21fKunXklYnvGg8BOwDYPseSZcCd8TvfpaSICQSib6ix8JY0mbA8cA4gsw7JqfO9sBXCGuEt9v+aNkxOxbath8gqDeay7cpaXMcIcHvqNFsfpfojKTTTnRC3cZJL2fQMavXCcB7CerhmyRNsn13ps4E4FDC+uDTVUyhB94jMgns3lC3my+R6IQeqz3WA6bGCS6SziY4Ht6dqbMXcILtp6GaKXRXJn8xct95ku6VdI+kt0t6paQrJN0f/18i1t1K0h2SbovmfO/spu9EItH/1G3S5JmqvGVNk+O2d9PhlgceyexPi2VZVgNWk/RnSddHdUop3c60jwcutb1tDLu6ECEX5JW2j5F0CHAI8AXgSmCSbUtaGzgHWKPL/hOJRKJntDPTzpomF6C8Zk378xIC6W1MyOR1raS1YuC9XLqxHnkFsBFwMoDt6bGjrYDTY7XTgQ/H75+33TjhrKdkIpEYUOqmVvMsVd4qMI2Q5LzBCsBjOXUutP2y7b8D99EiGmo36pHXEnJAnirpVkk/i6Z/y9h+HCD+P1uxLmlrSfcSbLc/XnTg5BGZSAwGtVOP9Nbk7yZggqRVoiZiR2BSU53fAJsASFqSoC55oOyg3ahH5gXWBfazfYOk4wmqkEJsX0AwE9wIOAp4T0G95BHZZyTrkcTcgF1pBl3xWJ4haV/gMoLJ3ym275J0JDDZ9qT43fsk3U2Ilnqw7X+VHbcboT0NmGb7hrh/HkFoPyFpWduPS1qWEJ61+WKukbSqpCWbIgT2nCSoe0MynUx0Qt0e7r12mrF9MXBxU9nhmc8GDopbJbqx0/6HpEckrW77PkJMkbvjthsh7dhuBE9IJL0O+FtciFwXmB8ofaIk+ou63YCJRLvMmtm7mfZI0a31yH6EfJDzE/QwexD05OdI2hN4GNgu1t0G2FXSy8BLwA6Zhckxp+osskhwlbUfBGGX1COJqtR5bFRcYBxT1EdyM5d2ddplwiUF8U8kRp6xesDPmP5o1xL3wTe9t7K8Wfm2K8ZEwiePyEQiMWLUbXLU53NYoPsckYsDPwPWIthdfxw4EFg9VlkceMb2m2L9Q4E9Cauk+9u+rJv+E6NHUo8kOqF2iX1roB7puUek7R0aX0r6NvBs/LwmwU7xDcBywO8lrWZ7ZpfnkBgF6nTjJRKd0kuTv5FiJDwiG98L2B44KxZtBZxt+3/R82cqIaBKIpEYUMoe9v2oupw5U5W3sWIkPCIbbAg8Yfv+uF8leAqQPCITibmBfnx7s1V5Gyu6EdoNj8gTbb8ZeIHhHpE7MTTLhmrBU0KhfZLtibYnzjPPwnlVEolEDahfYt+exh4ZEUbCIxJJ8wIfISTyzdZvFTwlkUjMJfTnTHusz6A1Hc+0bf8DeCSmFoMhj0gIMUXutT0t02QSsKOkBSStQohkdWOn/ScSif6nHwVzGYM+04Z8j0gIViJZ1QgxUMo5BME+A/h0shxJJAabupn8zZzVVV6YUaEroW37NmBiTvnuBfWPBo7ups92SVH+ekOy0070mn70UK6DemTgPSIhCetekKL8JdqhihDuF0GdZVYN7LQ7FtpRl/2rTNFrgcOBVxFssmcRwrLubvsxSQcDO2f6fT2wlO1/d3oOVUiCpjek3zHRDo3x0o+CuYw6ONd0E5r1PqDhnj4OeBS4AHja9pdj+f4EQb6P7eOA42L5FsBnRlpgJxKJ0adugjrL3KQeeTchVvZDTeVFuSCbbbgTiVw6meE3C41+1J0OMnVe/xho9UgTw6xFJB0N7EqIO7JJtqKkhYDNgH2LDhZT0e8NoHGL0Y2DTVqIrDe9uOHrJDQGjbr99gNvPQIQzf22BA5tlNk+DDgsRvXbFzgi02QL4M9lqpFe5ohMgrrepJl2vambyV8NtCM9mWl/ALjF9hM5351JyLyeFdpz2HAnEkWkmXZiNJlb1CPD9NOSJmSCRG0J3Jv5bjHgXcAuPeg3Mcok4Zdol7qNmYG2HoHZ+un3Ap/MFB8TzQFnAQ8B+2S+2xq43HYK3Vcz6nbzJRKd0ONk7CNCtx6RLxLssrNl25TUPw04rZs+E2NDnS0CEmNH/XTaAz7TTsw91OnGS9SDfpwIzKiBeqQr+xZJn5F0l6Qpks6SNF7SKpJukHS/pF9F65Jsm20lWdIcMUsS/Us29nGyyElUpaog7geBDWGmXXUbK7pxY18e2B9Y0/ZLMYLfjsDmwHdtny3px4REvifGNovGNjcUHLbnJDvt3tAvN1WiXpSpR/pxTNVBp92tJfm8wIIx6cFCwOPApoSECACnAx/O1D8KOBb4b5f9VibNDhOJRFUGeqZt+1FJ3wIeBl4CLgduBp6xPSNWm50HUtKbgRVtXyTpc2XH7tYjsugJngR359RtQSnRH9RtzNRhpt2NemQJQjS/VYBngHMJjjbNWNI8wHeB3ascu1uPyCSce0/dbr5E/9OPnqozB9x65D3A323/E0DS+cA7gMUlzRtn2408kIsCawFXSwJ4NTBJ0pa2J3dzAa1IOu1EYuyonU67/2V2V0L7YeBt0cHmJUKkv8nAVcC2wNnAbsCFtp8Flmw0lHQ18LmRFtiQBHUikajOrEGeadu+QdJ5wC2EnI+3ElQavwPOlvS1WHZyL060U9JMuzf0o01toj+p89gY+IBRto9geDAoCAl+12vRbuNu+m2HqoK6Sr2ywVjWvs6DOJFolzo/4Ad6IXLQ6HZg1WlgJhKJfGap/9UjI+ERea2k2+L2mKTfxLprSLpO0v9amfz1kiRMe8eCy204e0skqtDp2+lYMbONbazoWGhnPCIn2l4LGAfsaHtD22+y/SbgOuD82OTfsf63ujzntkmCpjckR6VEL+nHe3KWqm9VkLSZpPskTZV0SEm9yuE9ulWPNDwiXyZ4RD6WOYlFCd6RewDYfhJ4UtIHu+yzLZKASSTGjro5ZfXSeiQmPD+BEL56GnCTpEm2726q11Z4j556RNq+PFNla+BK2/9p99i9zBGZ6A11uvESiU7psfXIesBU2w8ASDqb4JB4d1O9RniPSmrjbtQjWY/I5YCFJWUz0nSccd32SbYn2p6YBHZ/kKL8JTqhbg/7dtQjkvaWNDmz7d10uOWBRzL7s8N6NMiG96h6jiPhEflLSa8iPGW27uL4PaFug6ZfSb9johPqpx6pTjbcRgF5upbZk/l2w3s0GAmPSIDtgItsj1o0vyKKZoV1Gkj9wFjZ3qZs7InRZGZvLf6mAStm9hthPRp0FN5jJDwiIcTVPiZbX9KrCUL9FcAsSQcSYnG3rfMuY8HlNiy8SbPldZsBjDXNv+to9tsPx0hUp52x0m8P1B4719wETJC0CvAoQS5+tPFlp+E9RsIjMtfj0fY/CE+aEaV5sKSZdm9IeuxEVdoRxP12H/ZSaNueIWlf4DKCSfQptu+SdCQw2fakTo6bPCITicSIUfZG22+zbIBep4i0fTFwcVPZ4QV1N65yzK6EtqQDgL0ICvef2v6epKMIViWzgCeB3W0/Jmkx4JfASrHfb9k+tZv+E6NHv9xUicGhH8fUQMcekbQWQWCvB0wHLpX0O+A421+OdfYHDgf2AT4N3G17C0lLAfdJOsP29G4vIjHy1DkIUGLsqNs4GUv39Kp0M9N+PXC97RcBJP0R2Nr2sZk6CzNk4mJgUYVl0kUIbu0zSNSCut18if6gbgv+g54EYQpwdLTJfomQhX0ygKSjgV2BZ4FNYv0fApMYymSzg+3ct5HkEZlIDD79qNOug3pEdueOm5L2JKg9nie4Zr5k+zOZ7w8Fxts+QtK2wAbAQcCqwBXAOq1M/rrNEVmWBKFfBkpd6MebLNGfjMVYmTH90a7nyd9eaZfK8uazD/9yTOblXYVmtX2y7XVtb0RQd9zfVOVMYJv4eQ/gfAemAn8H1uim/zzyTP7yXK+T0GmPZPKXqEqdH+5uYxsrurUeWdr2k5JWAj4CvF3SBNsN4b0lcG/8/DDBa/JaScsAqxOy3CRqQN1uvkR/kHTavadbO+1fR532y8CnbT8t6WeSVieohx4iWI5AiGR1mqQ7CSaCX7D9VJf9J0aJ9KaSmBsYdOsRbM9x59repqDuY8D7uukvkRgLUv7P9qjzbzKrBql9k0dkoq/ph4BRdRZCY0Gd38rqYD3SUmhLOgX4EPBkTCuGpFcCvwJWBh4Eto+qEQHHE8z/XiR4Q94S28wE7oyHfdj2lr29lMRIMlY3XgoYVT/q/Hv3/zy7mvXIacBmTWWHELLSTACujPsAHwAmxG1v4MRMm5cauSNHUmBXHTDJGiKRGDnqmjBjVhvbWNFSaNu+hmDOl2Ur4PT4+XTgw5nyn0ezvuuBxSUt26uTrULVQVLn2UAi0c/U2eRvhlx5Gys61WkvY/txANuPS1o6lhel13kcGC9pMsF1/Rjbvyk6+Fh4RHYr7Ad9sSolQUh0Qt1M/uqgHun1QmRZep2VYrS/1wJ/kHSn7b/lHSSbxqcTj8hO6HZg1WlgdkLSaSd6TT8uWNZhIbJTj8gnGmqP+P+TsbwwvU40+SNmJr4aeHOHfScSiZpQVRD3g8CGYPJXdRsrOhXak4Dd4ufdgAsz5bsq8Dbg2ag+WULSAgCSliTEIGlOI59IJAaMVmrDxtYvDIQbu6SzgI2BJSVNI6QXOwY4JwaMepiQyBdChobNgakEk789YvnrgZ9ImkV4UBxju6+EdtJpJxKJOqhHuoryNxp0G+WvjCRM60G3i5FpIXL0qfqb9/Jv04sof59ZecfK8ua7D549JpFKBs4jMgnswSMtEteLlNh3ZGmp05Z0iqQnJU3JlL1S0hWS7o//LxHLD5Z0W9ymSJoZ664o6SpJ90i6K+aWTCQSA07dnGvcxr+xoqcekbaPa3g9AocCf7TdSCv2WduvB94GfFrSmj26hsQokPVwq9uNmEhUZW70iMyyE3BWPMbjjRgktp8D7iE43SQSiQGm39QfraiDyV+vPSIBkLQQYXa+b3NDSSsTbLRvKDp4yhHZf9Tt5kv0B8kjsveM1ELkFsCfo2pkNpIWAX4NHFiWG3IsPCIT5fSj91oi0Wtm1EBs99ojssGORNVIA0nzEQT2GbbP77DflqQofyND1hEiCexEGXUeH3VYiOx0pt3wiDyG4R6RSFoMeBewS6ZMwMnAPba/0/HZViCZ/I0MaaadqEqd7eLrYPLXa49IgK2By22/kCnbAPgYcKek22LZF21f3P0lJEaDut18if6gfjrt/lePtBTatncq+OrdBfVPI5gJZsv+RH4EwERNSDPtxNzAQMy0E4lEolPq9nCf2edhPaBzj8jtomfjLEkTM+XrZTwib5e0dea7A6KX5F2SDuz9pSRGkrQQmeiEui34D4qd9mnAD4GfZ8qmAB8BftJUdwom29KgAAAgAElEQVQw0faMaFVyu6TfAmsAewHrAdOBSyX9zvb9XZ5/YpRI6pHE3EAddNodeUTavsf2fTl1X7Q9I+6OZ8hW/fXA9Znv/0hYsOw5yeRvZEgz7URV6jw+BsKNvV0krS/pLuBOYJ8opKcAG0l6VfSW3JzhGW6aj7G3pMmSJs+a9UJRtVySyV8iMbbU2+Sv/9UjPRfatm+w/QbgrcChksbbvgf4JnAFcClwOyGIVNExTrI90fbE5MKeSNSXur3R9tq5RtJmku6TNFXSITnfHyTpbkl3SLpS0mtaHbPnQrtBFNQvAGvF/ZNtr2t7I4K6JemzE4lEXzHTrry1QtI44ATgA8CawE450U1vJawDrg2cBxzb6rg9NfmTtArwSFyIfA2wOvBg/G5p209KWomwiPn2XvadGFnSQmSiE+o2Tnqs9lgPmBqTmSPpbEKE1NmpFm1flal/PRlP8iI69Yj8N/ADYCngd5Jus/1+4J3AIZJeJujq/8/2U/FQv5b0KuBl4NO2n27VdyKRqB91E9RZ2llgzEYjjZwUg901WB54JLM/DVi/5JB7Ape06rcbj8gLcur+AvhFwXH6+i+ZEvuWM1bX0G1+yOZjDMLfot+p81tZOyZ/2WikBeR5ged2IGkXYCIhblMpA+cRueByG1a60ZtjIqQ8hK0ZC+HXi37mhr9NP1H1HuxHeqwemcZwK7kVgMeaK0l6D3AY8C7b/2t10E49Io+TdG9c8bxA0uKZ7w6NK6X3SXp/pnxxSefFdvdIGhGddt5TPs+2ON3I7VHXmzAxNtTVpt925a0CNwETJK0iaX5CyOpJ2QqS3kxwUtzSdnOI61w69Yi8Ajg0Ljh+k5AP8gtxZXRH4A3AcsDvJa1meyZwPHCp7W3jBSxU5QS7JQmb3lC3my8xdtRZPTKzhzPtKB/3BS4DxgGn2L5L0pHAZNuTgOOARYBzQwRrHra9Zdlxq+i0r4kpwrJll2d2rwe2jZ+3As6OU/y/S5oKrBedbTYCdo/tpxPc2Uec7IBJArxz6nwjJvqTfhxTvXaaieGnL24qOzzz+T3tHrMXOu2PA7+Kn5cnCPEG02LZS8A/gVMlrQPcDBzQFHN7Nr3MEZkEdSIxdpQJ4n4Q0s1UVHuMKV0JbUmHETwbz2gU5VRz7GddYD/bN0g6HjgE+HLeccciR2QV4V42yAbdeiSR6IS6JUEYS/f0qnQstCXtBnwIeLeHHk9Fq6XTgGm2GxnYzyMI7b4hWY+UM+jXl0jAgET5y0PSZsAXCCueL2a+mgTsKGmB6B05AbjR9j+ARyStHuu9m4xXUC/JsxLJW8VOapP2eemxa2dviUQR7Tzg+21M9dKNfaTo1CPyUGAB4Iq44nm97X3iyug5BIE8g+D5ODMeaj/gjGg58gCwR68vBuYUxkWDIc0c2yf9ZokqtGPP329jaiDUIwUekSeX1D8aODqn/DaCx0+ihvTjSn+i/0k67d4zcB6RzSSTv95QpxsvUQ/6cSIwENYjkk4hLDg+aXutWHYcsAXB1vpvwB62n5G0M3BwpvnawLq2b5N0KbBs7PNahqtORowkqHtDP95gif6nbiZ/dZhpV1mIPA3YrKnsCmCtGAP2rwQdN7bPsP0m228CPgY8GNUiANvbXocQX3spYLsenH9L6upO22+kdGOJTqjbpKnXSRBGgk5zRF6eyQV5PcG0r5mdgLMybf4TP84LzE9BtKtEIpEYK2Z6VuVtrOhF5pqPkx8DdgcyQhtA0mXAk8BzBFvtXLrJEZln2pdnUlS3GUA/0G/mWYn+pM5vYj0OGDUi9NojslG+PvCi7SnZctvvlzQ+1t+UoGaZg248IlNi35Ej/WaJKlQ1+evHOOd10Gn32iOywY40zbIb2P6vpEmE4FK5QruXJOuRRGLsKDP56xdBnaUOHpEdCe2MR+S7mjwikTQPYZFxo0zZIsCith+XNC+wOcGCZMRJgjqR6E/60SJp1oCY/FX2iIxNNiLEGXkgc5iFgUmSFiDElf0D8ONeXUQikehP6mbyNxAz7Q48Iq8G3tZU9gTw1nZPLpFI1Ju6eUSOpVVIVQbeIzKRSCSqUgf1SCWTv4I8kUfFHJG3Sbpc0nJNbd4qaaakbTNlu0m6P2679e4yhkhR/hKJsaVOM+tmBsK5JnIac3pFHmd77ej9eBEwO4WOpHHANwm50RplryTow9cH1gOOkLRE56eeTzL5GxmyNtrpgZcoox9N+aoyy668jRWVhHaBV+R/MrsLM9zDcT/g1wRHmgbvB66w/W/bTxPM/ZofBIlEYoCo2wO+DjPtbp1rjgZ2BZ4FNollywNbE5xnsouPywOPZPYb+SPzjptyRPYZdZsxJRKdMHPkY9h1TVdu7LYPs70iwcNx31j8PeALORH8ivJH5h33JNsTbU/sRmAnEomxpW4P+4F3Y89wJvA7gs56InB2tN9eEthc0gzCzHrjTJsVgKt71H9ihOlHR4hE/1M3k79Bd2OfYPv+uLslcC+A7VUydU4DLrL9m7gQ+fXM4uP7iCFdE/1PnW68RKJT6pAEoarJ31nAdcDqkqZJ2hM4RtIUSXcQBPABZcew/W/gKOCmuB0Zy3pKVeGSdN2JxMhQ5wd8HaxH1O9PlhTlrz9I6pFEvzNj+qN562Zt8erFX19Z3vzjmXu67q8TBt4jMkX56w1JSCfmBgbCjT0vR2Tmu88BxwFL2X5KYfXxeEIUvxeB3W3fImkT4LuZpmsAO9r+TY+uo5AkqBOJ0aXOb2X9rnmAznNEImlF4L3Aw5niDwAT4rY3cCKA7asyuSM3JQj0y7s680Qi0ffUSWBDPXTaVaL8XSNp5Zyvvgt8HrgwU7YV8POYFOF6SYtLWtb245k62wKXNMfhHimqqkeqzMirZuFop11dqPPsKTF2lJn89aO7ex1m2p0mQdgSeNT27dEeu0GR12NWaO8IfKfF8UfdI7LbQdMvg26kGPTrS/SOqmOlH8dUHey02/aIlLQQcBiZAFHZr3PKZv8KkpYF3kgmkFQe3XhEJpO/RGLsqWtwsUH1iFwVWAVozLJXAG6RtB5hZr1ipu4KwGOZ/e2BC2y/3NnptiaZ/CUSY09d76+BsB5pxvadwNKNfUkPAhOj9cgkYF9JZxNCsD7bpM/eieQFmUgMNHVe/6hDEoSOckTaLko3djHB3G8qwUJkj8xxVibMwv/Y1RknEomBoB+F+0AsRBbkiMx+v3Lms4FPF9R7kIJQrIlEYjCZ2xP7StqM4LsyDviZ7WOavl8A+DnwFuBfwA5RVhYy8B6Rid7Qj7OiRH9S57HRy5l2zOB1AsGfZRpwk6RJtu/OVNsTeNr26yTtSMj4tUPZcZPQTiQSPaUf7a+r0mOd9nrAVNsPAMS1vq2ArNDeCvhK/Hwe8ENJctnTox0Tl7HagL1Ho81o9pWuKf0O6ZrGvq9uNoIvyeTMtnfT99sSVCKN/Y8BP2yqMwVYIbP/N2DJsn67ylwziuw9Sm1Gs690TaPbZjT7StfUeZvR7qtjnPEnidtJTVWqZOuqnNGrQV2EdiKRSNSNVn4rw+pImhdYjKYk6s0koZ1IJBIjw03ABEmrSJqfEMJjUlOdScBu8fO2wB8c9SRF1GUhsvm1Y6TajGZf6ZpGt81o9pWuqfM2o93XiGF7hqR9CWE7xgGn2L5L0pHAZNuTgJOBX0iaSphh79jquH2fuSaRSCQSQyT1SCKRSNSIJLQTiUSiRiShnUgkEjUiCW1A0goxjyWSFpDUedaFRGIuRdJarWsluqXvFiIlfaTse9vn57Q5FnjA9o+byj8DvNr2F0r6+ziwL7CY7VUlrQb8yPZ7OrqAOY9/mu3d4+fdbJ/eZvsNCG6uryFY+4gQm+u1JW3mAe5wUyLmkvrrln1v+5acNq8DlrH956byDYHHbP8tp83bbF9f5ZwybdoeD5m2CxO8yx5qKn+D7bty6h/Uoq/SjEvtEsfaiYTfcS1JawNb2v5ai3YLAZ8FVrK9l6QJwOq2L2qj70VsP9+izvdzip8lWD5cmFP/T8D8hLyyZ9p+psJ5tD32Mm3fCUywfaqkpYBFbP+9VZ91px+F9qnx49LAO4A/xP1NgKttz3ETS7obWMseHsG8ivCSdBshRsANtt8cy+6wvXZJm7cBPwBeTxik44AXbL8ip+6tmePeYrt0kOa0vxf4DHAzMLNRbvtfLdqdARxq++GyerHuVSVf2/amOW0uAr5o+46m8omE8L1b5LSZff2SrrP99grn1vZ4iO22AX5IiJxmYLeGACj6O0g6In5cHXgrQza1WwDX2P5ETpvnKPFgyxsTmbZ/BA4GfpIZI1NaPWwl/YowHnaNwn5B4DqHxNmVkPSw7ZVa1DkJWAM4NxZtA9xFcAZ5wPaBOW0mAB8HtgNuBE61fUVJH42xNx6YCNxOmJisTbgn31nQ7ohYf3Xbq0laDjjX9gZl1zQI9J2dtu09YLZQWNMxiUJMVXZCcbM5U07YnqWmJJY5/Nf29Ea1GJmrVZsfEuwpzyUMnF2B1xWdW4tjteJZ25d00G5Z4C5JNwIvzD4Ze8vmirY36eD4KzcL7HisyQWJoGH47zq+SicdjgeALxOSczwq6R3AWZIOjraxuX9f21+Nx74cWNf2c3H/KwwJruY2i8Y6RwL/AH4Rj78zsGiLy1vI9o1NQ3RGizYAq9reQdJO8RxeyhvnJW8OAhap0M/rgE1tz4jHOxG4nBC17s68Brbvl/QlQiyO7wNvjuf2xby3osbYi8GU9nZIstJQtXyu5Ny2Bt4M3BKP85ikVr/3QNB3QjvDyh6e9eYJYLWCui9KmmD7/mxhfOq/1KKfP0v6PDA+6rU/DbR8zbQ9VdI42zOBUyX9paDqCvE1U5nP2ePsn9co89p4laTjgPOB/2XaFb42Rr7a6hoK+l0LWJOMULX985yqZUJ3wYLyeSQtQVhLaXyeLWxsl7nvtjMeAOax/Wg87l8kbQpcJGlFWj9IVwKmZ/anAyu3aPN+2+tn9k+UdANwbEmbpySt2jgfSdsyPAl2EdPj7LrRblUyYyPD14HjyH8QVFnPWh5YmKASIX5ezvZMSXP0F9U7ewAfBK4AtrB9S5wFX0cYw0Ws0RDYALanSCp7c5hu25Iav8Fcsw7Vz0L7akmXAWcRBueOQNFr/OHAJZK+RnhthDADPhSY4xWuic8Tgs3cCxxA8F76SYs2L0a31NuiPv1xwoDO4+DM58ktjpvl2037EzOfDcyhsshiu+0MQfGVc2OC0L4Y+ADwJ0KQ9mZukrSX7Z82HWNPhv4GzSwWv2sI6uyDx0Chnp72xgPAC5JWaeg444x7Y+BCwvWV8QvgRkkXxL62Jv83yDJT0s7A2bHNTmTUWQV8muDJt4akR4G/A7u0aANwBHApsGJUg20A7J5T7xbgN7bn+HtImkPVk8OxhDF+NeFvthHw9Sggf59T/4fATwmz6tmTpTgL/lKLvu6R9DPgl4TfbxfgnpL650j6CbC4pL0IKpmfltQfGPpOp50lLkI1AvJeY/uCkrprEQRkQx94F3Bc9uld0nY+YAJhsNzfeB0sqf8awkxvfoK+eTHC4uXUVn3F9ksAz7SKMRDrvtYxHm9ZWea7PYFX2j4u7k8DXkG46T5v+8SSvu4E1gFutb2OpGUIoSXz9NPLABcQZqHZB+X8wNa2/9Hq2tqlzfGwLvBcztvX/MBOrRaEJb0FaOhTr7F9a4v6KxMylGxAGEd/Bg50iywkse3ChDeD51rVzbR5FfA2wt/1ettP5dRZHfhXwXfL2H6iQj/LEtZ8BNxouzngUXP9BQkLpPdVu5LZ7cYDnyI8GACuAU60/d+SNu8F3hfP7bIy3fkg0ddCuxMkvQLA9n8q1t+MMNt5mKjCAPayfXmLdpUGp6TDgXNs36uQWugS4E2EV9aP2s6bsWTbz7FoJulm228pqH8TsFljoVJxITTeFJfb3iivXax7o+31JN1MWOh7Dphi+w0lbTYh86C0/YeSuq8hPKyezbT9MPAgcILt6UVtu0HSCgQrg6vi32Be2y+0aDMOWIbM22iVRd2K59ORlYq6sLRoB0lrxPGa219RP5K2AL4FzG97lajeODJvHaUH57gK8HhDqMf7cZkqD8m603fqERWvxjdM3XJX4yUdSFi4WDDs6ingcNtnS1rR9iMFXX4PeI/tv8bjrEZ4hX59yTnOHpxAq8G5A3BU/LxbvI6lCPrY08l/zUTSGsAbgMU03OztFZTrk+fxcMuScwFs/zcO7DImS1qc8Jp5M/A8wQKgjOzCbeGsKHIOQdXwbPzNzgW+QXiI/QgofGWPv8E3CVYkosV4yLSbbdIJrEownfwRUGjSKWk/ggriCYKKQ4QxWWZRtBSwF0H3nRX0H8+p3umCWbPKLEuhyiyO6YMZMhttnFuRiu0ggsowr78y1dxXCLPyq+Pxb1PxonTj3O6k3Pqm6Dc/l2BN1GBmLHtrWX+DQN8J7cZqfDsorO6vB2zkodQ+rwWOj7O7vSi27niyIbBj/3+V9M8WXTb6uzq2KRuc0zNqkPcDZ8fFy3sU4ucWsTrwIWBxgslZg+cI11PEYtkd218HGuaPrypph+3/ix9/LOlS4BV5FiLxeCsSHm7PMaSn3kbSS4QUSh+z/bOmZgtmXq93IUQ9+3Y8t9vKzo2gX93CdpmeM4/9iSadMPvvu0yLNgcQTMlKzSqbuBC4lvAQLtVlZ6xUXtli8bW5XSdWPhCE2Y8JD+NWenYIi4gAexap4QqYYftZtTTYGsaH2qmcYd7sm5mDBdj8HR6rVvSd0O6QnYE3ZvVfth+QtD3wT+CjJW2nSJpEmAWaaF8qact4nOb4t9De4Pxf1Lc/QVA5ZM2YFipq5OC8cKGkt9u+rkpHkcslfc1288LPkQRzrVIkLU9mRiZpI9vX5FQ9Afi+7dOa2u9KsBQAaBba2R9sU8JCccM0s9WpPdGBwIZ8k85WPMKQxURVFnKJE1cBNyj4CZwKXFJljQNA0mTgFOAs209XaDKjbC0jh0MJgv48oB2/gimSPgqMU7Dc2h8osqoCwBnHp/gwbcyUb7T9ZEnTf0rasnF/StoKmEN3P4gMhE5b0n22V2/3u/j9L0oObdu75rQ5GbgSOITgcLA/MJ/tfXLqrk9QgywFfM/2UbF8c8JsdKeS/hsLNHsSVCVZM7y81+7GotbPCIP/9li8DsFy5RMu8YKT1MgEfTdDMzLnqX0k/dV2rsldXPxct/mmk3Q8wX78H4S3h9VsvxwXu35re+KcRxvW9tXAbxhu+lhmRoakbxMemHsA/0ew2Ljf9qElbU4mvOn8rqmvQo9IBculv9i+uOx8mtqIoKb5OOFt4FfAadk3v4J2r4vXswPh73oqYb0i92aOb6JPEhaOs9eTO8uXdAXhof0mwtvDMIp01AqemoeRWRwEjipbTMy03Z5gnnh1bLshcLDt8wrqrwqcASwX6z9CcDaqZAxQZwZFaF8JfN32lU3lmwJfKtHdIWlxV3C3bWqTHZwQBufXWqx0j2/+vsrrsaRzCeaIHyXMlncG7rF9QIt2ryUIeoC7neNWntPmPmBt23k2v811p9qeQ+UUVR332Z6Q850IgubVBO+1R2P5m4GlbV9W0t+pOcUuenhl2o0j6GezguQnznHGyrQ5Iq+8odYoaPMcwexzOvBy5vxKde6Z9psQzN0WJjxsD2n1hhV/6w8RXOFnEWbfxzePKUl5rt12QSiEqGZYl2D6OMc6gzswJ22FpNuB9zYe9HGN4Pe212nRbhGCHKtseVN3BkVov4GgU/wTQb9qwkxzA0Ish7tL2v6NIXfbKuqDccAxtg9uVbep3e+ArTzkXbYscJELrEAy7RrWH3fYXlvBPPGysgdRbHchYeZ2oVtYSmTaXAJsVzYbz9T9LsGr7sDG8eMs/7vAS0UPlfj7XeYexXapgto06RwtFMz2diFk6X6CkMVkEmGGe67tVUraNhxZNic8iM4gmCh+zG24s7c4v6Vst1rfQdJvKV9MbGk9IulO22/M7M8D3J4ti+W72P6lCixwyt6GBoWB0Gk7pPBZizAbfQNhRnUN8MkKr2YTCAuEe0k6geC8cXrRzNTBG6xU0BbwG+A8hZgYKxJuzjI33QaNWdsz8Rr/QWvvPIDvEGa131BwZf8V4SFR9nu8SHCmuJLhr9F5XpufJ1h+PCSpoZdciaAK+mJRB/H3e1HSYo6mf1VQMNv7AUN20H8CDrA9rUW7OUw6FZyC5nhAxwfKJwhmn5fY/kvmuy+5dSCnLRmyM77arQM4XUeYzX646TomS/pxQRsUTDKfIQj5QzJvRjcoBBhrrj8fw22grya8bbzcXLeJJSQdzZwWMc0Thm+1OE4VLtWQ8xSEsZunamo4sc0VLut5DMRMu1coeMydQTCru5EQcGkOk7eoJ51AWKzJxvVopV/9NLAZ4Sb4ZFYolLT5BPBrgrnZqYTZ7Zdtt/LabLQfR1j024tgv10WwGi3vHKXOKIomBG+jiAQp9p+scI5nUNwDLmC4b9frkt/bHMFcCZByEGYoe5s+70t+rqX8LY1zKTT9hwmnQoeeQsR/vYfA/5o+6D4XWmwL0nHEN7uzohFOwE32z6kpM32ts9pKtvOdm6ck0ydPIer2d6fBdc1H+GBSry2mc4JgNXU7naC1UlzsLIij9euUDDrfCdx0uUC56k4pve3/d2ROI9+ZyCEtjq07Y5tFyfoiXcFniboBS8A3kJYnZ/jFbUd/WrTa5wIN8ydwK2x0Yi9zkWBugVh1rIuYaa9X4s28zMU0+O+VrMx5YdOfRa4s3khMtOmk4fDbc2v/XllOe2ucZNDUV5ZLJ8d3VHBHPNHwJIEAXy9YyS+gn7uAN7U0JVHwXKry6NF5jlOtYwEWdCuzOHq9mbdcF5ZTrvCYzbVK7K1FjCrVT9Nx1qSsAj5cNnDQdJV7twEstYMinqkm1elmwgzuO09PO7y9ZJyYxk4Rp7LIqnIqL/53C4oKJ8DSe8CnrZ9R1xd3wiYSnDvLV0sVAjfuT4hRsUJhNf1wsW32GZjwmzsQcINt6JCDPA8k78GewJvZygOyMbA9cBqko60PYd1TplwLuEpSbsw9Pq8EyHsaivaMemcbecb9d57K3i0/oFqUfEWJ2TUhiZ7+SySPkDQRS+v4QHEXkFJlD917nA1U9KqDZVfXKSuYq/9W0n/R2urkzxb64Z3caGqLJ7LRQQVz5S4znMLwSLmtZJ+avt7BU3/IumHBLVf9m2tJ16h/cxAzLSzSFqH4fEpipxDvm77i5LmaSXMSvpakxC4aCdCCNVCk7UOjn0CQSUyHriPIDQuJXiBjbO9c4v2mwFXODjyVO3zZoJr/X1xfzXC20bhbCsuQn3CMY6Fgq3tiQTd8DXOxIYumZEBpd5vSFqJEJDo7fEYfyHotB8qahPbVTbplPRL4Je2L206xicID8r5SvrZCTiG8PBqBFc61PbZOXXXISw2HkkIdtbgOeAqF9heK9gifxjYkqFY3412Zxep2yS9m6BaeyCe22uAPWyXBdxq2+oktnkTYW1pe0IArF/b/mFJ/bscwyRI+iIh2t+uCmFW/1w0JpQfA945+vbBw/bAbARPtimEm+FIghpiv4K6t3TYx2sI9tm3E3R9TxHChrZqdwWweGZ/CYIVRVH9u+P/4wkzynFxXwTVQ6v+xhPckc8n6MQ/A4xv0eaOKmVN39/ZtC9CvBII6oHm365wG6ExsXib9ecB3tFhX8sSBOpWhIxJrerP1/ifEBt66Yr9vL2Dc1uAMAlYB1igx7/xaoSHzz2EBeL9gIcqtr0t8/lKYMe879I2tA2EeiTDnsD6HjJB+yZhhf4HOXXHqSmecxbn2E8rxMxejBB+c1uHgO9/d7UgNUs5Yw9u+2lJS5fU/2+s919JDznOmG1bUqtVfwihRJ9j6Np3IizibVfSZrKCY0ljdrozxWFWG1wbX3Gz2U2uUTD/a7Z/X9btpxvrOJVc5OZoPXOqK5h0Onhnfpswo69yfu8HFrV9nkO874aH3s6SnnRO5LloGfIDB6unxQhjdCbwSkmfs31Wc5vYbi+Cmus6SSJYj2wDPATs7ibVgKRNbf8hZ91hVUm49cJ5VauTewlOOFs4OrfEv08VHlGI9TKNsO5yaWy/IOFh1nxO6xOsgVYlTMo+7s48ZevLWD81erkR/ojjM/vjKZiVEnR0DxBe4Zq3BwraXEgwHfshcTZWVDen7c2EqICN/ddQMtsnDOKDCLkAG58b+49U6O/2KmVN3y/A0Oz8AsLsvHRWRnjobUuwz/5e/KyCurdkPl9X8Xe7mxAEq7l8HuKMvkX7eQhxwc8F7ie8ga3aos1XCcIw9zqa6l5PeCA3l7+66BoJ0RAbnw8kxLxutLm1pK8pDM3OPxrH1KsIXpXX5l1H/P/UnO2UCtf2M8Iax6ZxO5UQqre53tYE3fIjhPgm7wb+XvHvuzTBQuVC4H2Z8k2Az+XUn0zInLMAYQJS+LY6qNuYn0BPLyYInNsJAZ2+SghCdGBB3cKbo0UfixHcjq8gCPingfUqtNuMIPB/EbeHCNlOiuofUbZV6O804G2Z/fUJMb/H8u9za97nFm3u6uS7gvobA48S3kCuLPq7xe9nEWzk/xP3/1NQt1B9VPRd0+/wO8IsueXvwnBVwpkEnX5jv2wCsEqVspw6bT34CTbUOxMyP71IWNt4X6t+mo6xKCFBb9H3t5Ttzw3bmJ9Azy8ovGLtH7c3l9TrSGg3HWNpgv7uL1Sb/S5JWGnfgpAlfCSu/07gDoJ+cRbBEuTv8XPuzJQQ7zvbdtjWor+PEGawz1YQcLcTdPmvynx+ZWMraHMTIRZ2c/kEQlbwVr/H4oR4IzcQXr23J7x2v42Ks8EWx/8rIeJcc/l8BO/LvDZXxXGwLkGF9OpYPi9wb0lftxD05uMJHpRvyHx3T1m7nLKbK1zbLWTeSgiZhSoJyfg3/STwh4r11yKYwZ6FmKkAABZ6SURBVD5EmNzcnL2+TL0H4phrbMP2u/171mEbNJ02BN2g41ZmFXJ8dkfSwq7o7p3hBds/AH6gEAK2kKiD3Ax4re0jJa0kaT3nOO/E+t/PK2/gYkeUTkJdNlzOO2nbTsjUTtKNdZNKDjow6YS2vBvPB34qaV8Pd+f/PsU5ET8Zv3814U2wkeXn3YSZdxGHE9QD44BJtu+K/b2LILyar6FTE8EGBxNylA6zOqnQDoc1oZ/QOnVfg5OAgxwtWqL56U8ZHjMb4I8MD1Wc3TfleSgHgoEy+ZN0AMHz79eEQbY1cFIUrEVt3kHQ3S1ie6VojvVJD8WW7lWbRlCfTW2/Pi6CXm471767yAGlgduwdY5C5MMEc74PltT7ppsW9vLKmr7/s+05XKd7ieZMJTcF+JZLUsl1Y9KpNrwboxPO1wgmjll3/pMJnqtzLBo3flPleERWOLd5CQufT2fKFibcy8831e3IRLDpGAsQIh6K8BbQMphYJ3TqADRXMtZT/V5uhNf5hTP7C9P69f4GQiyQrJ6xdIGrwza3xP+zbUoXBrv8LeYn3LDnENQWpxJmxC3Psfk3bdHmeMIi1E60eE0lqAMKtxb9bFelrOxa2hxH82T2x5X9DoTFzg2BN8ZtwRbHv5OgPunU7HQh4MvAT+P+BOBDJfXbMhEkBrHKKd+L8OAfifF6QbymleP2JeICbUH9ZQgPxkvi/pqEpA09P7d+2wZNPSKGe3o1UkWVYvsRDQ/C39IhpYM2Lyu4NhtAIfRkWXjQjiKnKSQ73YkQBOsqwqLnes7x4sy0+RQh1vSqCu7YDRalRRB7wqv2iwyFqYXi19RG+qrxBBXH7YS/z9qEB+E7c9o0aATmb1XWoG2TziYqeTfGY82SdKztSmaCBN36U8DCkv4Tz9GN/906nOupBFVRo79phN9hmApH0udtHwt8NDr/NJ93kYrtswyphrL8ijCmzmxxfp3wcYLxwPkwO+BbmSrmNMLvcFjc/2s8v5NH4Nz6ikET2qcSIp01XMU/TOs/4iNR3WGFuBv7Exbxet3m+4TZxNIKkdO2Jcwmiug0ctplBJvZdzoGEFJIIFDGmYSEw98gOA41eK6VcCt7GOTU3SSez9nA3o7qjaj+yI14qA5dvoE1GK4/H3Yq5OvPG3wDuDV63c32biypDyFj0DbA+Y5TvyIcwvoeLOlC21u1OG4eq9reoSGIbb8k5ab+aYzJyW0ef5xz4lPb/k+03e45DuqewoBhOSxp+xxJjexHMyRV9v6tMwMltG1/R9LVDEUK28P2rS2a7UN4xV+eMGO5nGBt0NM2ts+IbuLvjuf2YZcs3rnzQPNvIbjW/z4uIJ1NeL0vO7dnCcl2jwf+3bhhJS0qaX3bNzS3acziJP2AnDeCklkcBFflOzN1p0T35zweIwidLRnu6PMcwY68iLtdEuCpDNtnxXH0VsLf6gseWiws4iCCOm6mQp7MlrNm21tpeIqtG1whfjUwPTqfNN7aViUTGyRz/N/G/9uN9TJf3sJ8dC3vaR5GhbgwhRS9UQIvKMQjb/wGb6P9FHG1ZCAWIhVScu1DCBF6J3Cy+yTQfQNJbyTM/iCYZ02p2O7v5AvFsplio+0GBFXJNgSb9Qtsn1RS/1aCbrlxI8xDMKubI+qcpC1s/7ZowbRMUEg6ixDk55eEa9uFsKhbmHpN0nxuHf952LW0K7QlrWH7Xkm5Ufbc42BEkrYjvFFdDa1TbGXavZfwlrYmYcKwAcHW++qmep2q2D5HmFx8ytHbVyFxdSPw2HGtrq0qCkm0HyEEAruBpjejoslL/Bv9gLA4PYWQzm9bF8QaGiQGRWj/iuAIcS3B++1B21XMwYpM654lCKsLu22j4KZ8IWHh8g7CoHwjwRZ1K9v/aXF+2Qzq4wleYK+0fXhBk7xjzEPwmtvRJem5lB/+dHa40oI2c8R/zitr+n48w92jryEEZCpL1/Yh4CiGkg6XzmQl7e6mpMOtkHSS7b3VQTCiqJ7YmeC0cpRCtvplXWDSGdt0lGIr1n0VwdZchLCxcyS1jaaAEBaHX014SEJ4kD9ouzACn6R9CCqhRQiC/wVCxqZ2EgS3JK7zNNZh1iaYPJ7laM7You28DFm2tAwjPCgMitCenaoo/iFvzJsdFrQ9iTADzsbOuIsgZB/IE/7ttIkCfjrweQ+PtfwNgpVBaXzrgnP+k+2yRbtGvbWZM+tIoR2rpPMJs77Gjfl/wCa2P1zSpqOY0O0iaSpB+NzZSmfc1G41grlgQ9gDudlXGvXnIVhb/LnN82vLpDO2qZRiK6fdBgTvyBcUwtWuS8gNmRvxUG3EFG+q81pCQuDZORhVkmyhW6J54U6EBL9HOsdUV/nx22dTNr4HhUHRac9+wsYFiXbavo5wozVyN55IeOV8L0HV0m2b9xCS5c62FHFIufXFkuPPpulVfR6C1UWVWNynEGYudzFkpdLK+WAfwoLpl2LdKwlJcfOO3+kCYUPofIU5BWmZyucRglllu7OMcwmxLX5KNaugWZK+RcWAURnWt71uVDHhEBCslf63aoqtZk4E1lHwDziYkLjj58C7CuovpUy2G0mrENQJrTgv5+F7HmHdpGdEYf1BgsBemXLHpC0KymEuca4ZFKG9joLpFIRXpQWVMaUqWwwiLCYuzNAixsLAclGwFjkStNNmep5+PT5cqjgqfDvzeQbBJX37Cu3eZnvNCvWy5/QkYRGzCp0uEEKw6PkMTWmsWvB54GJJf2R4QP5WmX9mdPBKX9kSJENbJp0QrEg0PMXWSS5IsdXEDNtWcJ75vu2Ti9YWIp8Bro4L0xDT3RVVVveelJWRdDpBL30JIcBV6VqP27BWGlQGQmjbLrWOaMGxhGS2VzNk3vV1BS+z3/egzXhJb2ZO0zMRIpWV4s5TKl0naU2XZKJvJgqavZhTpTKHHtz27cDtks7sQJf4rO1L2mxzNPA8QWi0Y8FQNftKlrYtQcg36fxyhfP7M+FN0YTclFV4TsHUbRdgo/iwKDTFs32ppAkMLYS38mxcnRDSYHGGz2yfI4yPXvIxgr58NWD/zFtylVSBHyQ8XGY/SGwf2ePz6zsGQqfdLQppjtYjDJQbbT/WqzZRsJet4OcKZUmn2d49ft6tzBqjoP1GwG8J2dv/x9BNULao+BfCYm5zItdfl7SZQNDPr8nwm6csu8kxBDPE8xkuSAutMyRNdgeZgdRB9pVOiTPUhknnlW4Rj0UhhdxxtG898mpCaNabbF+rkNVnY9s/L2nzDuZ8GBfWj23ebvu6sjpjhUJM8oUIIVx/RnhI3mh7zzE9sVEgCW0gLhpNYLjQKcuL2FGbNs9ptslaJwt7ceHuIILePKtPL0zPlWc9UqGfPxHCxX6XMCvbgzCujihp04l1xjGEiHEtExl0S4eWIL+w/bFWZU3fd2w90g4KKddWJZh9Nh7GdrktPZJWIJjVbUCYePyJEA52Wi/PrxMUrZoy/y9CUGe9r2XjmjMQ6pFuUMj/dwAhCeltBDOq6whB37tu08Vqd7dP04c9PGltFS6StLntKothDRa0faUkxQfCVyRdSxDkcxBno18jOJI8nyn/QIt+Pg18Pq4DvExrk7+irC1ASyuDHxEtQQhmhs8TbJQLLUEIr+nZ/sfResFuHg/PWP8vwmJzKZKeY2h8zE9QjTxvu8jdfiKwZgeLuKcSvGUb2Y52iWXvbfM4I8FL8f8XJS1HCDmwyhiez6gx1wttgvB9K8HWdZMoVL7awzYNneDShDCTf4j7mxBei4uExwrRKkOZz7NpNUsC7pV0JkFFklVBlAmrA4AvVhWMkf9GU7X7Je1LSDKQm0ZN0v4E4XsPcLKkAzxk1340YTEqF9stLWaaeBfht86zNmhlZVDZEiTqlr/I0OI3hN9tOsFipYw865GWuv7m30L6//bOPsaOsgrjz+PWAgXaalpExaYStEJigtImSiNBiR8kktAgtIgSpTb+oRHR0D/UtJhGS5AADTQESmxNo42IQKImTYuVgouVVvlIUaJNqhWFpmixSNWmzeMf5x139u6dj3fu3K+555fc7J3deWfm7u597zvnPOc5vBwWqstiL0yn/WLRsVs4Q9LG1PYmkqXqH3rAT0nOhuWXkkT4fX28np7hkzbwH1kfRpA8SVYRt6CuMUm2m9ZH8TxZH8EkJr4+5xw3pp7HekcAwCmwybqMkVNyrbETI2Ce1jNgvhFrYB9GWUqGFQAukPQvWoXdAyTnS1qHqYnaSbC9NvkOSQcyXsvq8LWK2qC0EkTSWgBrSa6VVORP0jq2qnqk9TgPk5xiG5tiDoDf0Xplpj/As0rEEw6F33XyoXI17G6gb5BcBGs4siZsnwYLAT4PC9E1Hp+0gRfCJ/bDALaTPAyTs9U9Zn4yYQcOwjLmbUkSj8yoOCw4V6XJKiQv2x0rM1YvaXcYqxLnHEtCIpL+RDO6f4DWQKJIXJ/WJq+EyQY3I0Ob3GEit4oSZF/L+ccAfENS7l1buPN5MBlD8hpJ388b0xLySbT7eaGPm/KOl8N1sH6ot4fjP4GSTRC6yD2w2ofk//VmWPeo82GNFD7Rv0vrDZ6ITEEr+50FYKukY3WOIXkXLHG5BfYGWAZgnwoqItslIcskJqskkWheFQknw265f1OQIHw/bAItbAhBcgesO8nTqe9NgxWHXKMc6WbymkmuAvBXmTY58/dQQyI3VgnyA5hEbjlsZftdADslTXEvJDkTFiZ6K6w5wfawfSPsbiLX+Y9kOmRxHNZSbkNLfLx1TNqY6sm8fQvO/WVJd1QZWwdMNUYguR7AIUk3he3oRPpQogEw9e7XAyU7enc6JjV2CWzVcjuAJQX7XgqbdA/CVn7JYxPsTVd0ru2wVdG08PgMgO2R1/s2mA9E3j6lG0LAErdnZvxsccF5dsK8MP4Ai8+OwUras/b/bbvnJV/35jLfa7PPUphP9oG81wPzotkEK3C5P/ytdgI4v5P/55zzXQXrqvM9WOXkfpi5UpVjHejGNUacfy9CT05YSOSiov+7pj1GOjwiK1l+huQ8ZcRG6xiT4gnYyqhMIUUnFYcAMFedJ5FewESLr0xUsiGEclb5Kvb6WArTJi+X9BJNm5znNtdJIjdaCULTq18Pa3V3LoBPh9X+0Ta7n60Jr5z7YBP9PLXxsM44V+xd1NcBLFKLtBBWkh5LlEdEF9gCYCfJl2EKkscBgOQ5GBFr1pGetANvBvBcSNL83z9Y+Uma6DGcWkhxJ8nMQgp1VnEIAC/HJpE42Rv7dbA44TMF56nSECIamZ/1beE658CSUXnFIdGJ3A6VID8B8AUF+SNMI78bLR8AgbRXzgmS+8tO2IFYKV4laWEGfY2nSvoWyZ/D3oPbFJbYsNcTbb42jIx8TJsT9pWTUE4TgopjKhVSMNKSNDVuHiyJlBgfjcNWY3nFNWnVx3GYfWfuCjhMoOtgySHCjLOul1SLyoBmbn8zTIe7BpZ8nAN7k14raWvB+CrWsdFKEJIz1WKzS/Idkv7YZt8TmPiwJ0zpcxTl/7btLHQz47kkvwMzD0tLC59VRsNmTtaBT/oRTJfvi71+0u/4zKg80BJ/hU06mTHZ1H77YG84dvHa5vX795NzbXtgssUrARyGGWEB5qPxVInx7ZoV58a40dIgFhY/X52x78rU8ytbfvbtLv1OHoGtrsfC41OwZGnrfucgxNZhtra3wfIpq2Aty/r+9/VH/MNX2raSuxMWh5wOexO8pnyjmipjolY7qXG/AHCJUtauZYiJe6bVFSR/LOmKEsdv22YsQcXFP6VIryBJ/l7SuamfZXan4YR17FWwhq8JM2F6+cxilEglSPp3N0mlUkW1UoaWu6hEijflLopWG/A1tXRzIbkQ9iGUZ3PqDCh+m2P//MtgvssLAVwLk+bVOkbVCymqWpLGxD3TyaWyRkrpOPE3kVG2XgPpD6t/t/wsb8VROZEr6ZMkl8KKNo4CuFrZYSJmPG+3XQuyBHhRYQxgtQFT2m9J2kMrbnKGEJ+0AUjaR3JM0gkAG2lud7WPQTUbzqqWpDHqEWU8z0SpYpWg3Y1tHluWxCs97ZOOsJ3p7awOErmRSpC8312tt7EV7m7yvK9PqeWinJ7jk7YZzkyHvcFvgfkznFr3mFj1SIo3qppzWYx6JG9iVF7YJ9C1GJs680oHgI+SjE3kxihBKn2oVCT27mY3yRWSJilfSC7H5LsPZ4jwmLaVUB+ErWJvgMU875a0r+YxVdUjlSxJy8Y966Bbsds6YIXekjFKkH6RF89P7fMmWDn+MUxM0gth/7dLZDJKZ8gY2ZU2rVXTWZLWh+2dMHc6wWxWp0zAVcakqKqVjbIkDdc1BuAKFRsCVaZFFjajwuq8V5TuLUlypaRbJB1pIwv8LEzDPSgUvh5JBwFcSPKDmCiS+pmkHTnDnAFnZFfaJMcBLJP0l7D9NMw/+TQAGyVdUseY1NhK6pGqkHxU0sXdOPYwQXOFWwMrE89N5PZDCVKVQbsep3eM7EobwPRk8g38UtY38B+0Xo+1jAnJv3HYKu0yRKpHGGlJmmKcZlL1Q0yu2sxs6dVQYhK5PVeCxDBEdzdOFxnlSfsN6Q1JX0xtzq1xzFmwisF3AXgWFlseh4VTyhBlSZriwvA13ehUyOnI01BiErk9U4JUQdX8zp2GMcqT9q8zMuufR7YcL3pMUpAR1CYLYZPpdQA2kHxF0nkF13lckkI8fZ3MkjSryUD6vFW7uDeNR0h+pGQit5dKEMepxCjHtM+ANTH4L4AkZHABgJMAXB6SOB2PSY2dBVNyLA5fZ8MUDbmm8iHZuRWWCLsIwCFYuOTdGft/Je94JYpyGkUIKZwK+5uVbaHmOAPLyE7aCSQ/hAn97XNlMusxY0jeG/Z9FeY9vQvWW/Jwyes7E2ZJulvS40HKd7EyHO5IJtrdBTDT+6S572UAHpP0uTLndRxnMBn5SbvbkNwK86/YC4tn/wolJWhtjjUHwN9Lyte2wWR/r4bt0wH8SNLHYs87zHSQyHWcgaSqp65TkjBJLgJwa/jWV2GVattIZvYPJPk+ko+SfJDke0juhU38B0mWmXjnwYoqEo4BmF/pRQw3d8MqWJNE7p9hiVzHGUpGORHZM8LKeC/JV2DdNf4J4OOwHoxZpch3wWSCswDsAHCppF203oVbYHHuPDYDeJLkQzDlwxJYq6lRo1Ii13EGFQ+PdBmSX4IpRhbDEmGJ3G8clohsa7nKipakLcd4L4APhM3HJD3V0YsZQmITuY4z6PhKu/vMh/Xiu0HSixHjqlqSppkB4IikjSTnkny7pP0R19AEYntLOs5A4yvtAYUTLanS7agQtk+W9PqC8athuvAFkt5J8i2wROTiLl72QBOTyHWcQcUTkQOKpDFJMyWdLmlaeJ5s507YgSUwo/zXwvH+BmBkKupqSOQ6zkDi4ZHmciwk4AQAOX4qTaXTRK7jDCS+0m4u95O8B8BskitgzWA3FIxpEtMkbQv2qi9J2gUAkp7v83U5Tkf4SruhSLqV5IcBHIFVR66StL3Pl9VL6kjkOs7A4YnIEWAUE3CdJnIdZ1Dx8EjD8AScUUMi13EGEl9pNwySezCRgLsXLQm4MkU5juMMLr7Sbh6egHOcBuOTdvPwBJzjNBgPjzQMT8A5TrPxSdtxHGeI8PCI4zjOEOGTtuM4zhDhk7bjOM4Q4ZO24zjOEPE/WGVp9jE0COEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the Missing Data if any\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.heatmap(train.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When we write the ML Code. We generally divide the data in X and Y\n",
    "# X - Input Data /Predictor Variables\n",
    "# Y - Predict the Output/Target Variable - SalePrice .drop\n",
    "x = train.drop(\"SalePrice\", axis = 1) # Axis = 0 : Row, Axis= 1: Column\n",
    "y = train[\"SalePrice\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</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>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</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>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</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>Gd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</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   1          60       RL           65     8450   Pave  Grvl      Reg   \n",
       "1   2          20       RL           80     9600   Pave  Grvl      Reg   \n",
       "2   3          60       RL           68    11250   Pave  Grvl      IR1   \n",
       "3   4          70       RL           60     9550   Pave  Grvl      IR1   \n",
       "4   5          60       RL           84    14260   Pave  Grvl      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...                 0        0     Gd  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0     Gd  MnPrv   \n",
       "2         Lvl    AllPub      ...                 0        0     Gd  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0     Gd  MnPrv   \n",
       "4         Lvl    AllPub      ...                 0        0     Gd  MnPrv   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0        Shed       0      2    2008        WD         Normal  \n",
       "1        Shed       0      5    2007        WD         Normal  \n",
       "2        Shed       0      9    2008        WD         Normal  \n",
       "3        Shed       0      2    2006        WD        Abnorml  \n",
       "4        Shed       0     12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    208500\n",
       "1    181500\n",
       "2    223500\n",
       "3    140000\n",
       "4    250000\n",
       "Name: SalePrice, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to convert the Cat Val in Numerical - One Hot Encoding\n",
    "newtrain = pd.get_dummies(train)\n",
    "newtest = pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
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       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
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       "      <td>978</td>\n",
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       "      <td>9550</td>\n",
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       "</table>\n",
       "<p>5 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1          60           65     8450            7            5       2003   \n",
       "1   2          20           80     9600            6            8       1976   \n",
       "2   3          60           68    11250            7            5       2001   \n",
       "3   4          70           60     9550            7            5       1915   \n",
       "4   5          60           84    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1          ...            \\\n",
       "0          2003         196         706          ...             \n",
       "1          1976           0         978          ...             \n",
       "2          2002         162         486          ...             \n",
       "3          1970           0         216          ...             \n",
       "4          2000         350         655          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0               0             0             0            1   \n",
       "1               0             0             0            1   \n",
       "2               0             0             0            1   \n",
       "3               0             0             0            1   \n",
       "4               0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 290 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual',\n",
       "       'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',\n",
       "       ...\n",
       "       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',\n",
       "       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',\n",
       "       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',\n",
       "       'SaleCondition_Partial'],\n",
       "      dtype='object', length=290)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we see that data follows different scales with different features so, we need to normalize the data\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variable and passin the Standard Scaler\n",
    "ss = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two First of all - Fit the Model on the Data | .fit\n",
    "# Then we will try applying that learning on unknown data - .predict\n",
    "scaledtrain = ss.fit_transform(newtrain)\n",
    "scaledtest = ss.fit_transform(newtest)\n",
    "\n",
    "# Tip - Standard Scaler converts the data into o and 1 scale but the data type is now array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledtrain = pd.DataFrame(scaledtrain)\n",
    "scaledtrain.columns = newtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>MSSubClass</th>\n",
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       "      <th>SaleType_Oth</th>\n",
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       "      <th>SaleCondition_Normal</th>\n",
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       "      <td>-0.220875</td>\n",
       "      <td>-0.207142</td>\n",
       "      <td>0.651479</td>\n",
       "      <td>-0.517200</td>\n",
       "      <td>1.050994</td>\n",
       "      <td>0.878668</td>\n",
       "      <td>0.514104</td>\n",
       "      <td>0.575425</td>\n",
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       "      <td>-0.301962</td>\n",
       "      <td>-0.045376</td>\n",
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       "      <td>-0.272616</td>\n",
       "      <td>-0.052414</td>\n",
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       "      <td>-0.117851</td>\n",
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       "      <td>0.460320</td>\n",
       "      <td>-0.091886</td>\n",
       "      <td>-0.071836</td>\n",
       "      <td>2.179628</td>\n",
       "      <td>0.156734</td>\n",
       "      <td>-0.429577</td>\n",
       "      <td>-0.570750</td>\n",
       "      <td>1.171992</td>\n",
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       "      <td>0.467651</td>\n",
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       "      <td>-0.084636</td>\n",
       "      <td>0.073480</td>\n",
       "      <td>0.651479</td>\n",
       "      <td>-0.517200</td>\n",
       "      <td>0.984752</td>\n",
       "      <td>0.830215</td>\n",
       "      <td>0.325915</td>\n",
       "      <td>0.092907</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058621</td>\n",
       "      <td>-0.301962</td>\n",
       "      <td>-0.045376</td>\n",
       "      <td>0.390293</td>\n",
       "      <td>-0.272616</td>\n",
       "      <td>-0.052414</td>\n",
       "      <td>-0.091035</td>\n",
       "      <td>-0.117851</td>\n",
       "      <td>0.467651</td>\n",
       "      <td>-0.305995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.723747</td>\n",
       "      <td>0.309859</td>\n",
       "      <td>-0.447940</td>\n",
       "      <td>-0.096897</td>\n",
       "      <td>0.651479</td>\n",
       "      <td>-0.517200</td>\n",
       "      <td>-1.863632</td>\n",
       "      <td>-0.720298</td>\n",
       "      <td>-0.570750</td>\n",
       "      <td>-0.499274</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058621</td>\n",
       "      <td>-0.301962</td>\n",
       "      <td>-0.045376</td>\n",
       "      <td>0.390293</td>\n",
       "      <td>3.668167</td>\n",
       "      <td>-0.052414</td>\n",
       "      <td>-0.091035</td>\n",
       "      <td>-0.117851</td>\n",
       "      <td>-2.138345</td>\n",
       "      <td>-0.305995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.721374</td>\n",
       "      <td>0.073375</td>\n",
       "      <td>0.641972</td>\n",
       "      <td>0.375148</td>\n",
       "      <td>1.374795</td>\n",
       "      <td>-0.517200</td>\n",
       "      <td>0.951632</td>\n",
       "      <td>0.733308</td>\n",
       "      <td>1.366489</td>\n",
       "      <td>0.463568</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058621</td>\n",
       "      <td>-0.301962</td>\n",
       "      <td>-0.045376</td>\n",
       "      <td>0.390293</td>\n",
       "      <td>-0.272616</td>\n",
       "      <td>-0.052414</td>\n",
       "      <td>-0.091035</td>\n",
       "      <td>-0.117851</td>\n",
       "      <td>0.467651</td>\n",
       "      <td>-0.305995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "0 -1.730865    0.073375    -0.220875 -0.207142     0.651479    -0.517200   \n",
       "1 -1.728492   -0.872563     0.460320 -0.091886    -0.071836     2.179628   \n",
       "2 -1.726120    0.073375    -0.084636  0.073480     0.651479    -0.517200   \n",
       "3 -1.723747    0.309859    -0.447940 -0.096897     0.651479    -0.517200   \n",
       "4 -1.721374    0.073375     0.641972  0.375148     1.374795    -0.517200   \n",
       "\n",
       "   YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1          ...            \\\n",
       "0   1.050994      0.878668    0.514104    0.575425          ...             \n",
       "1   0.156734     -0.429577   -0.570750    1.171992          ...             \n",
       "2   0.984752      0.830215    0.325915    0.092907          ...             \n",
       "3  -1.863632     -0.720298   -0.570750   -0.499274          ...             \n",
       "4   0.951632      0.733308    1.366489    0.463568          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0       -0.058621     -0.301962     -0.045376     0.390293   \n",
       "1       -0.058621     -0.301962     -0.045376     0.390293   \n",
       "2       -0.058621     -0.301962     -0.045376     0.390293   \n",
       "3       -0.058621     -0.301962     -0.045376     0.390293   \n",
       "4       -0.058621     -0.301962     -0.045376     0.390293   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0              -0.272616              -0.052414             -0.091035   \n",
       "1              -0.272616              -0.052414             -0.091035   \n",
       "2              -0.272616              -0.052414             -0.091035   \n",
       "3               3.668167              -0.052414             -0.091035   \n",
       "4              -0.272616              -0.052414             -0.091035   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0             -0.117851              0.467651              -0.305995  \n",
       "1             -0.117851              0.467651              -0.305995  \n",
       "2             -0.117851              0.467651              -0.305995  \n",
       "3             -0.117851             -2.138345              -0.305995  \n",
       "4             -0.117851              0.467651              -0.305995  \n",
       "\n",
       "[5 rows x 290 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledtest = pd.DataFrame(scaledtest)\n",
    "scaledtest.columns = newtest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.730864</td>\n",
       "      <td>-0.874711</td>\n",
       "      <td>0.567330</td>\n",
       "      <td>0.363929</td>\n",
       "      <td>-0.751101</td>\n",
       "      <td>0.400766</td>\n",
       "      <td>-0.340945</td>\n",
       "      <td>-1.072885</td>\n",
       "      <td>-0.563316</td>\n",
       "      <td>0.063428</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.728490</td>\n",
       "      <td>-0.874711</td>\n",
       "      <td>0.615963</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>-0.054877</td>\n",
       "      <td>0.400766</td>\n",
       "      <td>-0.439695</td>\n",
       "      <td>-1.214908</td>\n",
       "      <td>0.047057</td>\n",
       "      <td>1.063511</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.726115</td>\n",
       "      <td>0.061351</td>\n",
       "      <td>0.275532</td>\n",
       "      <td>0.809646</td>\n",
       "      <td>-0.751101</td>\n",
       "      <td>-0.497418</td>\n",
       "      <td>0.844059</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>-0.563316</td>\n",
       "      <td>0.773377</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.723741</td>\n",
       "      <td>0.061351</td>\n",
       "      <td>0.470064</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>-0.054877</td>\n",
       "      <td>0.400766</td>\n",
       "      <td>0.876976</td>\n",
       "      <td>0.678742</td>\n",
       "      <td>-0.450284</td>\n",
       "      <td>0.357958</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.721367</td>\n",
       "      <td>1.465443</td>\n",
       "      <td>-1.232092</td>\n",
       "      <td>-0.971808</td>\n",
       "      <td>1.337571</td>\n",
       "      <td>-0.497418</td>\n",
       "      <td>0.679475</td>\n",
       "      <td>0.394694</td>\n",
       "      <td>-0.563316</td>\n",
       "      <td>-0.387160</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045392</td>\n",
       "      <td>-0.295268</td>\n",
       "      <td>-0.052432</td>\n",
       "      <td>0.398568</td>\n",
       "      <td>-0.254879</td>\n",
       "      <td>-0.074253</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.134699</td>\n",
       "      <td>0.460211</td>\n",
       "      <td>-0.299365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 271 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "0 -1.730864   -0.874711     0.567330  0.363929    -0.751101     0.400766   \n",
       "1 -1.728490   -0.874711     0.615963  0.897861    -0.054877     0.400766   \n",
       "2 -1.726115    0.061351     0.275532  0.809646    -0.751101    -0.497418   \n",
       "3 -1.723741    0.061351     0.470064  0.032064    -0.054877     0.400766   \n",
       "4 -1.721367    1.465443    -1.232092 -0.971808     1.337571    -0.497418   \n",
       "\n",
       "   YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1          ...            \\\n",
       "0  -0.340945     -1.072885   -0.563316    0.063428          ...             \n",
       "1  -0.439695     -1.214908    0.047057    1.063511          ...             \n",
       "2   0.844059      0.678742   -0.563316    0.773377          ...             \n",
       "3   0.876976      0.678742   -0.450284    0.357958          ...             \n",
       "4   0.679475      0.394694   -0.563316   -0.387160          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "1       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "2       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "3       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "4       -0.045392     -0.295268     -0.052432     0.398568   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0              -0.254879              -0.074253             -0.091066   \n",
       "1              -0.254879              -0.074253             -0.091066   \n",
       "2              -0.254879              -0.074253             -0.091066   \n",
       "3              -0.254879              -0.074253             -0.091066   \n",
       "4              -0.254879              -0.074253             -0.091066   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0             -0.134699              0.460211              -0.299365  \n",
       "1             -0.134699              0.460211              -0.299365  \n",
       "2             -0.134699              0.460211              -0.299365  \n",
       "3             -0.134699              0.460211              -0.299365  \n",
       "4             -0.134699              0.460211              -0.299365  \n",
       "\n",
       "[5 rows x 271 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledtest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = scaledtrain.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# By Applying PCA we focus only on those columns that define the max variance in the data\n",
    "# We will initially take those columns that explain 65~70% of the variance in the data\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 120)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune the Data by PCA\n",
    "finaltrain = pca.fit_transform(scaledtrain)\n",
    "finaltrain = pd.DataFrame(finaltrain)\n",
    "finaltrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 120)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune the Data by PCA\n",
    "finaltest = pca.fit_transform(scaledtest)\n",
    "finaltest = pd.DataFrame(finaltest)\n",
    "finaltest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the Linear Regression\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(finaltrain, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr = lr.predict(finaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time to Export the Data\n",
    "ID = test[\"Id\"].values.tolist()\n",
    "Prediction = pred_lr\n",
    "d = {\"Id\":ID, \"SalePrice\":Prediction}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.68818892,  0.37427023, -0.17173755, ...,  0.20256957,\n",
       "       -0.24777256,  0.58127862])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the Fresh Transformation of Scaling the Data once again\n",
    "scaledtarget =ss.fit_transform(pd.DataFrame(train.SalePrice))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So that we can reconvert the Pred VALUES IN ORIGINAL\n",
    "actualsales = ss.inverse_transform(pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126268.47192218, 210643.97529379, 167282.60804411, ...,\n",
       "       197008.31772764, 161244.26561044, 227083.60691137])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actualsales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":ID, \"SalePrice\":actualsales})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>126268.471922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>210643.975294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>167282.608044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>199972.217737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>173666.102335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  126268.471922\n",
       "1  1462  210643.975294\n",
       "2  1463  167282.608044\n",
       "3  1464  199972.217737\n",
       "4  1465  173666.102335"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the Data as csv\n",
    "solution.to_csv(\"LM Model.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(finaltrain, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf = rf.predict(finaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = ss.inverse_transform(pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Id\":ID, \"SalePrice\":ypred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the Data as csv\n",
    "solution.to_csv(\"RF Model.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "bag = BaggingRegressor()\n",
    "bag.fit(finaltrain, y)\n",
    "pred_bag = bag.predict(finaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = ss.inverse_transform(pred_bag)\n",
    "solution = pd.DataFrame({\"Id\":ID, \"SalePrice\":ypred})\n",
    "# Export the Data as csv\n",
    "solution.to_csv(\"Bagging Model.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada = AdaBoostRegressor()\n",
    "ada.fit(finaltrain, y)\n",
    "pred_ada = ada.predict(finaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = ss.inverse_transform(pred_ada)\n",
    "solution = pd.DataFrame({\"Id\":ID, \"SalePrice\":ypred})\n",
    "# Export the Data as csv\n",
    "solution.to_csv(\"ADABoost Model.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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