Predicting House Sale Prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Predicting House Sale Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll work with housing data for the city of Ames, Iowa, United States from 2006 to 2010. You can read more about why the data was collected [here](https://doi.org/10.1080/10691898.2011.11889627). You can also read about the different columns in the data [here](https://s3.amazonaws.com/dq-content/307/data_description.txt)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "pd.options.display.max_columns = 999\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "houses = pd.read_csv(\"AmesHousing.tsv\", delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Lot Config</th>\n",
       "      <th>Land Slope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition 1</th>\n",
       "      <th>Condition 2</th>\n",
       "      <th>Bldg Type</th>\n",
       "      <th>House Style</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Roof Style</th>\n",
       "      <th>Roof Matl</th>\n",
       "      <th>Exterior 1st</th>\n",
       "      <th>Exterior 2nd</th>\n",
       "      <th>Mas Vnr Type</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>Exter Qual</th>\n",
       "      <th>Exter Cond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>Bsmt Qual</th>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Heating QC</th>\n",
       "      <th>Central Air</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>Low Qual Fin SF</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <th>Full Bath</th>\n",
       "      <th>Half Bath</th>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <th>Kitchen Qual</th>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Fireplace Qu</th>\n",
       "      <th>Garage Type</th>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <th>Garage Finish</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Garage Qual</th>\n",
       "      <th>Garage Cond</th>\n",
       "      <th>Paved Drive</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>Plywood</td>\n",
       "      <td>Stone</td>\n",
       "      <td>112.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Gd</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>639.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Fa</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1656</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>P</td>\n",
       "      <td>210</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
       "0      1  526301100           20        RL         141.0     31770   Pave   \n",
       "1      2  526350040           20        RH          80.0     11622   Pave   \n",
       "2      3  526351010           20        RL          81.0     14267   Pave   \n",
       "3      4  526353030           20        RL          93.0     11160   Pave   \n",
       "4      5  527105010           60        RL          74.0     13830   Pave   \n",
       "\n",
       "  Alley Lot Shape Land Contour Utilities Lot Config Land Slope Neighborhood  \\\n",
       "0   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
       "1   NaN       Reg          Lvl    AllPub     Inside        Gtl        NAmes   \n",
       "2   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
       "3   NaN       Reg          Lvl    AllPub     Corner        Gtl        NAmes   \n",
       "4   NaN       IR1          Lvl    AllPub     Inside        Gtl      Gilbert   \n",
       "\n",
       "  Condition 1 Condition 2 Bldg Type House Style  Overall Qual  Overall Cond  \\\n",
       "0        Norm        Norm      1Fam      1Story             6             5   \n",
       "1       Feedr        Norm      1Fam      1Story             5             6   \n",
       "2        Norm        Norm      1Fam      1Story             6             6   \n",
       "3        Norm        Norm      1Fam      1Story             7             5   \n",
       "4        Norm        Norm      1Fam      2Story             5             5   \n",
       "\n",
       "   Year Built  Year Remod/Add Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
       "0        1960            1960        Hip   CompShg      BrkFace      Plywood   \n",
       "1        1961            1961      Gable   CompShg      VinylSd      VinylSd   \n",
       "2        1958            1958        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
       "3        1968            1968        Hip   CompShg      BrkFace      BrkFace   \n",
       "4        1997            1998      Gable   CompShg      VinylSd      VinylSd   \n",
       "\n",
       "  Mas Vnr Type  Mas Vnr Area Exter Qual Exter Cond Foundation Bsmt Qual  \\\n",
       "0        Stone         112.0         TA         TA     CBlock        TA   \n",
       "1         None           0.0         TA         TA     CBlock        TA   \n",
       "2      BrkFace         108.0         TA         TA     CBlock        TA   \n",
       "3         None           0.0         Gd         TA     CBlock        TA   \n",
       "4         None           0.0         TA         TA      PConc        Gd   \n",
       "\n",
       "  Bsmt Cond Bsmt Exposure BsmtFin Type 1  BsmtFin SF 1 BsmtFin Type 2  \\\n",
       "0        Gd            Gd            BLQ         639.0            Unf   \n",
       "1        TA            No            Rec         468.0            LwQ   \n",
       "2        TA            No            ALQ         923.0            Unf   \n",
       "3        TA            No            ALQ        1065.0            Unf   \n",
       "4        TA            No            GLQ         791.0            Unf   \n",
       "\n",
       "   BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF Heating Heating QC Central Air  \\\n",
       "0           0.0        441.0         1080.0    GasA         Fa           Y   \n",
       "1         144.0        270.0          882.0    GasA         TA           Y   \n",
       "2           0.0        406.0         1329.0    GasA         TA           Y   \n",
       "3           0.0       1045.0         2110.0    GasA         Ex           Y   \n",
       "4           0.0        137.0          928.0    GasA         Gd           Y   \n",
       "\n",
       "  Electrical  1st Flr SF  2nd Flr SF  Low Qual Fin SF  Gr Liv Area  \\\n",
       "0      SBrkr        1656           0                0         1656   \n",
       "1      SBrkr         896           0                0          896   \n",
       "2      SBrkr        1329           0                0         1329   \n",
       "3      SBrkr        2110           0                0         2110   \n",
       "4      SBrkr         928         701                0         1629   \n",
       "\n",
       "   Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  Bedroom AbvGr  \\\n",
       "0             1.0             0.0          1          0              3   \n",
       "1             0.0             0.0          1          0              2   \n",
       "2             0.0             0.0          1          1              3   \n",
       "3             1.0             0.0          2          1              3   \n",
       "4             0.0             0.0          2          1              3   \n",
       "\n",
       "   Kitchen AbvGr Kitchen Qual  TotRms AbvGrd Functional  Fireplaces  \\\n",
       "0              1           TA              7        Typ           2   \n",
       "1              1           TA              5        Typ           0   \n",
       "2              1           Gd              6        Typ           0   \n",
       "3              1           Ex              8        Typ           2   \n",
       "4              1           TA              6        Typ           1   \n",
       "\n",
       "  Fireplace Qu Garage Type  Garage Yr Blt Garage Finish  Garage Cars  \\\n",
       "0           Gd      Attchd         1960.0           Fin          2.0   \n",
       "1          NaN      Attchd         1961.0           Unf          1.0   \n",
       "2          NaN      Attchd         1958.0           Unf          1.0   \n",
       "3           TA      Attchd         1968.0           Fin          2.0   \n",
       "4           TA      Attchd         1997.0           Fin          2.0   \n",
       "\n",
       "   Garage Area Garage Qual Garage Cond Paved Drive  Wood Deck SF  \\\n",
       "0        528.0          TA          TA           P           210   \n",
       "1        730.0          TA          TA           Y           140   \n",
       "2        312.0          TA          TA           Y           393   \n",
       "3        522.0          TA          TA           Y             0   \n",
       "4        482.0          TA          TA           Y           212   \n",
       "\n",
       "   Open Porch SF  Enclosed Porch  3Ssn Porch  Screen Porch  Pool Area Pool QC  \\\n",
       "0             62               0           0             0          0     NaN   \n",
       "1              0               0           0           120          0     NaN   \n",
       "2             36               0           0             0          0     NaN   \n",
       "3              0               0           0             0          0     NaN   \n",
       "4             34               0           0             0          0     NaN   \n",
       "\n",
       "   Fence Misc Feature  Misc Val  Mo Sold  Yr Sold Sale Type Sale Condition  \\\n",
       "0    NaN          NaN         0        5     2010       WD          Normal   \n",
       "1  MnPrv          NaN         0        6     2010       WD          Normal   \n",
       "2    NaN         Gar2     12500        6     2010       WD          Normal   \n",
       "3    NaN          NaN         0        4     2010       WD          Normal   \n",
       "4  MnPrv          NaN         0        3     2010       WD          Normal   \n",
       "\n",
       "   SalePrice  \n",
       "0     215000  \n",
       "1     105000  \n",
       "2     172000  \n",
       "3     244000  \n",
       "4     189900  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transform_features(df):\n",
    "    return df\n",
    "\n",
    "def select_features(df):\n",
    "    return df[['Gr Liv Area', 'SalePrice']]\n",
    "\n",
    "def train_and_test(df):\n",
    "    train = df[:1460]\n",
    "    test = df[1460:]\n",
    "    \n",
    "    #select numerical columns\n",
    "    numeric_train = train.select_dtypes(include=['integer', 'float'])\n",
    "    numeric_test = test.select_dtypes(include=['integer', 'float'])\n",
    "    \n",
    "    features = numeric_train.columns.drop('SalePrice')\n",
    "    \n",
    "    #train a model\n",
    "    lr = LinearRegression()\n",
    "    lr.fit(train[features], train['SalePrice'])\n",
    "    predictions = lr.predict(test[features])\n",
    "    mse = mean_squared_error(predictions, test['SalePrice'])\n",
    "    rmse = np.sqrt(mse)\n",
    "    \n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57088.25161263909\n"
     ]
    }
   ],
   "source": [
    "transform_df = transform_features(houses)\n",
    "filtered_df = select_features(transform_df)\n",
    "rmse = train_and_test(filtered_df)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# columns with missing values\n",
    "missing = houses.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#drop columns with more than 5% missing values\n",
    "more_than_5 = missing[missing>len(houses)/20].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "houses = houses.drop(more_than_5.index, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(houses.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text_mv_counts = houses.select_dtypes(include=['object']).isnull().sum().sort_values(ascending=False)\n",
    "\n",
    "drop_missing_cols_2 = text_mv_counts[text_mv_counts > 0]\n",
    "\n",
    "houses = houses.drop(drop_missing_cols_2.index, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(houses.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtFin SF 1       1\n",
       "BsmtFin SF 2       1\n",
       "Bsmt Unf SF        1\n",
       "Total Bsmt SF      1\n",
       "Garage Cars        1\n",
       "Garage Area        1\n",
       "Bsmt Full Bath     2\n",
       "Bsmt Half Bath     2\n",
       "Mas Vnr Area      23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for numerical cols, fill in missing values with the most popular value\n",
    "num_missing = houses.select_dtypes(include=['int', 'float']).isnull().sum()\n",
    "fixable_numeric_cols = num_missing[(num_missing < len(houses)/20) & (num_missing > 0)].sort_values()\n",
    "fixable_numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bsmt Full Bath': 0.0,\n",
       " 'Bsmt Half Bath': 0.0,\n",
       " 'Bsmt Unf SF': 0.0,\n",
       " 'BsmtFin SF 1': 0.0,\n",
       " 'BsmtFin SF 2': 0.0,\n",
       " 'Garage Area': 0.0,\n",
       " 'Garage Cars': 2.0,\n",
       " 'Mas Vnr Area': 0.0,\n",
       " 'Total Bsmt SF': 0.0}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replacement_values_dict = houses[fixable_numeric_cols.index].mode().to_dict(orient='records')[0]\n",
    "replacement_values_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "houses = houses.fillna(replacement_values_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order              0\n",
       "PID                0\n",
       "MS SubClass        0\n",
       "MS Zoning          0\n",
       "Lot Area           0\n",
       "Street             0\n",
       "Lot Shape          0\n",
       "Land Contour       0\n",
       "Utilities          0\n",
       "Lot Config         0\n",
       "Land Slope         0\n",
       "Neighborhood       0\n",
       "Condition 1        0\n",
       "Condition 2        0\n",
       "Bldg Type          0\n",
       "House Style        0\n",
       "Overall Qual       0\n",
       "Overall Cond       0\n",
       "Year Built         0\n",
       "Year Remod/Add     0\n",
       "Roof Style         0\n",
       "Roof Matl          0\n",
       "Exterior 1st       0\n",
       "Exterior 2nd       0\n",
       "Mas Vnr Area       0\n",
       "Exter Qual         0\n",
       "Exter Cond         0\n",
       "Foundation         0\n",
       "BsmtFin SF 1       0\n",
       "BsmtFin SF 2       0\n",
       "                  ..\n",
       "Central Air        0\n",
       "1st Flr SF         0\n",
       "2nd Flr SF         0\n",
       "Low Qual Fin SF    0\n",
       "Gr Liv Area        0\n",
       "Bsmt Full Bath     0\n",
       "Bsmt Half Bath     0\n",
       "Full Bath          0\n",
       "Half Bath          0\n",
       "Bedroom AbvGr      0\n",
       "Kitchen AbvGr      0\n",
       "Kitchen Qual       0\n",
       "TotRms AbvGrd      0\n",
       "Functional         0\n",
       "Fireplaces         0\n",
       "Garage Cars        0\n",
       "Garage Area        0\n",
       "Paved Drive        0\n",
       "Wood Deck SF       0\n",
       "Open Porch SF      0\n",
       "Enclosed Porch     0\n",
       "3Ssn Porch         0\n",
       "Screen Porch       0\n",
       "Pool Area          0\n",
       "Misc Val           0\n",
       "Mo Sold            0\n",
       "Yr Sold            0\n",
       "Sale Type          0\n",
       "Sale Condition     0\n",
       "SalePrice          0\n",
       "Length: 64, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create new columns\n",
    "houses['Years Before Sale'] = houses['Yr Sold'] - houses['Year Built']\n",
    "houses['Years Since Remod'] = houses['Yr Sold'] - houses['Year Remod/Add']\n",
    "\n",
    "# Drop rows with negative values for both of these new features\n",
    "houses = houses.drop([1702, 2180, 2181], axis=0)\n",
    "\n",
    "# No longer need original year columns\n",
    "houses = houses.drop([\"Year Built\", \"Year Remod/Add\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop columns that aren't useful for ML\n",
    "houses = houses.drop([\"PID\", \"Order\"], axis=1)\n",
    "\n",
    "# Drop columns that leak info about the final sale\n",
    "houses = houses.drop([\"Mo Sold\", \"Sale Condition\", \"Sale Type\", \"Yr Sold\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#update the transform function\n",
    "\n",
    "def transform_features(df):\n",
    "    num_missing = df.isnull().sum()\n",
    "    drop_missing_cols = num_missing[(num_missing > len(df)/20)].sort_values()\n",
    "    df = df.drop(drop_missing_cols.index, axis=1)\n",
    "    \n",
    "    text_mv_counts = df.select_dtypes(include=['object']).isnull().sum().sort_values(ascending=False)\n",
    "    drop_missing_cols_2 = text_mv_counts[text_mv_counts > 0]\n",
    "    df = df.drop(drop_missing_cols_2.index, axis=1)\n",
    "    \n",
    "    num_missing = df.select_dtypes(include=['int', 'float']).isnull().sum()\n",
    "    fixable_numeric_cols = num_missing[(num_missing < len(df)/20) & (num_missing > 0)].sort_values()\n",
    "    replacement_values_dict = df[fixable_numeric_cols.index].mode().to_dict(orient='records')[0]\n",
    "    df = df.fillna(replacement_values_dict)\n",
    "    \n",
    "    years_sold = df['Yr Sold'] - df['Year Built']\n",
    "    years_since_remod = df['Yr Sold'] - df['Year Remod/Add']\n",
    "    df['Years Before Sale'] = years_sold\n",
    "    df['Years Since Remod'] = years_since_remod\n",
    "    df = df.drop([1702, 2180, 2181], axis=0)\n",
    "\n",
    "    df = df.drop([\"PID\", \"Order\", \"Mo Sold\", \"Sale Condition\", \"Sale Type\", \"Year Built\", \"Year Remod/Add\"], axis=1)\n",
    "    return df\n",
    "\n",
    "def select_features(df):\n",
    "    return df[[\"Gr Liv Area\", \"SalePrice\"]]\n",
    "\n",
    "def train_and_test(df):  \n",
    "    train = df[:1460]\n",
    "    test = df[1460:]\n",
    "    \n",
    "    numeric_train = train.select_dtypes(include=['integer', 'float'])\n",
    "    numeric_test = test.select_dtypes(include=['integer', 'float'])\n",
    "    \n",
    "    ## You can use `pd.Series.drop()` to drop a value.\n",
    "    features = numeric_train.columns.drop(\"SalePrice\")\n",
    "    lr = LinearRegression()\n",
    "    lr.fit(train[features], train[\"SalePrice\"])\n",
    "    predictions = lr.predict(test[features])\n",
    "    mse = mean_squared_error(test[\"SalePrice\"], predictions)\n",
    "    rmse = np.sqrt(mse)\n",
    "    \n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55275.36731241307"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"AmesHousing.tsv\", delimiter=\"\\t\")\n",
    "transform_df = transform_features(df)\n",
    "filtered_df = select_features(transform_df)\n",
    "rmse = train_and_test(filtered_df)\n",
    "\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numerical_df = transform_df.select_dtypes(include=['int', 'float'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr = numerical_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f540e358518>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGoCAYAAADmeoeBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEAgEAoFA6FT+P5yHRnJK0587AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f540e2eb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abs_corr_coeffs = numerical_df.corr()['SalePrice'].abs().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtFin SF 1         0.439284\n",
       "Fireplaces           0.474831\n",
       "TotRms AbvGrd        0.498574\n",
       "Mas Vnr Area         0.506983\n",
       "Years Since Remod    0.534985\n",
       "Full Bath            0.546118\n",
       "Years Before Sale    0.558979\n",
       "1st Flr SF           0.635185\n",
       "Garage Area          0.641425\n",
       "Total Bsmt SF        0.644012\n",
       "Garage Cars          0.648361\n",
       "Gr Liv Area          0.717596\n",
       "Overall Qual         0.801206\n",
       "SalePrice            1.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_corr_coeffs[abs_corr_coeffs > 0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform_df = transform_df.drop(abs_corr_coeffs[abs_corr_coeffs < 0.4].index, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nominal_features = [\"PID\", \"MS SubClass\", \"MS Zoning\", \"Street\", \"Alley\", \"Land Contour\", \"Lot Config\", \"Neighborhood\", \n",
    "                    \"Condition 1\", \"Condition 2\", \"Bldg Type\", \"House Style\", \"Roof Style\", \"Roof Matl\", \"Exterior 1st\", \n",
    "                    \"Exterior 2nd\", \"Mas Vnr Type\", \"Foundation\", \"Heating\", \"Central Air\", \"Garage Type\", \n",
    "                    \"Misc Feature\", \"Sale Type\", \"Sale Condition\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform_cat_cols = []\n",
    "for col in nominal_features:\n",
    "    if col in transform_df.columns:\n",
    "        transform_cat_cols.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# How many unique values in each categorical column?\n",
    "uniqueness_counts = transform_df[transform_cat_cols].apply(lambda col: len(col.value_counts())).sort_values()\n",
    "# Aribtrary cutoff of 10 unique values\n",
    "drop_nonuniq_cols = uniqueness_counts[uniqueness_counts > 10].index\n",
    "transform_df = transform_df.drop(drop_nonuniq_cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select just the remaining text columns and convert to categorical\n",
    "text_cols = transform_df.select_dtypes(include=['object'])\n",
    "for col in text_cols:\n",
    "    transform_df[col] = transform_df[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dummy columns and add back to the dataframe!\n",
    "transform_df = pd.concat([\n",
    "    transform_df, \n",
    "    pd.get_dummies(transform_df.select_dtypes(include=['category']))\n",
    "], axis=1).drop(text_cols,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#update the select_features function\n",
    "\n",
    "def select_features(df, coeff_threshold=0.4, uniq_threshold=10):\n",
    "    \n",
    "    numerical_df = df.select_dtypes(include=['int', 'float'])\n",
    "    abs_corr_coeffs = numerical_df.corr()['SalePrice'].abs().sort_values()\n",
    "    df = df.drop(abs_corr_coeffs[abs_corr_coeffs < coeff_threshold].index, axis=1)\n",
    "    \n",
    "    nominal_features = [\"PID\", \"MS SubClass\", \"MS Zoning\", \"Street\", \"Alley\", \"Land Contour\", \"Lot Config\", \"Neighborhood\", \n",
    "                    \"Condition 1\", \"Condition 2\", \"Bldg Type\", \"House Style\", \"Roof Style\", \"Roof Matl\", \"Exterior 1st\", \n",
    "                    \"Exterior 2nd\", \"Mas Vnr Type\", \"Foundation\", \"Heating\", \"Central Air\", \"Garage Type\", \n",
    "                    \"Misc Feature\", \"Sale Type\", \"Sale Condition\"]\n",
    "    \n",
    "    transform_cat_cols = []\n",
    "    for col in nominal_features:\n",
    "        if col in df.columns:\n",
    "            transform_cat_cols.append(col)\n",
    "\n",
    "    uniqueness_counts = df[transform_cat_cols].apply(lambda col: len(col.value_counts())).sort_values()\n",
    "    drop_nonuniq_cols = uniqueness_counts[uniqueness_counts > 10].index\n",
    "    df = df.drop(drop_nonuniq_cols, axis=1)\n",
    "    \n",
    "    text_cols = df.select_dtypes(include=['object'])\n",
    "    for col in text_cols:\n",
    "        df[col] = df[col].astype('category')\n",
    "    df = pd.concat([df, pd.get_dummies(df.select_dtypes(include=['category']))], axis=1).drop(text_cols,axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#updating the train_and_test function\n",
    "\n",
    "def train_and_test(df, k=0):\n",
    "    \"\"\" the argument k controls the type of cross\n",
    "        validation that occurs\"\"\"\n",
    "    \n",
    "    numeric_df = df.select_dtypes(include=['integer', 'float'])\n",
    "    features = numeric_df.columns.drop(\"SalePrice\")\n",
    "    lr = LinearRegression()\n",
    "    \n",
    "    if k == 0:\n",
    "        ## if k equals 0, perform holdout validation\n",
    "        train = df[:1460]\n",
    "        test = df[1460:]\n",
    "\n",
    "        lr.fit(train[features], train[\"SalePrice\"])\n",
    "        predictions = lr.predict(test[features])\n",
    "        mse = mean_squared_error(test[\"SalePrice\"], predictions)\n",
    "        rmse = np.sqrt(mse)\n",
    "\n",
    "        return rmse\n",
    "    \n",
    "    if k == 1:\n",
    "        ## if k equals 1, perform simple cross validation\n",
    "        \n",
    "        # Randomize rows from `df` and return\n",
    "        shuffled_df = df.sample(frac=1, )\n",
    "        fold_one = df[:1460]\n",
    "        fold_two = df[1460:]\n",
    "        \n",
    "        lr.fit(fold_one[features], fold_one[\"SalePrice\"])\n",
    "        predictions_one = lr.predict(fold_two[features])        \n",
    "        \n",
    "        mse_one = mean_squared_error(fold_two[\"SalePrice\"], predictions_one)\n",
    "        rmse_one = np.sqrt(mse_one)\n",
    "        \n",
    "        lr.fit(fold_two[features], fold_two[\"SalePrice\"])\n",
    "        predictions_two = lr.predict(fold_one[features])        \n",
    "       \n",
    "        mse_two = mean_squared_error(fold_one[\"SalePrice\"], predictions_two)\n",
    "        rmse_two = np.sqrt(mse_two)\n",
    "        \n",
    "        avg_rmse = np.mean([rmse_one, rmse_two])\n",
    "        print(rmse_one)\n",
    "        print(rmse_two)\n",
    "        return avg_rmse\n",
    "    else:\n",
    "        ## if k is greater than 1, implement k-fold cross validation\n",
    "        \n",
    "        kf = KFold(n_splits=k, shuffle=True)\n",
    "        rmse_values = []\n",
    "        \n",
    "        for train_index, test_index, in kf.split(df):\n",
    "            train = df.iloc[train_index]\n",
    "            test = df.iloc[test_index]\n",
    "            \n",
    "            lr.fit(train[features], train[\"SalePrice\"])\n",
    "            predictions = lr.predict(test[features])\n",
    "            \n",
    "            mse = mean_squared_error(test[\"SalePrice\"], predictions)\n",
    "            rmse = np.sqrt(mse)\n",
    "            rmse_values.append(rmse)\n",
    "        print(rmse_values)\n",
    "        avg_rmse = np.mean(rmse_values)\n",
    "        return avg_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24307.33966635315, 28596.61638399279, 36595.73885344297, 26033.114802474483]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "28883.202426565847"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"AmesHousing.tsv\", delimiter=\"\\t\")\n",
    "\n",
    "transform_df = transform_features(df)\n",
    "filtered_df = select_features(transform_df)\n",
    "rmse = train_and_test(filtered_df, k=4)\n",
    "\n",
    "rmse"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}