kaggle-houseprice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalesPrice</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>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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>None</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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>None</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave  None      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave  None      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave  None      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave  None      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave  None      IR1   \n",
       "\n",
       "  LandContour LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType  \\\n",
       "0         Lvl    Inside       Gtl      CollgCr       Norm       Norm     1Fam   \n",
       "1         Lvl       FR2       Gtl      Veenker      Feedr       Norm     1Fam   \n",
       "2         Lvl    Inside       Gtl      CollgCr       Norm       Norm     1Fam   \n",
       "3         Lvl    Corner       Gtl      Crawfor       Norm       Norm     1Fam   \n",
       "4         Lvl       FR2       Gtl      NoRidge       Norm       Norm     1Fam   \n",
       "\n",
       "  HouseStyle  OverallQual  OverallCond  YearBuilt  YearRemodAdd RoofStyle  \\\n",
       "0     2Story            7            5       2003          2003     Gable   \n",
       "1     1Story            6            8       1976          1976     Gable   \n",
       "2     2Story            7            5       2001          2002     Gable   \n",
       "3     2Story            7            5       1915          1970     Gable   \n",
       "4     2Story            8            5       2000          2000     Gable   \n",
       "\n",
       "  RoofMatl Exterior1st Exterior2nd MasVnrType  MasVnrArea ExterQual ExterCond  \\\n",
       "0  CompShg     VinylSd     VinylSd    BrkFace       196.0        Gd        TA   \n",
       "1  CompShg     MetalSd     MetalSd       None         0.0        TA        TA   \n",
       "2  CompShg     VinylSd     VinylSd    BrkFace       162.0        Gd        TA   \n",
       "3  CompShg     Wd Sdng     Wd Shng       None         0.0        TA        TA   \n",
       "4  CompShg     VinylSd     VinylSd    BrkFace       350.0        Gd        TA   \n",
       "\n",
       "  Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1  BsmtFinSF1  \\\n",
       "0      PConc       Gd       TA           No          GLQ       706.0   \n",
       "1     CBlock       Gd       TA           Gd          ALQ       978.0   \n",
       "2      PConc       Gd       TA           Mn          GLQ       486.0   \n",
       "3     BrkTil       TA       Gd           No          ALQ       216.0   \n",
       "4      PConc       Gd       TA           Av          GLQ       655.0   \n",
       "\n",
       "  BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF Heating HeatingQC  \\\n",
       "0          Unf         0.0      150.0        856.0    GasA        Ex   \n",
       "1          Unf         0.0      284.0       1262.0    GasA        Ex   \n",
       "2          Unf         0.0      434.0        920.0    GasA        Ex   \n",
       "3          Unf         0.0      540.0        756.0    GasA        Gd   \n",
       "4          Unf         0.0      490.0       1145.0    GasA        Ex   \n",
       "\n",
       "  CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  \\\n",
       "0          Y      SBrkr       856       854             0       1710   \n",
       "1          Y      SBrkr      1262         0             0       1262   \n",
       "2          Y      SBrkr       920       866             0       1786   \n",
       "3          Y      SBrkr       961       756             0       1717   \n",
       "4          Y      SBrkr      1145      1053             0       2198   \n",
       "\n",
       "   BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  \\\n",
       "0           1.0           0.0         2         1             3             1   \n",
       "1           0.0           1.0         2         0             3             1   \n",
       "2           1.0           0.0         2         1             3             1   \n",
       "3           1.0           0.0         1         0             3             1   \n",
       "4           1.0           0.0         2         1             4             1   \n",
       "\n",
       "  KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  \\\n",
       "0          Gd             8        Typ           0        None     Attchd   \n",
       "1          TA             6        Typ           1          TA     Attchd   \n",
       "2          Gd             6        Typ           1          TA     Attchd   \n",
       "3          Gd             7        Typ           1          Gd     Detchd   \n",
       "4          Gd             9        Typ           1          TA     Attchd   \n",
       "\n",
       "   GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual GarageCond  \\\n",
       "0       2003.0          RFn         2.0       548.0         TA         TA   \n",
       "1       1976.0          RFn         2.0       460.0         TA         TA   \n",
       "2       2001.0          RFn         2.0       608.0         TA         TA   \n",
       "3       1998.0          Unf         3.0       642.0         TA         TA   \n",
       "4       2000.0          RFn         3.0       836.0         TA         TA   \n",
       "\n",
       "  PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "0          Y           0           61              0          0            0   \n",
       "1          Y         298            0              0          0            0   \n",
       "2          Y           0           42              0          0            0   \n",
       "3          Y           0           35            272          0            0   \n",
       "4          Y         192           84              0          0            0   \n",
       "\n",
       "   PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold SaleType  \\\n",
       "0         0   None  None        None        0       2    2008       WD   \n",
       "1         0   None  None        None        0       5    2007       WD   \n",
       "2         0   None  None        None        0       9    2008       WD   \n",
       "3         0   None  None        None        0       2    2006       WD   \n",
       "4         0   None  None        None        0      12    2008       WD   \n",
       "\n",
       "  SaleCondition  SalesPrice  \n",
       "0        Normal    208500.0  \n",
       "1        Normal    181500.0  \n",
       "2        Normal    223500.0  \n",
       "3       Abnorml    140000.0  \n",
       "4        Normal    250000.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 1000\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import linear_model, preprocessing, model_selection, metrics, pipeline\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\Z003N71U\\Downloads\\Training\\data_combined_cleaned.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_train: 0.942081, \n",
      "R2_test: 0.196740\n",
      "rmse train: 19473.835709\n",
      "rmse_test: 67893.104940\n"
     ]
    }
   ],
   "source": [
    "df_clean = df.drop(columns=[\"Id\"])  # remove the id column as it does not add an value to the feature\n",
    "\n",
    "df_clean = df_clean[~df.SalesPrice.isnull()] # no need of empty sales price rows, cos sales price is what we are gonna predict\n",
    "\n",
    "target = \"SalesPrice\"\n",
    "X = df_clean.copy()\n",
    "del X[target]\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "y = df_clean[target]\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                            , test_size = 0.3, random_state = 123)\n",
    "\n",
    "est = linear_model.LinearRegression()\n",
    "est.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = est.predict(X_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_train: 1.000000, \n",
      "R2_test: 0.838407\n",
      "rmse train: 0.000000\n",
      "rmse_test: 30451.497318\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb22eb00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean = df.drop(columns=[\"Id\"])  # remove the id column as it does not add an value to the feature\n",
    "\n",
    "df_clean = df_clean[~df.SalesPrice.isnull()] # no need of empty sales price rows, cos sales price is what we are gonna predict\n",
    "\n",
    "target = \"SalesPrice\"\n",
    "X = df_clean.copy()\n",
    "del X[target]\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "y = df_clean[target]\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                            , test_size = 0.3, random_state = 123)\n",
    "\n",
    "est = pipeline.Pipeline([\n",
    "    (\"poly\", preprocessing.PolynomialFeatures(degree=2\n",
    "                                    , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"lr\", linear_model.LinearRegression())\n",
    "])\n",
    "\n",
    "#est = linear_model.LinearRegression()\n",
    "est.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = est.predict(X_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))\n",
    "\n",
    "pd.DataFrame({\"actual\": y_test, \"Predicted\": y_test_pred}).plot.scatter(0,1, color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x58f9160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame({\"actual\": y_test, \"Predicted\": y_test_pred}).plot.scatter(0,1, color=\"red\")\n",
    "plt.scatter([y_train],[y_train_pred], color= [\"red\", \"blue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb77b4a8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([y_test],[y_test_pred], color= [\"red\", \"blue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_train: 0.896578, \n",
      "R2_test: 0.864171\n",
      "rmse train: 26022.351097\n",
      "rmse_test: 27918.633972\n"
     ]
    }
   ],
   "source": [
    "df_clean = df.drop(columns=[\"Id\"])  # remove the id column as it does not add an value to the feature\n",
    "\n",
    "df_clean = df_clean[~df.SalesPrice.isnull()] # no need of empty sales price rows, cos sales price is what we are gonna predict\n",
    "\n",
    "target = \"SalesPrice\"\n",
    "X = df_clean.copy()\n",
    "del X[target]\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "y = df_clean[target]\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y\n",
    "                            , test_size = 0.3, random_state = 123)\n",
    "\n",
    "est = pipeline.Pipeline([\n",
    "    (\"poly\", preprocessing.PolynomialFeatures(degree=1\n",
    "                                    , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"lr\", linear_model.Ridge(alpha=400.0, max_iter=1000))\n",
    "])\n",
    "\n",
    "#est = linear_model.LinearRegression()\n",
    "est.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = est.predict(X_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "R2_train: 0.896578, \n",
      "R2_test: 0.864171\n",
      "rmse train: 26022.351097\n",
      "rmse_test: 27918.633972\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.9s finished\n"
     ]
    }
   ],
   "source": [
    "pram_grid = {\n",
    "    \"lr__alpha\": 100 * np.linspace(10, 12, 20)\n",
    "}\n",
    "gs = model_selection.GridSearchCV(est, pram_grid, cv=5, verbose=True)\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = est.predict(X_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lr__alpha': 1000.0}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100.        , 101.05263158, 102.10526316, 103.15789474,\n",
       "       104.21052632, 105.26315789, 106.31578947, 107.36842105,\n",
       "       108.42105263, 109.47368421, 110.52631579, 111.57894737,\n",
       "       112.63157895, 113.68421053, 114.73684211, 115.78947368,\n",
       "       116.84210526, 117.89473684, 118.94736842, 120.        ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 * np.linspace(10, 12, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"model.pickel\", \"wb\") as f:\n",
    "    pickle.dump(gs,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_train: 0.860159, \n",
      "R2_test: 0.849497\n",
      "rmse train: 30259.164013\n",
      "rmse_test: 29387.985154\n"
     ]
    }
   ],
   "source": [
    "with open(\"model.pickel\", \"rb\") as f:\n",
    "    model_reloaded = pickle.load(f)\n",
    "    \n",
    "y_train_pred = model_reloaded.predict(X_train)\n",
    "y_test_pred = model_reloaded.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_train: 0.912570, \n",
      "R2_test: 0.861076\n",
      "rmse train: 23925.992834\n",
      "rmse_test: 28234.910567\n"
     ]
    }
   ],
   "source": [
    "est = pipeline.Pipeline([\n",
    "    (\"poly\", preprocessing.PolynomialFeatures(degree=1\n",
    "                                    , include_bias=False)),\n",
    "    (\"scaler\", preprocessing.StandardScaler()),\n",
    "    (\"lr\", linear_model.ElasticNet(alpha=600.0, l1_ratio=1))\n",
    "])\n",
    "\n",
    "#est = linear_model.LinearRegression()\n",
    "est.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = est.predict(X_train)\n",
    "y_test_pred = est.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_train: -0.643154, \n",
      "R2_test: -0.810286\n",
      "rmse train: 103724.042353\n",
      "rmse_test: 101922.760214\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "model= GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "r2_train = metrics.r2_score(y_train, y_train_pred)\n",
    "r2_test = metrics.r2_score(y_test, y_test_pred)\n",
    "rmse_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_pred))\n",
    "rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "\n",
    "print(\"R2_train: %f, \\nR2_test: %f\\nrmse train: %f\\nrmse_test: %f\" \n",
    "      % (r2_train, r2_test, rmse_train, rmse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"accuraccy %f\" % ((167 + )))"
   ]
  }
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