ml2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "AaIG4zSAMLzu"
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "uIU18c3NMton"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1989,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1113,
     "status": "ok",
     "timestamp": 1518028344713,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "DLGVoa0AOaOY",
    "outputId": "076fb31f-0d7b-4370-aeb1-d33f2ddaca6e",
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>500</td>\n",
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       "      <th>18</th>\n",
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       "      <th>19</th>\n",
       "      <td>20</td>\n",
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       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>325300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>RM</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7449</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>139400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9742</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>120</td>\n",
       "      <td>RM</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4224</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8246</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>154000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>110.0</td>\n",
       "      <td>14230</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>256300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7200</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>98.0</td>\n",
       "      <td>11478</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>306000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>47.0</td>\n",
       "      <td>16321</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>207500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>RM</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6324</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>68500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>1431</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>21930</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>192140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>1432</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4928</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>1433</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10800</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>64500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>1434</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>10261</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>186500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>1435</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17400</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>1436</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8400</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>COD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>174000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>1437</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdWo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>120500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>1438</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12444</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>394617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>1439</td>\n",
       "      <td>20</td>\n",
       "      <td>RM</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7407</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>149700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>1440</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11584</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>197000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>1441</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>79.0</td>\n",
       "      <td>11526</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>191000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1442</td>\n",
       "      <td>120</td>\n",
       "      <td>RM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4426</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>149300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>1443</td>\n",
       "      <td>60</td>\n",
       "      <td>FV</td>\n",
       "      <td>85.0</td>\n",
       "      <td>11003</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>1444</td>\n",
       "      <td>30</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8854</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>121000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>1445</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>63.0</td>\n",
       "      <td>8500</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>179600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>1446</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8400</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>1447</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26142</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>157900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>1448</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>1449</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11767</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdWo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>112000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>1450</td>\n",
       "      <td>180</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1533</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1451</td>\n",
       "      <td>90</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>136000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1452</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9262</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>287090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>1453</td>\n",
       "      <td>180</td>\n",
       "      <td>RM</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3675</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>1454</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>90.0</td>\n",
       "      <td>17217</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>84500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>1455</td>\n",
       "      <td>20</td>\n",
       "      <td>FV</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7500</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "5        6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6        7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7        8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8        9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9       10         190       RL         50.0     7420   Pave   NaN      Reg   \n",
       "10      11          20       RL         70.0    11200   Pave   NaN      Reg   \n",
       "11      12          60       RL         85.0    11924   Pave   NaN      IR1   \n",
       "12      13          20       RL          NaN    12968   Pave   NaN      IR2   \n",
       "13      14          20       RL         91.0    10652   Pave   NaN      IR1   \n",
       "14      15          20       RL          NaN    10920   Pave   NaN      IR1   \n",
       "15      16          45       RM         51.0     6120   Pave   NaN      Reg   \n",
       "16      17          20       RL          NaN    11241   Pave   NaN      IR1   \n",
       "17      18          90       RL         72.0    10791   Pave   NaN      Reg   \n",
       "18      19          20       RL         66.0    13695   Pave   NaN      Reg   \n",
       "19      20          20       RL         70.0     7560   Pave   NaN      Reg   \n",
       "20      21          60       RL        101.0    14215   Pave   NaN      IR1   \n",
       "21      22          45       RM         57.0     7449   Pave  Grvl      Reg   \n",
       "22      23          20       RL         75.0     9742   Pave   NaN      Reg   \n",
       "23      24         120       RM         44.0     4224   Pave   NaN      Reg   \n",
       "24      25          20       RL          NaN     8246   Pave   NaN      IR1   \n",
       "25      26          20       RL        110.0    14230   Pave   NaN      Reg   \n",
       "26      27          20       RL         60.0     7200   Pave   NaN      Reg   \n",
       "27      28          20       RL         98.0    11478   Pave   NaN      Reg   \n",
       "28      29          20       RL         47.0    16321   Pave   NaN      IR1   \n",
       "29      30          30       RM         60.0     6324   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1430  1431          60       RL         60.0    21930   Pave   NaN      IR3   \n",
       "1431  1432         120       RL          NaN     4928   Pave   NaN      IR1   \n",
       "1432  1433          30       RL         60.0    10800   Pave  Grvl      Reg   \n",
       "1433  1434          60       RL         93.0    10261   Pave   NaN      IR1   \n",
       "1434  1435          20       RL         80.0    17400   Pave   NaN      Reg   \n",
       "1435  1436          20       RL         80.0     8400   Pave   NaN      Reg   \n",
       "1436  1437          20       RL         60.0     9000   Pave   NaN      Reg   \n",
       "1437  1438          20       RL         96.0    12444   Pave   NaN      Reg   \n",
       "1438  1439          20       RM         90.0     7407   Pave   NaN      Reg   \n",
       "1439  1440          60       RL         80.0    11584   Pave   NaN      Reg   \n",
       "1440  1441          70       RL         79.0    11526   Pave   NaN      IR1   \n",
       "1441  1442         120       RM          NaN     4426   Pave   NaN      Reg   \n",
       "1442  1443          60       FV         85.0    11003   Pave   NaN      Reg   \n",
       "1443  1444          30       RL          NaN     8854   Pave   NaN      Reg   \n",
       "1444  1445          20       RL         63.0     8500   Pave   NaN      Reg   \n",
       "1445  1446          85       RL         70.0     8400   Pave   NaN      Reg   \n",
       "1446  1447          20       RL          NaN    26142   Pave   NaN      IR1   \n",
       "1447  1448          60       RL         80.0    10000   Pave   NaN      Reg   \n",
       "1448  1449          50       RL         70.0    11767   Pave   NaN      Reg   \n",
       "1449  1450         180       RM         21.0     1533   Pave   NaN      Reg   \n",
       "1450  1451          90       RL         60.0     9000   Pave   NaN      Reg   \n",
       "1451  1452          20       RL         78.0     9262   Pave   NaN      Reg   \n",
       "1452  1453         180       RM         35.0     3675   Pave   NaN      Reg   \n",
       "1453  1454          20       RL         90.0    17217   Pave   NaN      Reg   \n",
       "1454  1455          20       FV         62.0     7500   Pave  Pave      Reg   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "2            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "3            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "4            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "5            Lvl    AllPub    ...            0    NaN  MnPrv        Shed   \n",
       "6            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "7            Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
       "8            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "9            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "10           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "11           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "12           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "13           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "14           Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
       "15           Lvl    AllPub    ...            0    NaN  GdPrv         NaN   \n",
       "16           Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
       "17           Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
       "18           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "19           Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "20           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "21           Bnk    AllPub    ...            0    NaN  GdPrv         NaN   \n",
       "22           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "23           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "24           Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "25           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "26           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "27           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "28           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "29           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "...          ...       ...    ...          ...    ...    ...         ...   \n",
       "1430         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1431         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1432         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1433         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1434         Low    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1435         Lvl    AllPub    ...            0    NaN  GdPrv         NaN   \n",
       "1436         Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
       "1437         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1438         Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "1439         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1440         Bnk    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1441         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1442         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1443         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1444         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1445         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1446         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1447         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1448         Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
       "1449         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1450         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1451         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1452         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1453         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1454         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1455         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1456         Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
       "1457         Lvl    AllPub    ...            0    NaN  GdPrv        Shed   \n",
       "1458         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "1459         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
       "\n",
       "     MiscVal MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0          0      2   2008        WD         Normal     208500  \n",
       "1          0      5   2007        WD         Normal     181500  \n",
       "2          0      9   2008        WD         Normal     223500  \n",
       "3          0      2   2006        WD        Abnorml     140000  \n",
       "4          0     12   2008        WD         Normal     250000  \n",
       "5        700     10   2009        WD         Normal     143000  \n",
       "6          0      8   2007        WD         Normal     307000  \n",
       "7        350     11   2009        WD         Normal     200000  \n",
       "8          0      4   2008        WD        Abnorml     129900  \n",
       "9          0      1   2008        WD         Normal     118000  \n",
       "10         0      2   2008        WD         Normal     129500  \n",
       "11         0      7   2006       New        Partial     345000  \n",
       "12         0      9   2008        WD         Normal     144000  \n",
       "13         0      8   2007       New        Partial     279500  \n",
       "14         0      5   2008        WD         Normal     157000  \n",
       "15         0      7   2007        WD         Normal     132000  \n",
       "16       700      3   2010        WD         Normal     149000  \n",
       "17       500     10   2006        WD         Normal      90000  \n",
       "18         0      6   2008        WD         Normal     159000  \n",
       "19         0      5   2009       COD        Abnorml     139000  \n",
       "20         0     11   2006       New        Partial     325300  \n",
       "21         0      6   2007        WD         Normal     139400  \n",
       "22         0      9   2008        WD         Normal     230000  \n",
       "23         0      6   2007        WD         Normal     129900  \n",
       "24         0      5   2010        WD         Normal     154000  \n",
       "25         0      7   2009        WD         Normal     256300  \n",
       "26         0      5   2010        WD         Normal     134800  \n",
       "27         0      5   2010        WD         Normal     306000  \n",
       "28         0     12   2006        WD         Normal     207500  \n",
       "29         0      5   2008        WD         Normal      68500  \n",
       "...      ...    ...    ...       ...            ...        ...  \n",
       "1430       0      7   2006        WD         Normal     192140  \n",
       "1431       0     10   2009        WD         Normal     143750  \n",
       "1432       0      8   2007        WD         Normal      64500  \n",
       "1433       0      5   2008        WD         Normal     186500  \n",
       "1434       0      5   2006        WD         Normal     160000  \n",
       "1435       0      7   2008       COD        Abnorml     174000  \n",
       "1436       0      5   2007        WD         Normal     120500  \n",
       "1437       0     11   2008       New        Partial     394617  \n",
       "1438       0      4   2010        WD         Normal     149700  \n",
       "1439       0     11   2007        WD         Normal     197000  \n",
       "1440       0      9   2008        WD         Normal     191000  \n",
       "1441       0      5   2008        WD         Normal     149300  \n",
       "1442       0      4   2009        WD         Normal     310000  \n",
       "1443       0      5   2009        WD         Normal     121000  \n",
       "1444       0     11   2007        WD         Normal     179600  \n",
       "1445       0      5   2007        WD         Normal     129000  \n",
       "1446       0      4   2010        WD         Normal     157900  \n",
       "1447       0     12   2007        WD         Normal     240000  \n",
       "1448       0      5   2007        WD         Normal     112000  \n",
       "1449       0      8   2006        WD        Abnorml      92000  \n",
       "1450       0      9   2009        WD         Normal     136000  \n",
       "1451       0      5   2009       New        Partial     287090  \n",
       "1452       0      5   2006        WD         Normal     145000  \n",
       "1453       0      7   2006        WD        Abnorml      84500  \n",
       "1454       0     10   2009        WD         Normal     185000  \n",
       "1455       0      8   2007        WD         Normal     175000  \n",
       "1456       0      2   2010        WD         Normal     210000  \n",
       "1457    2500      5   2010        WD         Normal     266500  \n",
       "1458       0      4   2010        WD         Normal     142125  \n",
       "1459       0      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1400,
     "status": "ok",
     "timestamp": 1518028347539,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "b6kDGQj2OdZG",
    "outputId": "912d07f9-012e-48d5-d099-c806ad002968"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    1460.000000  \n",
       "mean   180921.195890  \n",
       "std     79442.502883  \n",
       "min     34900.000000  \n",
       "25%    129975.000000  \n",
       "50%    163000.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
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     "status": "ok",
     "timestamp": 1518028349785,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "ShErNodAO84g",
    "outputId": "52f00849-d03d-4ad6-a8a7-ac4cfee924f4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 125,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1613,
     "status": "ok",
     "timestamp": 1518028352360,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "pyke9x4-P7Wy",
    "outputId": "78e18566-f7e4-47e0-d37d-739a380c6745"
   },
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_data = data['SalePrice']\n",
    "price_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "Bwvp4p-fRKW6"
   },
   "outputs": [],
   "source": [
    "columns_of_interest = ['LotArea', 'BedroomAbvGr']\n",
    "two_columns_of_data = data[columns_of_interest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 204,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 803,
     "status": "ok",
     "timestamp": 1518028356008,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "sn01CcITRR-N",
    "outputId": "76ebecb0-af5a-43ca-e3f0-1f71947542f6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8450</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11250</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9550</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14260</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotArea  BedroomAbvGr\n",
       "0     8450             3\n",
       "1     9600             3\n",
       "2    11250             3\n",
       "3     9550             3\n",
       "4    14260             4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_columns_of_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 297,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1324,
     "status": "ok",
     "timestamp": 1518028358422,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "FuH2mUiERliH",
    "outputId": "4e7b31de-d6f3-4dde-ca48-4db1a1f2245a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10516.828082</td>\n",
       "      <td>2.866438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9981.264932</td>\n",
       "      <td>0.815778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7553.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9478.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11601.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>215245.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             LotArea  BedroomAbvGr\n",
       "count    1460.000000   1460.000000\n",
       "mean    10516.828082      2.866438\n",
       "std      9981.264932      0.815778\n",
       "min      1300.000000      0.000000\n",
       "25%      7553.500000      2.000000\n",
       "50%      9478.500000      3.000000\n",
       "75%     11601.500000      3.000000\n",
       "max    215245.000000      8.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_columns_of_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "53aGFHaIRs85"
   },
   "outputs": [],
   "source": [
    "y = data['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "9rItp2blSCRu"
   },
   "outputs": [],
   "source": [
    "list_of_features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "jZInyk6wSrbw"
   },
   "outputs": [],
   "source": [
    "X = data[list_of_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "LqcoBUtRSyJE"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1385,
     "status": "ok",
     "timestamp": 1518028371862,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "hVfXLqiKS3Ij",
    "outputId": "e11c7598-5742-402c-f372-5f0df4b06040"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_model = DecisionTreeRegressor()\n",
    "data_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 222,
     "output_extras": [
      {
       "item_id": 1
      },
      {
       "item_id": 2
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1279,
     "status": "ok",
     "timestamp": 1518028374018,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "ZfRfF9QCS-99",
    "outputId": "340df22a-1b0c-4bcb-fbac-4c9dd407400a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making predictions for the following 5 houses:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8450</td>\n",
       "      <td>2003</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9600</td>\n",
       "      <td>1976</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11250</td>\n",
       "      <td>2001</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9550</td>\n",
       "      <td>1915</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14260</td>\n",
       "      <td>2000</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotArea  YearBuilt  1stFlrSF  2ndFlrSF  FullBath  BedroomAbvGr  \\\n",
       "0     8450       2003       856       854         2             3   \n",
       "1     9600       1976      1262         0         2             3   \n",
       "2    11250       2001       920       866         2             3   \n",
       "3     9550       1915       961       756         1             3   \n",
       "4    14260       2000      1145      1053         2             4   \n",
       "\n",
       "   TotRmsAbvGrd  \n",
       "0             8  \n",
       "1             6  \n",
       "2             6  \n",
       "3             7  \n",
       "4             9  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Making predictions for the following 5 houses:\")\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1588,
     "status": "ok",
     "timestamp": 1518028376685,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "V5U1bL0bTH1j",
    "outputId": "7924faef-032a-4234-9784-2bf0b32cd268"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predictions are\n",
      "[ 208500.  181500.  223500.  140000.  250000.]\n"
     ]
    }
   ],
   "source": [
    "print(\"The predictions are\")\n",
    "print(data_model.predict(X.head()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1050,
     "status": "ok",
     "timestamp": 1518028378621,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "F9HQ7zynTQ1Y",
    "outputId": "0d047dc9-ed38-49ef-8645-b6c0ff00c5e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.354337899543388"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "predicted_home_prices = data_model.predict(X)\n",
    "mean_absolute_error(y, predicted_home_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1338,
     "status": "ok",
     "timestamp": 1518028381137,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "l_oDqwsGTgvV",
    "outputId": "3248cf3c-e1a2-451b-cdf3-6c338054b4cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)\n",
    "new_data_model = DecisionTreeRegressor()\n",
    "new_data_model.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1358,
     "status": "ok",
     "timestamp": 1518028383697,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "PYXsESWNUeq-",
    "outputId": "0a510f00-2861-4bcb-924b-794873123eb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32822.0246575\n"
     ]
    }
   ],
   "source": [
    "val_predictions = new_data_model.predict(val_X)\n",
    "print(mean_absolute_error(val_y, val_predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "ui7LxxHPUh_J"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):\n",
    "    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)\n",
    "    model.fit(predictors_train, targ_train)\n",
    "    preds_val = model.predict(predictors_val)\n",
    "    mae = mean_absolute_error(targ_val, preds_val)\n",
    "    return(mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 89,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1246,
     "status": "ok",
     "timestamp": 1518028388172,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "o2icx8YBWmqQ",
    "outputId": "9d17ce9a-1a05-4d4c-d71d-a7dcc783a683"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max leaf nodes: 5  \t\t Mean Absolute Error:  35190\n",
      "Max leaf nodes: 50  \t\t Mean Absolute Error:  27825\n",
      "Max leaf nodes: 500  \t\t Mean Absolute Error:  32662\n",
      "Max leaf nodes: 5000  \t\t Mean Absolute Error:  33382\n"
     ]
    }
   ],
   "source": [
    "for max_leaf_nodes in [5, 50, 500, 5000]:\n",
    "    my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)\n",
    "    print(\"Max leaf nodes: %d  \\t\\t Mean Absolute Error:  %d\" %(max_leaf_nodes, my_mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1129,
     "status": "ok",
     "timestamp": 1518028390137,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "cUeCoOiIWrcU",
    "outputId": "d8690888-6ce6-4c1f-da7a-196e6267f1ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23796.17379\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "forest_model = RandomForestRegressor()\n",
    "forest_model.fit(train_X, train_y)\n",
    "house_preds = forest_model.predict(val_X)\n",
    "print(mean_absolute_error(val_y, house_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1133,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1147,
     "status": "ok",
     "timestamp": 1518028401191,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "K2uL5QOtavJ2",
    "outputId": "06adfeae-f40b-4c3b-a8e8-c25579d5843b",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "MSSubClass          0\n",
       "MSZoning            0\n",
       "LotFrontage       259\n",
       "LotArea             0\n",
       "Street              0\n",
       "Alley            1369\n",
       "LotShape            0\n",
       "LandContour         0\n",
       "Utilities           0\n",
       "LotConfig           0\n",
       "LandSlope           0\n",
       "Neighborhood        0\n",
       "Condition1          0\n",
       "Condition2          0\n",
       "BldgType            0\n",
       "HouseStyle          0\n",
       "OverallQual         0\n",
       "OverallCond         0\n",
       "YearBuilt           0\n",
       "YearRemodAdd        0\n",
       "RoofStyle           0\n",
       "RoofMatl            0\n",
       "Exterior1st         0\n",
       "Exterior2nd         0\n",
       "MasVnrType          8\n",
       "MasVnrArea          8\n",
       "ExterQual           0\n",
       "ExterCond           0\n",
       "Foundation          0\n",
       "                 ... \n",
       "BedroomAbvGr        0\n",
       "KitchenAbvGr        0\n",
       "KitchenQual         0\n",
       "TotRmsAbvGrd        0\n",
       "Functional          0\n",
       "Fireplaces          0\n",
       "FireplaceQu       690\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",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "PoolQC           1453\n",
       "Fence            1179\n",
       "MiscFeature      1406\n",
       "MiscVal             0\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            0\n",
       "SaleCondition       0\n",
       "SalePrice           0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many which columns have how many missing values in them?\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1061,
     "status": "ok",
     "timestamp": 1518028403328,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "wl7VlLlBawVI",
    "outputId": "fe26a219-f21e-43bd-dea8-ed0ead71c2e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "2eFRWUUIbwSD"
   },
   "outputs": [],
   "source": [
    "def score_dataset(train_X, val_X, train_y, val_y):\n",
    "    model = RandomForestRegressor()\n",
    "    model.fit(train_X, train_y)\n",
    "    preds_val = model.predict(val_X)\n",
    "    mae = mean_absolute_error(val_y, preds_val)\n",
    "    return(mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1571,
     "status": "ok",
     "timestamp": 1518028408335,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "NjKTylAda09b",
    "outputId": "a32829e4-3964-4370-afe2-0159a7902bb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error from Imputation:\n",
      "24242.5052185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)\n",
    "\n",
    "my_imputer = Imputer()\n",
    "imputed_train_X = my_imputer.fit_transform(train_X)\n",
    "imputed_val_X = my_imputer.transform(val_X)\n",
    "print(\"Mean Absolute Error from Imputation:\")\n",
    "print(score_dataset(imputed_train_X, imputed_val_X, train_y, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 395,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1103,
     "status": "ok",
     "timestamp": 1518028410347,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "JtcMaNhpc9iW",
    "outputId": "63e4487e-716c-4eb6-92f6-f975a929b18a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExterQual        object\n",
       "Functional       object\n",
       "RoofMatl         object\n",
       "CentralAir       object\n",
       "SaleType         object\n",
       "MSSubClass        int64\n",
       "Foundation       object\n",
       "MiscVal           int64\n",
       "GarageQual       object\n",
       "SaleCondition    object\n",
       "BsmtHalfBath      int64\n",
       "BsmtFinType1     object\n",
       "Exterior2nd      object\n",
       "HalfBath          int64\n",
       "Condition2       object\n",
       "LandContour      object\n",
       "Alley            object\n",
       "KitchenQual      object\n",
       "Street           object\n",
       "GarageArea        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "xFlCJ0WgefCR"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def get_mae(X, y):\n",
    "    # multiple by -1 to make positive MAE score instead of neg value returned as sklearn convention\n",
    "    return -1 * cross_val_score(RandomForestRegressor(50), X, y, scoring = 'neg_mean_absolute_error').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "wOI1_AaeesPX"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop(['SalePrice'], axis=1)\n",
    "y = data.SalePrice\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)\n",
    "\n",
    "\n",
    "one_hot_encoded_training_predictors = pd.get_dummies(train_X.select_dtypes(exclude=['float64']))\n",
    "\n",
    "predictors_without_categoricals = train_X.select_dtypes(exclude=['object', 'float64'])\n",
    "\n",
    "# print (predictors_without_categoricals.dtypes.sample(20))\n",
    "\n",
    "\n",
    "mae_without_categoricals = get_mae(predictors_without_categoricals, train_y)\n",
    "mae_one_hot_encoded = get_mae(one_hot_encoded_training_predictors, train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1254,
     "status": "ok",
     "timestamp": 1518028420179,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "rjCieYy1e99R",
    "outputId": "e77c95d1-bde0-4992-f8fe-df6436be7309"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error when Dropping Categoricals: 18291\n",
      "Mean Abslute Error with One-Hot Encoding: 18080\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error when Dropping Categoricals: ' + str(int(mae_without_categoricals)))\n",
    "print('Mean Abslute Error with One-Hot Encoding: ' + str(int(mae_one_hot_encoded)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "ofHlvFgqgtlp"
   },
   "outputs": [],
   "source": [
    "one_hot_encoded_training_predictors = pd.get_dummies(train_X)\n",
    "one_hot_encoded_test_predictors = pd.get_dummies(val_X)\n",
    "final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors, join='inner', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "2L1j4UAxhDv6"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "data = pd.read_csv('train.csv')\n",
    "data.dropna(axis=0, subset=['SalePrice'], inplace=True)\n",
    "y = data.SalePrice\n",
    "X = data.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])\n",
    "train_X, test_X, train_y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.25)\n",
    "\n",
    "my_imputer = Imputer()\n",
    "train_X = my_imputer.fit_transform(train_X)\n",
    "test_X = my_imputer.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1446,
     "status": "ok",
     "timestamp": 1518028429136,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "BnzJz520hcYV",
    "outputId": "9e44320d-b36d-41af-9365-87a8c1d11224"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error : 16165.6043878\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "my_model = XGBRegressor()\n",
    "my_model.fit(train_X, train_y, verbose=False)\n",
    "\n",
    "# make predictions\n",
    "predictions = my_model.predict(test_X)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(predictions, test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1394,
     "status": "ok",
     "timestamp": 1518028434201,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "P214n95whjLA",
    "outputId": "653e223a-e4d3-42d2-afe3-e2c9f90f8620"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error : 16602.612286\n"
     ]
    }
   ],
   "source": [
    "my_model = XGBRegressor(n_estimators=1000)\n",
    "my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False)\n",
    "\n",
    "predictions = my_model.predict(test_X)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(predictions, test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107,
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1311,
     "status": "ok",
     "timestamp": 1518028444454,
     "user": {
      "displayName": "Soubhik Rakshit",
      "photoUrl": "//lh5.googleusercontent.com/-jpoUdC5mcuk/AAAAAAAAAAI/AAAAAAAAFcE/T7LXfw7K1Jo/s50-c-k-no/photo.jpg",
      "userId": "112712503451559094605"
     },
     "user_tz": -330
    },
    "id": "XRuLJ-2ehwwn",
    "outputId": "ccdfe504-a70e-4a73-8a3f-c22b3dbc678f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error : 16671.5982449\n"
     ]
    }
   ],
   "source": [
    "my_model = XGBRegressor(n_estimators=1000, learning_rate=0.05)\n",
    "my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False)\n",
    "\n",
    "predictions = my_model.predict(test_X)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(predictions, test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "x3NrT7NHi-Dd"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "kaggle.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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