workbook.2.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering Project: Drivers of Error in Zillow Zestimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Science Team Members: Gabby Broussard and Barbara Marques\n",
    "\n",
    "#### Project Summary:\n",
    "\n",
    "Our team has been asked to analyze Zillow data on single unit/single family properties with transaction dates in 2017 to discover the drivers of errors in Zillow's zestimates. \n",
    "\n",
    "Data Source: Zillow database on Codeup's data server.\n",
    "\n",
    "#### Project Goals:\n",
    "- Build out caching in your python scripts to store the data locally as a csv in order to speed up future data acquisition.\n",
    "- Deliver a notebook presentation of methods used to isolate drivers of error in Zillow Zestimates\n",
    "\n",
    "#### Initial Questions: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='back'></a>\n",
    "### Quick Links to Sections within this Notebook\n",
    "\n",
    "- [Acquire Data](#AD)\n",
    "- [Prepare Data](#PD)\n",
    "- [Split Data](#SD)\n",
    "- [Explore Data](#Explore)\n",
    "- [Hypothesis Testing](#Hypo)\n",
    "- [Scale Data](#Scale)\n",
    "- [Create a Baseline Model](#Baseline)\n",
    "- [Model Data (train & validate)](#Model)\n",
    "- [Model on Test Data](#Test)\n",
    "- [Model Visualizations](#Visual)\n",
    "- [Recommendations & Key Takeaways](#Concl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import wrangle\n",
    "from acquire import get_zillow_data\n",
    "# Statistical Tests\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Visualizing\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "\n",
    "#Modeling Imports\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.cluster import KMeans, dbscan\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression, LassoLars\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "\n",
    "pd.options.display.float_format = '{:20,.2f}'.format\n",
    "pd.set_option('display.max_columns', 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='AD'></a>\n",
    "## Acquire\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "acquired_zillow =get_zillow_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52519, 69)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquired_zillow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>parcelid</th>\n",
       "      <th>airconditioningtypeid</th>\n",
       "      <th>architecturalstyletypeid</th>\n",
       "      <th>basementsqft</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>buildingclasstypeid</th>\n",
       "      <th>buildingqualitytypeid</th>\n",
       "      <th>calculatedbathnbr</th>\n",
       "      <th>decktypeid</th>\n",
       "      <th>finishedfloor1squarefeet</th>\n",
       "      <th>calculatedfinishedsquarefeet</th>\n",
       "      <th>finishedsquarefeet12</th>\n",
       "      <th>finishedsquarefeet13</th>\n",
       "      <th>finishedsquarefeet15</th>\n",
       "      <th>finishedsquarefeet50</th>\n",
       "      <th>finishedsquarefeet6</th>\n",
       "      <th>fips</th>\n",
       "      <th>fireplacecnt</th>\n",
       "      <th>fullbathcnt</th>\n",
       "      <th>garagecarcnt</th>\n",
       "      <th>garagetotalsqft</th>\n",
       "      <th>hashottuborspa</th>\n",
       "      <th>heatingorsystemtypeid</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lotsizesquarefeet</th>\n",
       "      <th>poolcnt</th>\n",
       "      <th>poolsizesum</th>\n",
       "      <th>pooltypeid10</th>\n",
       "      <th>pooltypeid2</th>\n",
       "      <th>pooltypeid7</th>\n",
       "      <th>propertylandusetypeid</th>\n",
       "      <th>rawcensustractandblock</th>\n",
       "      <th>regionidcity</th>\n",
       "      <th>regionidcounty</th>\n",
       "      <th>regionidneighborhood</th>\n",
       "      <th>regionidzip</th>\n",
       "      <th>roomcnt</th>\n",
       "      <th>storytypeid</th>\n",
       "      <th>threequarterbathnbr</th>\n",
       "      <th>typeconstructiontypeid</th>\n",
       "      <th>unitcnt</th>\n",
       "      <th>yardbuildingsqft17</th>\n",
       "      <th>yardbuildingsqft26</th>\n",
       "      <th>yearbuilt</th>\n",
       "      <th>numberofstories</th>\n",
       "      <th>fireplaceflag</th>\n",
       "      <th>structuretaxvaluedollarcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>landtaxvaluedollarcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxdelinquencyyear</th>\n",
       "      <th>censustractandblock</th>\n",
       "      <th>logerror</th>\n",
       "      <th>buildingclassdesc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>13,646.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33,797.00</td>\n",
       "      <td>52,368.00</td>\n",
       "      <td>390.00</td>\n",
       "      <td>4,386.00</td>\n",
       "      <td>52,424.00</td>\n",
       "      <td>52,257.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4,386.00</td>\n",
       "      <td>165.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>7,242.00</td>\n",
       "      <td>52,368.00</td>\n",
       "      <td>18,020.00</td>\n",
       "      <td>18,020.00</td>\n",
       "      <td>1,515.00</td>\n",
       "      <td>33,941.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,143.00</td>\n",
       "      <td>11,097.00</td>\n",
       "      <td>867.00</td>\n",
       "      <td>444.00</td>\n",
       "      <td>1,071.00</td>\n",
       "      <td>10,010.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>51,482.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>19,042.00</td>\n",
       "      <td>52,493.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>6,724.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>33,906.00</td>\n",
       "      <td>1,938.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>52,390.00</td>\n",
       "      <td>14,566.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>52,434.00</td>\n",
       "      <td>52,518.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>52,517.00</td>\n",
       "      <td>52,515.00</td>\n",
       "      <td>2,082.00</td>\n",
       "      <td>52,396.00</td>\n",
       "      <td>52,519.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26,259.00</td>\n",
       "      <td>1,497,295.81</td>\n",
       "      <td>12,995,228.09</td>\n",
       "      <td>2.44</td>\n",
       "      <td>7.10</td>\n",
       "      <td>678.98</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.30</td>\n",
       "      <td>nan</td>\n",
       "      <td>6.27</td>\n",
       "      <td>2.30</td>\n",
       "      <td>66.00</td>\n",
       "      <td>1,545.78</td>\n",
       "      <td>1,922.54</td>\n",
       "      <td>1,924.72</td>\n",
       "      <td>1,180.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,566.52</td>\n",
       "      <td>1,241.08</td>\n",
       "      <td>6,049.13</td>\n",
       "      <td>1.21</td>\n",
       "      <td>2.24</td>\n",
       "      <td>1.95</td>\n",
       "      <td>495.69</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.97</td>\n",
       "      <td>34,023,083.91</td>\n",
       "      <td>-118,194,356.12</td>\n",
       "      <td>11,475.45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>518.32</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>261.00</td>\n",
       "      <td>60,494,406.93</td>\n",
       "      <td>33,484.45</td>\n",
       "      <td>2,524.97</td>\n",
       "      <td>180,224.46</td>\n",
       "      <td>96,636.45</td>\n",
       "      <td>1.89</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>5.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>337.64</td>\n",
       "      <td>229.74</td>\n",
       "      <td>1,963.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.00</td>\n",
       "      <td>196,395.60</td>\n",
       "      <td>529,259.99</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>333,184.86</td>\n",
       "      <td>6,448.59</td>\n",
       "      <td>14.10</td>\n",
       "      <td>60,502,364,233,855.30</td>\n",
       "      <td>0.02</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15,161.07</td>\n",
       "      <td>859,396.93</td>\n",
       "      <td>3,346,502.73</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.67</td>\n",
       "      <td>711.83</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>674.80</td>\n",
       "      <td>1,003.89</td>\n",
       "      <td>1,003.81</td>\n",
       "      <td>169.71</td>\n",
       "      <td>nan</td>\n",
       "      <td>744.38</td>\n",
       "      <td>776.96</td>\n",
       "      <td>21.03</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.55</td>\n",
       "      <td>157.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.56</td>\n",
       "      <td>274,353.69</td>\n",
       "      <td>356,745.57</td>\n",
       "      <td>86,934.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>156.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>208,714.01</td>\n",
       "      <td>49,142.24</td>\n",
       "      <td>803.82</td>\n",
       "      <td>159,813.91</td>\n",
       "      <td>4,601.74</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.03</td>\n",
       "      <td>249.96</td>\n",
       "      <td>190.83</td>\n",
       "      <td>23.15</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>254,188.60</td>\n",
       "      <td>751,455.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>570,199.18</td>\n",
       "      <td>8,751.05</td>\n",
       "      <td>2.39</td>\n",
       "      <td>1,857,672,371,242.34</td>\n",
       "      <td>0.18</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>349.00</td>\n",
       "      <td>10,711,855.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>128.00</td>\n",
       "      <td>128.00</td>\n",
       "      <td>1,060.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>184.00</td>\n",
       "      <td>380.00</td>\n",
       "      <td>6,037.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33,340,620.00</td>\n",
       "      <td>-119,475,416.00</td>\n",
       "      <td>236.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>261.00</td>\n",
       "      <td>60,371,011.10</td>\n",
       "      <td>3,491.00</td>\n",
       "      <td>1,286.00</td>\n",
       "      <td>6,952.00</td>\n",
       "      <td>95,982.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1,878.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>129.00</td>\n",
       "      <td>1,000.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>161.00</td>\n",
       "      <td>49.18</td>\n",
       "      <td>4.00</td>\n",
       "      <td>60,371,011,101,000.00</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13,129.50</td>\n",
       "      <td>757,861.50</td>\n",
       "      <td>11,507,727.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>263.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>1,151.00</td>\n",
       "      <td>1,268.00</td>\n",
       "      <td>1,270.00</td>\n",
       "      <td>1,120.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,152.00</td>\n",
       "      <td>784.00</td>\n",
       "      <td>6,037.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>426.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>33,827,127.50</td>\n",
       "      <td>-118,400,864.50</td>\n",
       "      <td>5,584.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>424.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>261.00</td>\n",
       "      <td>60,374,012.02</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>1,286.00</td>\n",
       "      <td>41,131.00</td>\n",
       "      <td>96,206.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>1,950.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>77,038.00</td>\n",
       "      <td>193,417.25</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>75,815.00</td>\n",
       "      <td>2,652.09</td>\n",
       "      <td>14.00</td>\n",
       "      <td>60,374,013,031,011.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26,259.00</td>\n",
       "      <td>1,500,862.00</td>\n",
       "      <td>12,575,721.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>512.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>1,410.00</td>\n",
       "      <td>1,658.00</td>\n",
       "      <td>1,660.00</td>\n",
       "      <td>1,180.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,414.00</td>\n",
       "      <td>1,008.00</td>\n",
       "      <td>6,037.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>34,024,351.00</td>\n",
       "      <td>-118,153,319.00</td>\n",
       "      <td>6,843.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>261.00</td>\n",
       "      <td>60,376,203.01</td>\n",
       "      <td>24,812.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>118,208.00</td>\n",
       "      <td>96,414.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>280.00</td>\n",
       "      <td>180.00</td>\n",
       "      <td>1,961.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>131,661.50</td>\n",
       "      <td>373,166.50</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>217,720.00</td>\n",
       "      <td>4,643.29</td>\n",
       "      <td>15.00</td>\n",
       "      <td>60,376,203,014,006.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39,388.50</td>\n",
       "      <td>2,241,439.50</td>\n",
       "      <td>14,129,438.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>809.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>1,759.75</td>\n",
       "      <td>2,305.00</td>\n",
       "      <td>2,307.00</td>\n",
       "      <td>1,240.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,770.75</td>\n",
       "      <td>1,320.00</td>\n",
       "      <td>6,059.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>567.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>34,187,660.00</td>\n",
       "      <td>-117,929,317.50</td>\n",
       "      <td>8,800.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>600.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>261.00</td>\n",
       "      <td>60,590,423.26</td>\n",
       "      <td>40,227.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>274,514.00</td>\n",
       "      <td>96,995.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>320.00</td>\n",
       "      <td>1,979.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>226,099.00</td>\n",
       "      <td>618,940.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>408,300.00</td>\n",
       "      <td>7,373.06</td>\n",
       "      <td>15.00</td>\n",
       "      <td>60,590,423,252,001.50</td>\n",
       "      <td>0.04</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52,518.00</td>\n",
       "      <td>2,982,270.00</td>\n",
       "      <td>167,687,839.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>3,560.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>12.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>6,912.00</td>\n",
       "      <td>21,929.00</td>\n",
       "      <td>21,929.00</td>\n",
       "      <td>1,300.00</td>\n",
       "      <td>nan</td>\n",
       "      <td>12,467.00</td>\n",
       "      <td>5,598.00</td>\n",
       "      <td>6,111.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4,251.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>34,818,767.00</td>\n",
       "      <td>-117,554,636.00</td>\n",
       "      <td>6,971,010.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1,500.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>264.00</td>\n",
       "      <td>61,110,091.00</td>\n",
       "      <td>396,556.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>764,167.00</td>\n",
       "      <td>399,675.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3,191.00</td>\n",
       "      <td>868.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9,164,901.00</td>\n",
       "      <td>49,061,236.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>48,952,198.00</td>\n",
       "      <td>586,639.30</td>\n",
       "      <td>99.00</td>\n",
       "      <td>483,030,105,084,015.00</td>\n",
       "      <td>5.26</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Unnamed: 0                   id             parcelid  \\\n",
       "count            52,519.00            52,519.00            52,519.00   \n",
       "mean             26,259.00         1,497,295.81        12,995,228.09   \n",
       "std              15,161.07           859,396.93         3,346,502.73   \n",
       "min                   0.00               349.00        10,711,855.00   \n",
       "25%              13,129.50           757,861.50        11,507,727.50   \n",
       "50%              26,259.00         1,500,862.00        12,575,721.00   \n",
       "75%              39,388.50         2,241,439.50        14,129,438.50   \n",
       "max              52,518.00         2,982,270.00       167,687,839.00   \n",
       "\n",
       "       airconditioningtypeid  architecturalstyletypeid         basementsqft  \\\n",
       "count              13,646.00                     70.00                47.00   \n",
       "mean                    2.44                      7.10               678.98   \n",
       "std                     3.85                      2.67               711.83   \n",
       "min                     1.00                      2.00                38.00   \n",
       "25%                     1.00                      7.00               263.50   \n",
       "50%                     1.00                      7.00               512.00   \n",
       "75%                     1.00                      7.00               809.50   \n",
       "max                    13.00                     21.00             3,560.00   \n",
       "\n",
       "               bathroomcnt           bedroomcnt  buildingclasstypeid  \\\n",
       "count            52,519.00            52,519.00                 0.00   \n",
       "mean                  2.30                 3.30                  nan   \n",
       "std                   1.02                 0.95                  nan   \n",
       "min                   0.00                 0.00                  nan   \n",
       "25%                   2.00                 3.00                  nan   \n",
       "50%                   2.00                 3.00                  nan   \n",
       "75%                   3.00                 4.00                  nan   \n",
       "max                  18.00                14.00                  nan   \n",
       "\n",
       "       buildingqualitytypeid    calculatedbathnbr           decktypeid  \\\n",
       "count              33,797.00            52,368.00               390.00   \n",
       "mean                    6.27                 2.30                66.00   \n",
       "std                     1.72                 1.02                 0.00   \n",
       "min                     1.00                 1.00                66.00   \n",
       "25%                     5.00                 2.00                66.00   \n",
       "50%                     6.00                 2.00                66.00   \n",
       "75%                     8.00                 3.00                66.00   \n",
       "max                    12.00                18.00                66.00   \n",
       "\n",
       "       finishedfloor1squarefeet  calculatedfinishedsquarefeet  \\\n",
       "count                  4,386.00                     52,424.00   \n",
       "mean                   1,545.78                      1,922.54   \n",
       "std                      674.80                      1,003.89   \n",
       "min                      184.00                        128.00   \n",
       "25%                    1,151.00                      1,268.00   \n",
       "50%                    1,410.00                      1,658.00   \n",
       "75%                    1,759.75                      2,305.00   \n",
       "max                    6,912.00                     21,929.00   \n",
       "\n",
       "       finishedsquarefeet12  finishedsquarefeet13  finishedsquarefeet15  \\\n",
       "count             52,257.00                  2.00                  0.00   \n",
       "mean               1,924.72              1,180.00                   nan   \n",
       "std                1,003.81                169.71                   nan   \n",
       "min                  128.00              1,060.00                   nan   \n",
       "25%                1,270.00              1,120.00                   nan   \n",
       "50%                1,660.00              1,180.00                   nan   \n",
       "75%                2,307.00              1,240.00                   nan   \n",
       "max               21,929.00              1,300.00                   nan   \n",
       "\n",
       "       finishedsquarefeet50  finishedsquarefeet6                 fips  \\\n",
       "count              4,386.00               165.00            52,519.00   \n",
       "mean               1,566.52             1,241.08             6,049.13   \n",
       "std                  744.38               776.96                21.03   \n",
       "min                  184.00               380.00             6,037.00   \n",
       "25%                1,152.00               784.00             6,037.00   \n",
       "50%                1,414.00             1,008.00             6,037.00   \n",
       "75%                1,770.75             1,320.00             6,059.00   \n",
       "max               12,467.00             5,598.00             6,111.00   \n",
       "\n",
       "              fireplacecnt          fullbathcnt         garagecarcnt  \\\n",
       "count             7,242.00            52,368.00            18,020.00   \n",
       "mean                  1.21                 2.24                 1.95   \n",
       "std                   0.51                 0.99                 0.55   \n",
       "min                   1.00                 1.00                 0.00   \n",
       "25%                   1.00                 2.00                 2.00   \n",
       "50%                   1.00                 2.00                 2.00   \n",
       "75%                   1.00                 3.00                 2.00   \n",
       "max                   5.00                18.00                14.00   \n",
       "\n",
       "           garagetotalsqft       hashottuborspa  heatingorsystemtypeid  \\\n",
       "count            18,020.00             1,515.00              33,941.00   \n",
       "mean                495.69                 1.00                   3.97   \n",
       "std                 157.32                 0.00                   2.56   \n",
       "min                   0.00                 1.00                   1.00   \n",
       "25%                 426.00                 1.00                   2.00   \n",
       "50%                 462.00                 1.00                   2.00   \n",
       "75%                 567.00                 1.00                   7.00   \n",
       "max               4,251.00                 1.00                  24.00   \n",
       "\n",
       "                  latitude            longitude    lotsizesquarefeet  \\\n",
       "count            52,519.00            52,519.00            52,143.00   \n",
       "mean         34,023,083.91      -118,194,356.12            11,475.45   \n",
       "std             274,353.69           356,745.57            86,934.14   \n",
       "min          33,340,620.00      -119,475,416.00               236.00   \n",
       "25%          33,827,127.50      -118,400,864.50             5,584.00   \n",
       "50%          34,024,351.00      -118,153,319.00             6,843.00   \n",
       "75%          34,187,660.00      -117,929,317.50             8,800.00   \n",
       "max          34,818,767.00      -117,554,636.00         6,971,010.00   \n",
       "\n",
       "                   poolcnt          poolsizesum         pooltypeid10  \\\n",
       "count            11,097.00               867.00               444.00   \n",
       "mean                  1.00               518.32                 1.00   \n",
       "std                   0.00               156.47                 0.00   \n",
       "min                   1.00                24.00                 1.00   \n",
       "25%                   1.00               424.50                 1.00   \n",
       "50%                   1.00               500.00                 1.00   \n",
       "75%                   1.00               600.00                 1.00   \n",
       "max                   1.00             1,500.00                 1.00   \n",
       "\n",
       "               pooltypeid2          pooltypeid7  propertylandusetypeid  \\\n",
       "count             1,071.00            10,010.00              52,519.00   \n",
       "mean                  1.00                 1.00                 261.00   \n",
       "std                   0.00                 0.00                   0.08   \n",
       "min                   1.00                 1.00                 261.00   \n",
       "25%                   1.00                 1.00                 261.00   \n",
       "50%                   1.00                 1.00                 261.00   \n",
       "75%                   1.00                 1.00                 261.00   \n",
       "max                   1.00                 1.00                 264.00   \n",
       "\n",
       "       rawcensustractandblock         regionidcity       regionidcounty  \\\n",
       "count               52,519.00            51,482.00            52,519.00   \n",
       "mean            60,494,406.93            33,484.45             2,524.97   \n",
       "std                208,714.01            49,142.24               803.82   \n",
       "min             60,371,011.10             3,491.00             1,286.00   \n",
       "25%             60,374,012.02            12,447.00             1,286.00   \n",
       "50%             60,376,203.01            24,812.00             3,101.00   \n",
       "75%             60,590,423.26            40,227.00             3,101.00   \n",
       "max             61,110,091.00           396,556.00             3,101.00   \n",
       "\n",
       "       regionidneighborhood          regionidzip              roomcnt  \\\n",
       "count             19,042.00            52,493.00            52,519.00   \n",
       "mean             180,224.46            96,636.45                 1.89   \n",
       "std              159,813.91             4,601.74                 3.14   \n",
       "min                6,952.00            95,982.00                 0.00   \n",
       "25%               41,131.00            96,206.00                 0.00   \n",
       "50%              118,208.00            96,414.00                 0.00   \n",
       "75%              274,514.00            96,995.00                 5.00   \n",
       "max              764,167.00           399,675.00                15.00   \n",
       "\n",
       "               storytypeid  threequarterbathnbr  typeconstructiontypeid  \\\n",
       "count                47.00             6,724.00                   76.00   \n",
       "mean                  7.00                 1.01                    5.97   \n",
       "std                   0.00                 0.14                    0.23   \n",
       "min                   7.00                 1.00                    4.00   \n",
       "25%                   7.00                 1.00                    6.00   \n",
       "50%                   7.00                 1.00                    6.00   \n",
       "75%                   7.00                 1.00                    6.00   \n",
       "max                   7.00                 7.00                    6.00   \n",
       "\n",
       "                   unitcnt   yardbuildingsqft17   yardbuildingsqft26  \\\n",
       "count            33,906.00             1,938.00                65.00   \n",
       "mean                  1.00               337.64               229.74   \n",
       "std                   0.03               249.96               190.83   \n",
       "min                   1.00                11.00                12.00   \n",
       "25%                   1.00               200.00                72.00   \n",
       "50%                   1.00               280.00               180.00   \n",
       "75%                   1.00               400.00               320.00   \n",
       "max                   3.00             3,191.00               868.00   \n",
       "\n",
       "                 yearbuilt      numberofstories        fireplaceflag  \\\n",
       "count            52,390.00            14,566.00                81.00   \n",
       "mean              1,963.43                 1.41                 1.00   \n",
       "std                  23.15                 0.54                 0.00   \n",
       "min               1,878.00                 1.00                 1.00   \n",
       "25%               1,950.00                 1.00                 1.00   \n",
       "50%               1,961.00                 1.00                 1.00   \n",
       "75%               1,979.00                 2.00                 1.00   \n",
       "max               2,016.00                 6.00                 1.00   \n",
       "\n",
       "       structuretaxvaluedollarcnt    taxvaluedollarcnt       assessmentyear  \\\n",
       "count                   52,434.00            52,518.00            52,519.00   \n",
       "mean                   196,395.60           529,259.99             2,016.00   \n",
       "std                    254,188.60           751,455.76                 0.00   \n",
       "min                        129.00             1,000.00             2,016.00   \n",
       "25%                     77,038.00           193,417.25             2,016.00   \n",
       "50%                    131,661.50           373,166.50             2,016.00   \n",
       "75%                    226,099.00           618,940.00             2,016.00   \n",
       "max                  9,164,901.00        49,061,236.00             2,016.00   \n",
       "\n",
       "       landtaxvaluedollarcnt            taxamount   taxdelinquencyyear  \\\n",
       "count              52,517.00            52,515.00             2,082.00   \n",
       "mean              333,184.86             6,448.59                14.10   \n",
       "std               570,199.18             8,751.05                 2.39   \n",
       "min                   161.00                49.18                 4.00   \n",
       "25%                75,815.00             2,652.09                14.00   \n",
       "50%               217,720.00             4,643.29                15.00   \n",
       "75%               408,300.00             7,373.06                15.00   \n",
       "max            48,952,198.00           586,639.30                99.00   \n",
       "\n",
       "         censustractandblock             logerror    buildingclassdesc  \n",
       "count              52,396.00            52,519.00                 0.00  \n",
       "mean   60,502,364,233,855.30                 0.02                  nan  \n",
       "std     1,857,672,371,242.34                 0.18                  nan  \n",
       "min    60,371,011,101,000.00                -4.66                  nan  \n",
       "25%    60,374,013,031,011.00                -0.02                  nan  \n",
       "50%    60,376,203,014,006.00                 0.01                  nan  \n",
       "75%    60,590,423,252,001.50                 0.04                  nan  \n",
       "max   483,030,105,084,015.00                 5.26                  nan  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acquired_zillow.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finishedsquarefeet15    52519\n",
       "buildingclassdesc       52519\n",
       "buildingclasstypeid     52519\n",
       "finishedsquarefeet13    52517\n",
       "storydesc               52472\n",
       "                        ...  \n",
       "assessmentyear              0\n",
       "logerror                    0\n",
       "transactiondate             0\n",
       "propertylandusedesc         0\n",
       "Unnamed: 0                  0\n",
       "Length: 69, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_counts = acquired_zillow.isnull().sum().sort_values(ascending=False)\n",
    "null_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Takeaways:\n",
    "##### There are numerous null values that need to be handled\n",
    "#### Columns with too many nulls need to be dropped from the data frame.\n",
    "##### There are many outliers so they either need to be removed or handled by using a RobustScaler when scaling the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='PD'></a>\n",
    "## Prepare\n",
    "\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= wrangle.wrangle_zillow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>buildingquality</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>fips</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>regionidcity</th>\n",
       "      <th>regionidcounty</th>\n",
       "      <th>regionidzip</th>\n",
       "      <th>roomcnt</th>\n",
       "      <th>unit_count</th>\n",
       "      <th>age</th>\n",
       "      <th>structure_tax_value</th>\n",
       "      <th>tax_value</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>land_tax_value</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>logerror</th>\n",
       "      <th>transactiondate</th>\n",
       "      <th>heating_system</th>\n",
       "      <th>county</th>\n",
       "      <th>age_bin</th>\n",
       "      <th>taxrate</th>\n",
       "      <th>acres</th>\n",
       "      <th>acres_bin</th>\n",
       "      <th>tax_value_bin</th>\n",
       "      <th>land_tax_value_bin</th>\n",
       "      <th>sqft_bin</th>\n",
       "      <th>structure_dollar_per_sqft</th>\n",
       "      <th>structure_dollar_sqft_bin</th>\n",
       "      <th>land_dollar_per_sqft</th>\n",
       "      <th>lot_dollar_sqft_bin</th>\n",
       "      <th>bath_bed_ratio</th>\n",
       "      <th>cola</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10711855</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2,107.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,222,559.00</td>\n",
       "      <td>-118,617,387.00</td>\n",
       "      <td>9,158.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>249,655.00</td>\n",
       "      <td>624,139.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>374,484.00</td>\n",
       "      <td>7,659.36</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>$650,000</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>118.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>40.89</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10711877</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,220,261.00</td>\n",
       "      <td>-118,616,409.00</td>\n",
       "      <td>9,035.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>253,000.00</td>\n",
       "      <td>660,000.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>407,000.00</td>\n",
       "      <td>8,123.91</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>$900,000</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>134.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>45.05</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10711888</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,222,491.00</td>\n",
       "      <td>-118,616,854.00</td>\n",
       "      <td>9,800.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>257,591.00</td>\n",
       "      <td>542,923.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>285,332.00</td>\n",
       "      <td>6,673.24</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.20</td>\n",
       "      <td>$550,000</td>\n",
       "      <td>$350,000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>136.87</td>\n",
       "      <td>0.40</td>\n",
       "      <td>29.12</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10711910</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,477.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,221,864.00</td>\n",
       "      <td>-118,615,739.00</td>\n",
       "      <td>11,285.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>57,968.00</td>\n",
       "      <td>78,031.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>20,063.00</td>\n",
       "      <td>1,116.46</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>50-60</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>&lt; $80,000</td>\n",
       "      <td>&lt; $50,000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>39.25</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10711923</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,918.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,220,619.00</td>\n",
       "      <td>-118,615,253.00</td>\n",
       "      <td>11,239.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>167,869.00</td>\n",
       "      <td>415,459.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>247,590.00</td>\n",
       "      <td>5,239.85</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>2017-03-24</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>50-60</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>$250,000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>87.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>22.03</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   parcelid            bathrooms             bedrooms      buildingquality  \\\n",
       "0  10711855                 2.00                 3.00                 8.00   \n",
       "1  10711877                 2.00                 4.00                 8.00   \n",
       "2  10711888                 2.00                 4.00                 8.00   \n",
       "3  10711910                 2.00                 3.00                 8.00   \n",
       "4  10711923                 2.00                 4.00                 8.00   \n",
       "\n",
       "           square_feet  fips             latitude            longitude  \\\n",
       "0             2,107.00  6037        34,222,559.00      -118,617,387.00   \n",
       "1             1,882.00  6037        34,220,261.00      -118,616,409.00   \n",
       "2             1,882.00  6037        34,222,491.00      -118,616,854.00   \n",
       "3             1,477.00  6037        34,221,864.00      -118,615,739.00   \n",
       "4             1,918.00  6037        34,220,619.00      -118,615,253.00   \n",
       "\n",
       "              lot_size         regionidcity       regionidcounty  \\\n",
       "0             9,158.00            12,447.00             3,101.00   \n",
       "1             9,035.00            12,447.00             3,101.00   \n",
       "2             9,800.00            12,447.00             3,101.00   \n",
       "3            11,285.00            12,447.00             3,101.00   \n",
       "4            11,239.00            12,447.00             3,101.00   \n",
       "\n",
       "           regionidzip              roomcnt           unit_count  \\\n",
       "0            96,339.00                 0.00                 1.00   \n",
       "1            96,339.00                 0.00                 1.00   \n",
       "2            96,339.00                 0.00                 1.00   \n",
       "3            96,339.00                 0.00                 1.00   \n",
       "4            96,339.00                 0.00                 1.00   \n",
       "\n",
       "                   age  structure_tax_value            tax_value  \\\n",
       "0                45.00           249,655.00           624,139.00   \n",
       "1                45.00           253,000.00           660,000.00   \n",
       "2                45.00           257,591.00           542,923.00   \n",
       "3                57.00            57,968.00            78,031.00   \n",
       "4                57.00           167,869.00           415,459.00   \n",
       "\n",
       "        assessmentyear       land_tax_value            taxamount  \\\n",
       "0             2,016.00           374,484.00             7,659.36   \n",
       "1             2,016.00           407,000.00             8,123.91   \n",
       "2             2,016.00           285,332.00             6,673.24   \n",
       "3             2,016.00            20,063.00             1,116.46   \n",
       "4             2,016.00           247,590.00             5,239.85   \n",
       "\n",
       "              logerror transactiondate heating_system       county age_bin  \\\n",
       "0                -0.01      2017-07-07        Central  Los_Angeles   40-50   \n",
       "1                 0.02      2017-08-29        Central  Los_Angeles   40-50   \n",
       "2                 0.08      2017-04-04        Central  Los_Angeles   40-50   \n",
       "3                -0.04      2017-03-17        Central  Los_Angeles   50-60   \n",
       "4                -0.01      2017-03-24        Central  Los_Angeles   50-60   \n",
       "\n",
       "               taxrate                acres            acres_bin  \\\n",
       "0                 1.23                 0.21                 0.20   \n",
       "1                 1.23                 0.21                 0.20   \n",
       "2                 1.23                 0.22                 0.20   \n",
       "3                 1.43                 0.26                 0.30   \n",
       "4                 1.26                 0.26                 0.30   \n",
       "\n",
       "  tax_value_bin land_tax_value_bin             sqft_bin  \\\n",
       "0      $650,000           $450,000                 0.50   \n",
       "1      $900,000           $450,000                 0.40   \n",
       "2      $550,000           $350,000                 0.40   \n",
       "3     < $80,000          < $50,000                 0.30   \n",
       "4      $450,000           $250,000                 0.40   \n",
       "\n",
       "   structure_dollar_per_sqft  structure_dollar_sqft_bin  land_dollar_per_sqft  \\\n",
       "0                     118.49                       0.40                 40.89   \n",
       "1                     134.43                       0.40                 45.05   \n",
       "2                     136.87                       0.40                 29.12   \n",
       "3                      39.25                       0.10                  1.78   \n",
       "4                      87.52                       0.30                 22.03   \n",
       "\n",
       "  lot_dollar_sqft_bin       bath_bed_ratio  cola  \n",
       "0               20-49                 0.67     1  \n",
       "1               20-49                 0.50     1  \n",
       "2               20-49                 0.50     1  \n",
       "3                   1                 0.67     1  \n",
       "4               20-49                 0.50     1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>parcelid</th>\n",
       "      <td>10711855</td>\n",
       "      <td>10711877</td>\n",
       "      <td>10711888</td>\n",
       "      <td>10711910</td>\n",
       "      <td>10711923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buildingquality</th>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>square_feet</th>\n",
       "      <td>2,107.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>1,477.00</td>\n",
       "      <td>1,918.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>34,222,559.00</td>\n",
       "      <td>34,220,261.00</td>\n",
       "      <td>34,222,491.00</td>\n",
       "      <td>34,221,864.00</td>\n",
       "      <td>34,220,619.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>-118,617,387.00</td>\n",
       "      <td>-118,616,409.00</td>\n",
       "      <td>-118,616,854.00</td>\n",
       "      <td>-118,615,739.00</td>\n",
       "      <td>-118,615,253.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot_size</th>\n",
       "      <td>9,158.00</td>\n",
       "      <td>9,035.00</td>\n",
       "      <td>9,800.00</td>\n",
       "      <td>11,285.00</td>\n",
       "      <td>11,239.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionidcity</th>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionidcounty</th>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionidzip</th>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roomcnt</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_count</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>45.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>57.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>structure_tax_value</th>\n",
       "      <td>249,655.00</td>\n",
       "      <td>253,000.00</td>\n",
       "      <td>257,591.00</td>\n",
       "      <td>57,968.00</td>\n",
       "      <td>167,869.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax_value</th>\n",
       "      <td>624,139.00</td>\n",
       "      <td>660,000.00</td>\n",
       "      <td>542,923.00</td>\n",
       "      <td>78,031.00</td>\n",
       "      <td>415,459.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assessmentyear</th>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_tax_value</th>\n",
       "      <td>374,484.00</td>\n",
       "      <td>407,000.00</td>\n",
       "      <td>285,332.00</td>\n",
       "      <td>20,063.00</td>\n",
       "      <td>247,590.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taxamount</th>\n",
       "      <td>7,659.36</td>\n",
       "      <td>8,123.91</td>\n",
       "      <td>6,673.24</td>\n",
       "      <td>1,116.46</td>\n",
       "      <td>5,239.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logerror</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactiondate</th>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>2017-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heating_system</th>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_bin</th>\n",
       "      <td>40-50</td>\n",
       "      <td>40-50</td>\n",
       "      <td>40-50</td>\n",
       "      <td>50-60</td>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taxrate</th>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acres</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acres_bin</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax_value_bin</th>\n",
       "      <td>$650,000</td>\n",
       "      <td>$900,000</td>\n",
       "      <td>$550,000</td>\n",
       "      <td>&lt; $80,000</td>\n",
       "      <td>$450,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_tax_value_bin</th>\n",
       "      <td>$450,000</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>$350,000</td>\n",
       "      <td>&lt; $50,000</td>\n",
       "      <td>$250,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_bin</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>structure_dollar_per_sqft</th>\n",
       "      <td>118.49</td>\n",
       "      <td>134.43</td>\n",
       "      <td>136.87</td>\n",
       "      <td>39.25</td>\n",
       "      <td>87.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>structure_dollar_sqft_bin</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_dollar_per_sqft</th>\n",
       "      <td>40.89</td>\n",
       "      <td>45.05</td>\n",
       "      <td>29.12</td>\n",
       "      <td>1.78</td>\n",
       "      <td>22.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot_dollar_sqft_bin</th>\n",
       "      <td>20-49</td>\n",
       "      <td>20-49</td>\n",
       "      <td>20-49</td>\n",
       "      <td>1</td>\n",
       "      <td>20-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bath_bed_ratio</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cola</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             0                    1  \\\n",
       "parcelid                              10711855             10711877   \n",
       "bathrooms                                 2.00                 2.00   \n",
       "bedrooms                                  3.00                 4.00   \n",
       "buildingquality                           8.00                 8.00   \n",
       "square_feet                           2,107.00             1,882.00   \n",
       "fips                                      6037                 6037   \n",
       "latitude                         34,222,559.00        34,220,261.00   \n",
       "longitude                      -118,617,387.00      -118,616,409.00   \n",
       "lot_size                              9,158.00             9,035.00   \n",
       "regionidcity                         12,447.00            12,447.00   \n",
       "regionidcounty                        3,101.00             3,101.00   \n",
       "regionidzip                          96,339.00            96,339.00   \n",
       "roomcnt                                   0.00                 0.00   \n",
       "unit_count                                1.00                 1.00   \n",
       "age                                      45.00                45.00   \n",
       "structure_tax_value                 249,655.00           253,000.00   \n",
       "tax_value                           624,139.00           660,000.00   \n",
       "assessmentyear                        2,016.00             2,016.00   \n",
       "land_tax_value                      374,484.00           407,000.00   \n",
       "taxamount                             7,659.36             8,123.91   \n",
       "logerror                                 -0.01                 0.02   \n",
       "transactiondate                     2017-07-07           2017-08-29   \n",
       "heating_system                         Central              Central   \n",
       "county                             Los_Angeles          Los_Angeles   \n",
       "age_bin                                  40-50                40-50   \n",
       "taxrate                                   1.23                 1.23   \n",
       "acres                                     0.21                 0.21   \n",
       "acres_bin                                 0.20                 0.20   \n",
       "tax_value_bin                         $650,000             $900,000   \n",
       "land_tax_value_bin                    $450,000             $450,000   \n",
       "sqft_bin                                  0.50                 0.40   \n",
       "structure_dollar_per_sqft               118.49               134.43   \n",
       "structure_dollar_sqft_bin                 0.40                 0.40   \n",
       "land_dollar_per_sqft                     40.89                45.05   \n",
       "lot_dollar_sqft_bin                      20-49                20-49   \n",
       "bath_bed_ratio                            0.67                 0.50   \n",
       "cola                                         1                    1   \n",
       "\n",
       "                                             2                    3  \\\n",
       "parcelid                              10711888             10711910   \n",
       "bathrooms                                 2.00                 2.00   \n",
       "bedrooms                                  4.00                 3.00   \n",
       "buildingquality                           8.00                 8.00   \n",
       "square_feet                           1,882.00             1,477.00   \n",
       "fips                                      6037                 6037   \n",
       "latitude                         34,222,491.00        34,221,864.00   \n",
       "longitude                      -118,616,854.00      -118,615,739.00   \n",
       "lot_size                              9,800.00            11,285.00   \n",
       "regionidcity                         12,447.00            12,447.00   \n",
       "regionidcounty                        3,101.00             3,101.00   \n",
       "regionidzip                          96,339.00            96,339.00   \n",
       "roomcnt                                   0.00                 0.00   \n",
       "unit_count                                1.00                 1.00   \n",
       "age                                      45.00                57.00   \n",
       "structure_tax_value                 257,591.00            57,968.00   \n",
       "tax_value                           542,923.00            78,031.00   \n",
       "assessmentyear                        2,016.00             2,016.00   \n",
       "land_tax_value                      285,332.00            20,063.00   \n",
       "taxamount                             6,673.24             1,116.46   \n",
       "logerror                                  0.08                -0.04   \n",
       "transactiondate                     2017-04-04           2017-03-17   \n",
       "heating_system                         Central              Central   \n",
       "county                             Los_Angeles          Los_Angeles   \n",
       "age_bin                                  40-50                50-60   \n",
       "taxrate                                   1.23                 1.43   \n",
       "acres                                     0.22                 0.26   \n",
       "acres_bin                                 0.20                 0.30   \n",
       "tax_value_bin                         $550,000            < $80,000   \n",
       "land_tax_value_bin                    $350,000            < $50,000   \n",
       "sqft_bin                                  0.40                 0.30   \n",
       "structure_dollar_per_sqft               136.87                39.25   \n",
       "structure_dollar_sqft_bin                 0.40                 0.10   \n",
       "land_dollar_per_sqft                     29.12                 1.78   \n",
       "lot_dollar_sqft_bin                      20-49                    1   \n",
       "bath_bed_ratio                            0.50                 0.67   \n",
       "cola                                         1                    1   \n",
       "\n",
       "                                             4  \n",
       "parcelid                              10711923  \n",
       "bathrooms                                 2.00  \n",
       "bedrooms                                  4.00  \n",
       "buildingquality                           8.00  \n",
       "square_feet                           1,918.00  \n",
       "fips                                      6037  \n",
       "latitude                         34,220,619.00  \n",
       "longitude                      -118,615,253.00  \n",
       "lot_size                             11,239.00  \n",
       "regionidcity                         12,447.00  \n",
       "regionidcounty                        3,101.00  \n",
       "regionidzip                          96,339.00  \n",
       "roomcnt                                   0.00  \n",
       "unit_count                                1.00  \n",
       "age                                      57.00  \n",
       "structure_tax_value                 167,869.00  \n",
       "tax_value                           415,459.00  \n",
       "assessmentyear                        2,016.00  \n",
       "land_tax_value                      247,590.00  \n",
       "taxamount                             5,239.85  \n",
       "logerror                                 -0.01  \n",
       "transactiondate                     2017-03-24  \n",
       "heating_system                         Central  \n",
       "county                             Los_Angeles  \n",
       "age_bin                                  50-60  \n",
       "taxrate                                   1.26  \n",
       "acres                                     0.26  \n",
       "acres_bin                                 0.30  \n",
       "tax_value_bin                         $450,000  \n",
       "land_tax_value_bin                    $250,000  \n",
       "sqft_bin                                  0.40  \n",
       "structure_dollar_per_sqft                87.52  \n",
       "structure_dollar_sqft_bin                 0.30  \n",
       "land_dollar_per_sqft                     22.03  \n",
       "lot_dollar_sqft_bin                      20-49  \n",
       "bath_bed_ratio                            0.50  \n",
       "cola                                         1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the first 5 rows of our new dataframe, transposed\n",
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50819 entries, 0 to 52504\n",
      "Data columns (total 37 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   parcelid                   50819 non-null  int64   \n",
      " 1   bathrooms                  50819 non-null  float64 \n",
      " 2   bedrooms                   50819 non-null  float64 \n",
      " 3   buildingquality            50819 non-null  float64 \n",
      " 4   square_feet                50819 non-null  float64 \n",
      " 5   fips                       50819 non-null  int64   \n",
      " 6   latitude                   50819 non-null  float64 \n",
      " 7   longitude                  50819 non-null  float64 \n",
      " 8   lot_size                   50819 non-null  float64 \n",
      " 9   regionidcity               50819 non-null  float64 \n",
      " 10  regionidcounty             50819 non-null  float64 \n",
      " 11  regionidzip                50819 non-null  float64 \n",
      " 12  roomcnt                    50819 non-null  float64 \n",
      " 13  unit_count                 50819 non-null  float64 \n",
      " 14  age                        50819 non-null  float64 \n",
      " 15  structure_tax_value        50819 non-null  float64 \n",
      " 16  tax_value                  50819 non-null  float64 \n",
      " 17  assessmentyear             50819 non-null  float64 \n",
      " 18  land_tax_value             50819 non-null  float64 \n",
      " 19  taxamount                  50819 non-null  float64 \n",
      " 20  logerror                   50819 non-null  float64 \n",
      " 21  transactiondate            50819 non-null  object  \n",
      " 22  heating_system             50819 non-null  object  \n",
      " 23  county                     50819 non-null  object  \n",
      " 24  age_bin                    50819 non-null  category\n",
      " 25  taxrate                    50819 non-null  float64 \n",
      " 26  acres                      50819 non-null  float64 \n",
      " 27  acres_bin                  50819 non-null  float64 \n",
      " 28  tax_value_bin              50819 non-null  category\n",
      " 29  land_tax_value_bin         48606 non-null  category\n",
      " 30  sqft_bin                   50819 non-null  float64 \n",
      " 31  structure_dollar_per_sqft  50819 non-null  float64 \n",
      " 32  structure_dollar_sqft_bin  50819 non-null  float64 \n",
      " 33  land_dollar_per_sqft       50819 non-null  float64 \n",
      " 34  lot_dollar_sqft_bin        50819 non-null  category\n",
      " 35  bath_bed_ratio             50819 non-null  float64 \n",
      " 36  cola                       50819 non-null  int64   \n",
      "dtypes: category(4), float64(27), int64(3), object(3)\n",
      "memory usage: 13.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2213"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are no remaining null values\n",
    "\n",
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating some new features:\n",
    "- bins for: age, square feet\n",
    "- tax rate\n",
    "- bath/bed ratio:  (bathroom count / bedroom count)\n",
    "- acres (lotsizesquarefeet/43560)\n",
    "- dollar per square foot\n",
    "- within city of Los Angeles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>parcelid</th>\n",
       "      <td>10711855</td>\n",
       "      <td>10711877</td>\n",
       "      <td>10711888</td>\n",
       "      <td>10711910</td>\n",
       "      <td>10711923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buildingquality</th>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>square_feet</th>\n",
       "      <td>2,107.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>1,477.00</td>\n",
       "      <td>1,918.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "      <td>6037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>34,222,559.00</td>\n",
       "      <td>34,220,261.00</td>\n",
       "      <td>34,222,491.00</td>\n",
       "      <td>34,221,864.00</td>\n",
       "      <td>34,220,619.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>-118,617,387.00</td>\n",
       "      <td>-118,616,409.00</td>\n",
       "      <td>-118,616,854.00</td>\n",
       "      <td>-118,615,739.00</td>\n",
       "      <td>-118,615,253.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot_size</th>\n",
       "      <td>9,158.00</td>\n",
       "      <td>9,035.00</td>\n",
       "      <td>9,800.00</td>\n",
       "      <td>11,285.00</td>\n",
       "      <td>11,239.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionidcity</th>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>12,447.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionidcounty</th>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>3,101.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionidzip</th>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>96,339.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roomcnt</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_count</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>45.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>57.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>structure_tax_value</th>\n",
       "      <td>249,655.00</td>\n",
       "      <td>253,000.00</td>\n",
       "      <td>257,591.00</td>\n",
       "      <td>57,968.00</td>\n",
       "      <td>167,869.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax_value</th>\n",
       "      <td>624,139.00</td>\n",
       "      <td>660,000.00</td>\n",
       "      <td>542,923.00</td>\n",
       "      <td>78,031.00</td>\n",
       "      <td>415,459.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assessmentyear</th>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,016.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_tax_value</th>\n",
       "      <td>374,484.00</td>\n",
       "      <td>407,000.00</td>\n",
       "      <td>285,332.00</td>\n",
       "      <td>20,063.00</td>\n",
       "      <td>247,590.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taxamount</th>\n",
       "      <td>7,659.36</td>\n",
       "      <td>8,123.91</td>\n",
       "      <td>6,673.24</td>\n",
       "      <td>1,116.46</td>\n",
       "      <td>5,239.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logerror</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactiondate</th>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>2017-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heating_system</th>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>Los_Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_bin</th>\n",
       "      <td>40-50</td>\n",
       "      <td>40-50</td>\n",
       "      <td>40-50</td>\n",
       "      <td>50-60</td>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taxrate</th>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acres</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acres_bin</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax_value_bin</th>\n",
       "      <td>$650,000</td>\n",
       "      <td>$900,000</td>\n",
       "      <td>$550,000</td>\n",
       "      <td>&lt; $80,000</td>\n",
       "      <td>$450,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_tax_value_bin</th>\n",
       "      <td>$450,000</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>$350,000</td>\n",
       "      <td>&lt; $50,000</td>\n",
       "      <td>$250,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_bin</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>structure_dollar_per_sqft</th>\n",
       "      <td>118.49</td>\n",
       "      <td>134.43</td>\n",
       "      <td>136.87</td>\n",
       "      <td>39.25</td>\n",
       "      <td>87.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>structure_dollar_sqft_bin</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_dollar_per_sqft</th>\n",
       "      <td>40.89</td>\n",
       "      <td>45.05</td>\n",
       "      <td>29.12</td>\n",
       "      <td>1.78</td>\n",
       "      <td>22.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lot_dollar_sqft_bin</th>\n",
       "      <td>20-49</td>\n",
       "      <td>20-49</td>\n",
       "      <td>20-49</td>\n",
       "      <td>1</td>\n",
       "      <td>20-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bath_bed_ratio</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cola</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             0                    1  \\\n",
       "parcelid                              10711855             10711877   \n",
       "bathrooms                                 2.00                 2.00   \n",
       "bedrooms                                  3.00                 4.00   \n",
       "buildingquality                           8.00                 8.00   \n",
       "square_feet                           2,107.00             1,882.00   \n",
       "fips                                      6037                 6037   \n",
       "latitude                         34,222,559.00        34,220,261.00   \n",
       "longitude                      -118,617,387.00      -118,616,409.00   \n",
       "lot_size                              9,158.00             9,035.00   \n",
       "regionidcity                         12,447.00            12,447.00   \n",
       "regionidcounty                        3,101.00             3,101.00   \n",
       "regionidzip                          96,339.00            96,339.00   \n",
       "roomcnt                                   0.00                 0.00   \n",
       "unit_count                                1.00                 1.00   \n",
       "age                                      45.00                45.00   \n",
       "structure_tax_value                 249,655.00           253,000.00   \n",
       "tax_value                           624,139.00           660,000.00   \n",
       "assessmentyear                        2,016.00             2,016.00   \n",
       "land_tax_value                      374,484.00           407,000.00   \n",
       "taxamount                             7,659.36             8,123.91   \n",
       "logerror                                 -0.01                 0.02   \n",
       "transactiondate                     2017-07-07           2017-08-29   \n",
       "heating_system                         Central              Central   \n",
       "county                             Los_Angeles          Los_Angeles   \n",
       "age_bin                                  40-50                40-50   \n",
       "taxrate                                   1.23                 1.23   \n",
       "acres                                     0.21                 0.21   \n",
       "acres_bin                                 0.20                 0.20   \n",
       "tax_value_bin                         $650,000             $900,000   \n",
       "land_tax_value_bin                    $450,000             $450,000   \n",
       "sqft_bin                                  0.50                 0.40   \n",
       "structure_dollar_per_sqft               118.49               134.43   \n",
       "structure_dollar_sqft_bin                 0.40                 0.40   \n",
       "land_dollar_per_sqft                     40.89                45.05   \n",
       "lot_dollar_sqft_bin                      20-49                20-49   \n",
       "bath_bed_ratio                            0.67                 0.50   \n",
       "cola                                         1                    1   \n",
       "\n",
       "                                             2                    3  \\\n",
       "parcelid                              10711888             10711910   \n",
       "bathrooms                                 2.00                 2.00   \n",
       "bedrooms                                  4.00                 3.00   \n",
       "buildingquality                           8.00                 8.00   \n",
       "square_feet                           1,882.00             1,477.00   \n",
       "fips                                      6037                 6037   \n",
       "latitude                         34,222,491.00        34,221,864.00   \n",
       "longitude                      -118,616,854.00      -118,615,739.00   \n",
       "lot_size                              9,800.00            11,285.00   \n",
       "regionidcity                         12,447.00            12,447.00   \n",
       "regionidcounty                        3,101.00             3,101.00   \n",
       "regionidzip                          96,339.00            96,339.00   \n",
       "roomcnt                                   0.00                 0.00   \n",
       "unit_count                                1.00                 1.00   \n",
       "age                                      45.00                57.00   \n",
       "structure_tax_value                 257,591.00            57,968.00   \n",
       "tax_value                           542,923.00            78,031.00   \n",
       "assessmentyear                        2,016.00             2,016.00   \n",
       "land_tax_value                      285,332.00            20,063.00   \n",
       "taxamount                             6,673.24             1,116.46   \n",
       "logerror                                  0.08                -0.04   \n",
       "transactiondate                     2017-04-04           2017-03-17   \n",
       "heating_system                         Central              Central   \n",
       "county                             Los_Angeles          Los_Angeles   \n",
       "age_bin                                  40-50                50-60   \n",
       "taxrate                                   1.23                 1.43   \n",
       "acres                                     0.22                 0.26   \n",
       "acres_bin                                 0.20                 0.30   \n",
       "tax_value_bin                         $550,000            < $80,000   \n",
       "land_tax_value_bin                    $350,000            < $50,000   \n",
       "sqft_bin                                  0.40                 0.30   \n",
       "structure_dollar_per_sqft               136.87                39.25   \n",
       "structure_dollar_sqft_bin                 0.40                 0.10   \n",
       "land_dollar_per_sqft                     29.12                 1.78   \n",
       "lot_dollar_sqft_bin                      20-49                    1   \n",
       "bath_bed_ratio                            0.50                 0.67   \n",
       "cola                                         1                    1   \n",
       "\n",
       "                                             4  \n",
       "parcelid                              10711923  \n",
       "bathrooms                                 2.00  \n",
       "bedrooms                                  4.00  \n",
       "buildingquality                           8.00  \n",
       "square_feet                           1,918.00  \n",
       "fips                                      6037  \n",
       "latitude                         34,220,619.00  \n",
       "longitude                      -118,615,253.00  \n",
       "lot_size                             11,239.00  \n",
       "regionidcity                         12,447.00  \n",
       "regionidcounty                        3,101.00  \n",
       "regionidzip                          96,339.00  \n",
       "roomcnt                                   0.00  \n",
       "unit_count                                1.00  \n",
       "age                                      57.00  \n",
       "structure_tax_value                 167,869.00  \n",
       "tax_value                           415,459.00  \n",
       "assessmentyear                        2,016.00  \n",
       "land_tax_value                      247,590.00  \n",
       "taxamount                             5,239.85  \n",
       "logerror                                 -0.01  \n",
       "transactiondate                     2017-03-24  \n",
       "heating_system                         Central  \n",
       "county                             Los_Angeles  \n",
       "age_bin                                  50-60  \n",
       "taxrate                                   1.26  \n",
       "acres                                     0.26  \n",
       "acres_bin                                 0.30  \n",
       "tax_value_bin                         $450,000  \n",
       "land_tax_value_bin                    $250,000  \n",
       "sqft_bin                                  0.40  \n",
       "structure_dollar_per_sqft                87.52  \n",
       "structure_dollar_sqft_bin                 0.30  \n",
       "land_dollar_per_sqft                     22.03  \n",
       "lot_dollar_sqft_bin                      20-49  \n",
       "bath_bed_ratio                            0.50  \n",
       "cola                                         1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>buildingquality</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>fips</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>regionidcity</th>\n",
       "      <th>regionidcounty</th>\n",
       "      <th>regionidzip</th>\n",
       "      <th>roomcnt</th>\n",
       "      <th>unit_count</th>\n",
       "      <th>age</th>\n",
       "      <th>structure_tax_value</th>\n",
       "      <th>tax_value</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>land_tax_value</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>logerror</th>\n",
       "      <th>taxrate</th>\n",
       "      <th>acres</th>\n",
       "      <th>acres_bin</th>\n",
       "      <th>sqft_bin</th>\n",
       "      <th>structure_dollar_per_sqft</th>\n",
       "      <th>structure_dollar_sqft_bin</th>\n",
       "      <th>land_dollar_per_sqft</th>\n",
       "      <th>bath_bed_ratio</th>\n",
       "      <th>cola</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "      <td>50,819.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12,961,988.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.30</td>\n",
       "      <td>6.16</td>\n",
       "      <td>1,890.06</td>\n",
       "      <td>6,049.21</td>\n",
       "      <td>34,021,696.06</td>\n",
       "      <td>-118,193,277.09</td>\n",
       "      <td>9,817.69</td>\n",
       "      <td>33,547.12</td>\n",
       "      <td>2,524.00</td>\n",
       "      <td>96,640.75</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.00</td>\n",
       "      <td>53.87</td>\n",
       "      <td>185,360.45</td>\n",
       "      <td>494,659.84</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>309,299.39</td>\n",
       "      <td>6,030.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.39</td>\n",
       "      <td>89.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>47.25</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2,192,354.52</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.36</td>\n",
       "      <td>907.21</td>\n",
       "      <td>21.13</td>\n",
       "      <td>272,994.72</td>\n",
       "      <td>355,631.91</td>\n",
       "      <td>20,784.28</td>\n",
       "      <td>49,280.87</td>\n",
       "      <td>803.66</td>\n",
       "      <td>4,676.38</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.87</td>\n",
       "      <td>192,985.80</td>\n",
       "      <td>503,547.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>361,072.59</td>\n",
       "      <td>5,813.63</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.18</td>\n",
       "      <td>56.31</td>\n",
       "      <td>0.15</td>\n",
       "      <td>65.85</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10,711,855.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>6,037.00</td>\n",
       "      <td>33,340,620.00</td>\n",
       "      <td>-119,475,265.00</td>\n",
       "      <td>236.00</td>\n",
       "      <td>3,491.00</td>\n",
       "      <td>1,286.00</td>\n",
       "      <td>95,982.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>148.00</td>\n",
       "      <td>3,254.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>2,250.00</td>\n",
       "      <td>49.18</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11,517,066.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1,266.00</td>\n",
       "      <td>6,037.00</td>\n",
       "      <td>33,827,301.00</td>\n",
       "      <td>-118,396,468.50</td>\n",
       "      <td>5,585.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>1,286.00</td>\n",
       "      <td>96,206.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>76,786.00</td>\n",
       "      <td>191,520.50</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>73,833.00</td>\n",
       "      <td>2,627.88</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>54.61</td>\n",
       "      <td>0.20</td>\n",
       "      <td>9.28</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12,591,550.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1,649.00</td>\n",
       "      <td>6,037.00</td>\n",
       "      <td>34,020,668.00</td>\n",
       "      <td>-118,151,539.00</td>\n",
       "      <td>6,832.00</td>\n",
       "      <td>24,812.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,415.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>130,092.00</td>\n",
       "      <td>368,866.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>215,518.00</td>\n",
       "      <td>4,587.67</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.40</td>\n",
       "      <td>81.51</td>\n",
       "      <td>0.30</td>\n",
       "      <td>30.23</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14,131,794.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2,275.00</td>\n",
       "      <td>6,059.00</td>\n",
       "      <td>34,186,063.50</td>\n",
       "      <td>-117,929,146.00</td>\n",
       "      <td>8,686.50</td>\n",
       "      <td>40,227.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,996.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>67.00</td>\n",
       "      <td>220,309.00</td>\n",
       "      <td>608,352.50</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>404,186.00</td>\n",
       "      <td>7,225.28</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.50</td>\n",
       "      <td>112.34</td>\n",
       "      <td>0.40</td>\n",
       "      <td>59.61</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>162,960,814.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>8,909.00</td>\n",
       "      <td>6,111.00</td>\n",
       "      <td>34,818,767.00</td>\n",
       "      <td>-117,572,338.00</td>\n",
       "      <td>862,329.00</td>\n",
       "      <td>396,556.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>399,675.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.00</td>\n",
       "      <td>3,097,672.00</td>\n",
       "      <td>4,993,132.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>4,847,224.00</td>\n",
       "      <td>72,122.20</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.81</td>\n",
       "      <td>19.80</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1,444.22</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1,604.53</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  parcelid            bathrooms             bedrooms  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean         12,961,988.63                 2.28                 3.30   \n",
       "std           2,192,354.52                 0.96                 0.91   \n",
       "min          10,711,855.00                 1.00                 1.00   \n",
       "25%          11,517,066.50                 2.00                 3.00   \n",
       "50%          12,591,550.00                 2.00                 3.00   \n",
       "75%          14,131,794.50                 3.00                 4.00   \n",
       "max         162,960,814.00                 7.00                 7.00   \n",
       "\n",
       "           buildingquality          square_feet                 fips  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean                  6.16             1,890.06             6,049.21   \n",
       "std                   1.36               907.21                21.13   \n",
       "min                   1.00               500.00             6,037.00   \n",
       "25%                   6.00             1,266.00             6,037.00   \n",
       "50%                   6.00             1,649.00             6,037.00   \n",
       "75%                   7.00             2,275.00             6,059.00   \n",
       "max                  12.00             8,909.00             6,111.00   \n",
       "\n",
       "                  latitude            longitude             lot_size  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean         34,021,696.06      -118,193,277.09             9,817.69   \n",
       "std             272,994.72           355,631.91            20,784.28   \n",
       "min          33,340,620.00      -119,475,265.00               236.00   \n",
       "25%          33,827,301.00      -118,396,468.50             5,585.00   \n",
       "50%          34,020,668.00      -118,151,539.00             6,832.00   \n",
       "75%          34,186,063.50      -117,929,146.00             8,686.50   \n",
       "max          34,818,767.00      -117,572,338.00           862,329.00   \n",
       "\n",
       "              regionidcity       regionidcounty          regionidzip  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean             33,547.12             2,524.00            96,640.75   \n",
       "std              49,280.87               803.66             4,676.38   \n",
       "min               3,491.00             1,286.00            95,982.00   \n",
       "25%              12,447.00             1,286.00            96,206.00   \n",
       "50%              24,812.00             3,101.00            96,415.00   \n",
       "75%              40,227.00             3,101.00            96,996.00   \n",
       "max             396,556.00             3,101.00           399,675.00   \n",
       "\n",
       "                   roomcnt           unit_count                  age  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean                  1.92                 1.00                53.87   \n",
       "std                   3.16                 0.00                22.87   \n",
       "min                   0.00                 1.00                 1.00   \n",
       "25%                   0.00                 1.00                39.00   \n",
       "50%                   0.00                 1.00                57.00   \n",
       "75%                   5.00                 1.00                67.00   \n",
       "max                  14.00                 1.00               139.00   \n",
       "\n",
       "       structure_tax_value            tax_value       assessmentyear  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean            185,360.45           494,659.84             2,016.00   \n",
       "std             192,985.80           503,547.32                 0.00   \n",
       "min                 148.00             3,254.00             2,016.00   \n",
       "25%              76,786.00           191,520.50             2,016.00   \n",
       "50%             130,092.00           368,866.00             2,016.00   \n",
       "75%             220,309.00           608,352.50             2,016.00   \n",
       "max           3,097,672.00         4,993,132.00             2,016.00   \n",
       "\n",
       "            land_tax_value            taxamount             logerror  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean            309,299.39             6,030.21                 0.02   \n",
       "std             361,072.59             5,813.63                 0.17   \n",
       "min               2,250.00                49.18                -4.66   \n",
       "25%              73,833.00             2,627.88                -0.02   \n",
       "50%             215,518.00             4,587.67                 0.01   \n",
       "75%             404,186.00             7,225.28                 0.04   \n",
       "max           4,847,224.00            72,122.20                 3.39   \n",
       "\n",
       "                   taxrate                acres            acres_bin  \\\n",
       "count            50,819.00            50,819.00            50,819.00   \n",
       "mean                  1.33                 0.23                 0.17   \n",
       "std                   0.36                 0.48                 0.10   \n",
       "min                   0.01                 0.01                 0.00   \n",
       "25%                   1.16                 0.13                 0.10   \n",
       "50%                   1.23                 0.16                 0.20   \n",
       "75%                   1.37                 0.20                 0.20   \n",
       "max                   9.81                19.80                 0.70   \n",
       "\n",
       "                  sqft_bin  structure_dollar_per_sqft  \\\n",
       "count            50,819.00                  50,819.00   \n",
       "mean                  0.39                      89.39   \n",
       "std                   0.18                      56.31   \n",
       "min                   0.00                       0.04   \n",
       "25%                   0.30                      54.61   \n",
       "50%                   0.40                      81.51   \n",
       "75%                   0.50                     112.34   \n",
       "max                   0.90                   1,444.22   \n",
       "\n",
       "       structure_dollar_sqft_bin  land_dollar_per_sqft       bath_bed_ratio  \\\n",
       "count                  50,819.00             50,819.00            50,819.00   \n",
       "mean                        0.28                 47.25                 0.70   \n",
       "std                         0.15                 65.85                 0.23   \n",
       "min                         0.00                  0.03                 0.20   \n",
       "25%                         0.20                  9.28                 0.50   \n",
       "50%                         0.30                 30.23                 0.67   \n",
       "75%                         0.40                 59.61                 0.80   \n",
       "max                         0.90              1,604.53                 3.00   \n",
       "\n",
       "                      cola  \n",
       "count            50,819.00  \n",
       "mean                  0.22  \n",
       "std                   0.42  \n",
       "min                   0.00  \n",
       "25%                   0.00  \n",
       "50%                   0.00  \n",
       "75%                   0.00  \n",
       "max                   1.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.000e+00, 0.000e+00, 1.400e+01, 1.600e+01, 1.100e+02, 4.791e+04,\n",
       "        2.575e+03, 1.000e+02, 8.300e+01, 9.000e+00]),\n",
       " array([-4.65542038, -3.85042393, -3.04542749, -2.24043104, -1.4354346 ,\n",
       "        -0.63043816,  0.17455829,  0.97955473,  1.78455118,  2.58954762,\n",
       "         3.39454407]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 20))\n",
    "\n",
    "plt.suptitle(\"Plotting the distributions of the Numerical columns\")\n",
    "# row, columns, index\n",
    "plt.subplot(3, 3, 1)\n",
    "plt.hist(df.bathrooms, edgecolor='indianred', alpha=0.5, color='lightcoral')\n",
    "plt.subplot(3, 3, 2)\n",
    "plt.hist(df.bedrooms, edgecolor='indianred', alpha=0.5, color='lightcoral')\n",
    "plt.subplot(3, 3, 3)\n",
    "plt.hist(df.square_feet, edgecolor='indianred', alpha=0.5, color='lightcoral')\n",
    "plt.subplot(3, 3, 4)\n",
    "plt.hist(df.tax_value, edgecolor='indianred', alpha=0.5, color='lightcoral')\n",
    "plt.subplot(3, 3, 5)\n",
    "plt.hist(df.logerror, edgecolor='indianred', alpha=0.5, color='lightcoral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50819 entries, 0 to 52504\n",
      "Data columns (total 37 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   parcelid                   50819 non-null  int64   \n",
      " 1   bathrooms                  50819 non-null  float64 \n",
      " 2   bedrooms                   50819 non-null  float64 \n",
      " 3   buildingquality            50819 non-null  float64 \n",
      " 4   square_feet                50819 non-null  float64 \n",
      " 5   fips                       50819 non-null  int64   \n",
      " 6   latitude                   50819 non-null  float64 \n",
      " 7   longitude                  50819 non-null  float64 \n",
      " 8   lot_size                   50819 non-null  float64 \n",
      " 9   regionidcity               50819 non-null  float64 \n",
      " 10  regionidcounty             50819 non-null  float64 \n",
      " 11  regionidzip                50819 non-null  float64 \n",
      " 12  roomcnt                    50819 non-null  float64 \n",
      " 13  unit_count                 50819 non-null  float64 \n",
      " 14  age                        50819 non-null  float64 \n",
      " 15  structure_tax_value        50819 non-null  float64 \n",
      " 16  tax_value                  50819 non-null  float64 \n",
      " 17  assessmentyear             50819 non-null  float64 \n",
      " 18  land_tax_value             50819 non-null  float64 \n",
      " 19  taxamount                  50819 non-null  float64 \n",
      " 20  logerror                   50819 non-null  float64 \n",
      " 21  transactiondate            50819 non-null  object  \n",
      " 22  heating_system             50819 non-null  object  \n",
      " 23  county                     50819 non-null  object  \n",
      " 24  age_bin                    50819 non-null  category\n",
      " 25  taxrate                    50819 non-null  float64 \n",
      " 26  acres                      50819 non-null  float64 \n",
      " 27  acres_bin                  50819 non-null  float64 \n",
      " 28  tax_value_bin              50819 non-null  category\n",
      " 29  land_tax_value_bin         48606 non-null  category\n",
      " 30  sqft_bin                   50819 non-null  float64 \n",
      " 31  structure_dollar_per_sqft  50819 non-null  float64 \n",
      " 32  structure_dollar_sqft_bin  50819 non-null  float64 \n",
      " 33  land_dollar_per_sqft       50819 non-null  float64 \n",
      " 34  lot_dollar_sqft_bin        50819 non-null  category\n",
      " 35  bath_bed_ratio             50819 non-null  float64 \n",
      " 36  cola                       50819 non-null  int64   \n",
      "dtypes: category(4), float64(27), int64(3), object(3)\n",
      "memory usage: 13.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Checking for Outliers:\n",
    "sns.boxplot(df['bedrooms'])\n",
    "plt.title('Bedrooms')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['bathrooms'])\n",
    "plt.title('Bathrooms')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['square_feet'])\n",
    "plt.title('Square Footage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['acres'])\n",
    "plt.title('Acres')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['taxrate'])\n",
    "plt.title('Tax Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Takeaways:  \n",
    "\n",
    "Removing Outliers:\n",
    "\n",
    "Remove extremes in bedrooms and baths (KEEP only 1-7 baths and 1-7 bedrooms)\n",
    "\n",
    "remove square feet > 9000\n",
    "\n",
    "remove lot size (acres) > 20 \n",
    "\n",
    "Remove tax rate > 10% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df):\n",
    "    '''\n",
    "    remove outliers in bed, bath, square feet, acres & tax rate\n",
    "    '''\n",
    "\n",
    "    return df[((df.bathrooms <= 7) & (df.bedrooms <= 7) &\n",
    "               (df.bathrooms >= 1) & \n",
    "               (df.bedrooms >= 1) & \n",
    "               (df.acres <= 20) &\n",
    "               (df.square_feet <= 9000) & \n",
    "               (df.taxrate <= 10)\n",
    "              )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parcelid</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>buildingquality</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>fips</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>regionidcity</th>\n",
       "      <th>regionidcounty</th>\n",
       "      <th>regionidzip</th>\n",
       "      <th>roomcnt</th>\n",
       "      <th>unit_count</th>\n",
       "      <th>age</th>\n",
       "      <th>structure_tax_value</th>\n",
       "      <th>tax_value</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>land_tax_value</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>logerror</th>\n",
       "      <th>transactiondate</th>\n",
       "      <th>heating_system</th>\n",
       "      <th>county</th>\n",
       "      <th>age_bin</th>\n",
       "      <th>taxrate</th>\n",
       "      <th>acres</th>\n",
       "      <th>acres_bin</th>\n",
       "      <th>tax_value_bin</th>\n",
       "      <th>land_tax_value_bin</th>\n",
       "      <th>sqft_bin</th>\n",
       "      <th>structure_dollar_per_sqft</th>\n",
       "      <th>structure_dollar_sqft_bin</th>\n",
       "      <th>land_dollar_per_sqft</th>\n",
       "      <th>lot_dollar_sqft_bin</th>\n",
       "      <th>bath_bed_ratio</th>\n",
       "      <th>cola</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10711855</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2,107.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,222,559.00</td>\n",
       "      <td>-118,617,387.00</td>\n",
       "      <td>9,158.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>249,655.00</td>\n",
       "      <td>624,139.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>374,484.00</td>\n",
       "      <td>7,659.36</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>$650,000</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>118.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>40.89</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10711877</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,220,261.00</td>\n",
       "      <td>-118,616,409.00</td>\n",
       "      <td>9,035.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>253,000.00</td>\n",
       "      <td>660,000.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>407,000.00</td>\n",
       "      <td>8,123.91</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>$900,000</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>134.43</td>\n",
       "      <td>0.40</td>\n",
       "      <td>45.05</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10711888</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,882.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,222,491.00</td>\n",
       "      <td>-118,616,854.00</td>\n",
       "      <td>9,800.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>257,591.00</td>\n",
       "      <td>542,923.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>285,332.00</td>\n",
       "      <td>6,673.24</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.20</td>\n",
       "      <td>$550,000</td>\n",
       "      <td>$350,000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>136.87</td>\n",
       "      <td>0.40</td>\n",
       "      <td>29.12</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10711910</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,477.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,221,864.00</td>\n",
       "      <td>-118,615,739.00</td>\n",
       "      <td>11,285.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>57,968.00</td>\n",
       "      <td>78,031.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>20,063.00</td>\n",
       "      <td>1,116.46</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>50-60</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>&lt; $80,000</td>\n",
       "      <td>&lt; $50,000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>39.25</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10711923</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1,918.00</td>\n",
       "      <td>6037</td>\n",
       "      <td>34,220,619.00</td>\n",
       "      <td>-118,615,253.00</td>\n",
       "      <td>11,239.00</td>\n",
       "      <td>12,447.00</td>\n",
       "      <td>3,101.00</td>\n",
       "      <td>96,339.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>167,869.00</td>\n",
       "      <td>415,459.00</td>\n",
       "      <td>2,016.00</td>\n",
       "      <td>247,590.00</td>\n",
       "      <td>5,239.85</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>2017-03-24</td>\n",
       "      <td>Central</td>\n",
       "      <td>Los_Angeles</td>\n",
       "      <td>50-60</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>$450,000</td>\n",
       "      <td>$250,000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>87.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>22.03</td>\n",
       "      <td>20-49</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   parcelid            bathrooms             bedrooms      buildingquality  \\\n",
       "0  10711855                 2.00                 3.00                 8.00   \n",
       "1  10711877                 2.00                 4.00                 8.00   \n",
       "2  10711888                 2.00                 4.00                 8.00   \n",
       "3  10711910                 2.00                 3.00                 8.00   \n",
       "4  10711923                 2.00                 4.00                 8.00   \n",
       "\n",
       "           square_feet  fips             latitude            longitude  \\\n",
       "0             2,107.00  6037        34,222,559.00      -118,617,387.00   \n",
       "1             1,882.00  6037        34,220,261.00      -118,616,409.00   \n",
       "2             1,882.00  6037        34,222,491.00      -118,616,854.00   \n",
       "3             1,477.00  6037        34,221,864.00      -118,615,739.00   \n",
       "4             1,918.00  6037        34,220,619.00      -118,615,253.00   \n",
       "\n",
       "              lot_size         regionidcity       regionidcounty  \\\n",
       "0             9,158.00            12,447.00             3,101.00   \n",
       "1             9,035.00            12,447.00             3,101.00   \n",
       "2             9,800.00            12,447.00             3,101.00   \n",
       "3            11,285.00            12,447.00             3,101.00   \n",
       "4            11,239.00            12,447.00             3,101.00   \n",
       "\n",
       "           regionidzip              roomcnt           unit_count  \\\n",
       "0            96,339.00                 0.00                 1.00   \n",
       "1            96,339.00                 0.00                 1.00   \n",
       "2            96,339.00                 0.00                 1.00   \n",
       "3            96,339.00                 0.00                 1.00   \n",
       "4            96,339.00                 0.00                 1.00   \n",
       "\n",
       "                   age  structure_tax_value            tax_value  \\\n",
       "0                45.00           249,655.00           624,139.00   \n",
       "1                45.00           253,000.00           660,000.00   \n",
       "2                45.00           257,591.00           542,923.00   \n",
       "3                57.00            57,968.00            78,031.00   \n",
       "4                57.00           167,869.00           415,459.00   \n",
       "\n",
       "        assessmentyear       land_tax_value            taxamount  \\\n",
       "0             2,016.00           374,484.00             7,659.36   \n",
       "1             2,016.00           407,000.00             8,123.91   \n",
       "2             2,016.00           285,332.00             6,673.24   \n",
       "3             2,016.00            20,063.00             1,116.46   \n",
       "4             2,016.00           247,590.00             5,239.85   \n",
       "\n",
       "              logerror transactiondate heating_system       county age_bin  \\\n",
       "0                -0.01      2017-07-07        Central  Los_Angeles   40-50   \n",
       "1                 0.02      2017-08-29        Central  Los_Angeles   40-50   \n",
       "2                 0.08      2017-04-04        Central  Los_Angeles   40-50   \n",
       "3                -0.04      2017-03-17        Central  Los_Angeles   50-60   \n",
       "4                -0.01      2017-03-24        Central  Los_Angeles   50-60   \n",
       "\n",
       "               taxrate                acres            acres_bin  \\\n",
       "0                 1.23                 0.21                 0.20   \n",
       "1                 1.23                 0.21                 0.20   \n",
       "2                 1.23                 0.22                 0.20   \n",
       "3                 1.43                 0.26                 0.30   \n",
       "4                 1.26                 0.26                 0.30   \n",
       "\n",
       "  tax_value_bin land_tax_value_bin             sqft_bin  \\\n",
       "0      $650,000           $450,000                 0.50   \n",
       "1      $900,000           $450,000                 0.40   \n",
       "2      $550,000           $350,000                 0.40   \n",
       "3     < $80,000          < $50,000                 0.30   \n",
       "4      $450,000           $250,000                 0.40   \n",
       "\n",
       "   structure_dollar_per_sqft  structure_dollar_sqft_bin  land_dollar_per_sqft  \\\n",
       "0                     118.49                       0.40                 40.89   \n",
       "1                     134.43                       0.40                 45.05   \n",
       "2                     136.87                       0.40                 29.12   \n",
       "3                      39.25                       0.10                  1.78   \n",
       "4                      87.52                       0.30                 22.03   \n",
       "\n",
       "  lot_dollar_sqft_bin       bath_bed_ratio  cola  \n",
       "0               20-49                 0.67     1  \n",
       "1               20-49                 0.50     1  \n",
       "2               20-49                 0.50     1  \n",
       "3                   1                 0.67     1  \n",
       "4               20-49                 0.50     1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = remove_outliers(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50819, 37)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='SD'></a>\n",
    "#### Splitting Data\n",
    "\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_validate, y_validate, X_test, y_test = wrangle.data_split(df,stratify_by='logerror')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28458, 36), (12197, 36), (10164, 36))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_validate.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28458,), (12197,), (10164,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape, y_validate.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train observations:  (28458, 37)\n",
      "validate observations:  (7115, 37)\n",
      "test observations:  (15246, 37)\n"
     ]
    }
   ],
   "source": [
    "train_validate, test = train_test_split(df, test_size=.30, random_state=123)\n",
    "train, validate = train_test_split(train_validate, test_size=.20, random_state=123)\n",
    "\n",
    "print(\"train observations: \", train.shape)\n",
    "print(\"validate observations: \", validate.shape)\n",
    "print(\"test observations: \", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>logerror</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41364</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32738</th>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30071</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27720</th>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48220</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26489</th>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42732</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28458 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  logerror\n",
       "41364                 0.02\n",
       "32738                -0.01\n",
       "707                   0.10\n",
       "30071                 0.09\n",
       "27720                -0.03\n",
       "...                    ...\n",
       "201                   0.03\n",
       "48220                 0.11\n",
       "26489                -0.10\n",
       "3575                 -0.08\n",
       "42732                 0.00\n",
       "\n",
       "[28458 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = pd.DataFrame(y_train)\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Takeaways:\n",
    "##### Dropped unneeded columns\n",
    "##### Renamed columns to make easier to call\n",
    "##### Created new features\n",
    "##### Identified and removed outliers\n",
    "##### Binned some variables to aid in exploration\n",
    "##### Split data and scaled data\n",
    "##### Data is ready for exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Explore'></a>\n",
    "## Explore\n",
    "\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28458 entries, 41364 to 42732\n",
      "Data columns (total 36 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   parcelid                   28458 non-null  int64   \n",
      " 1   bathrooms                  28458 non-null  float64 \n",
      " 2   bedrooms                   28458 non-null  float64 \n",
      " 3   buildingquality            28458 non-null  float64 \n",
      " 4   square_feet                28458 non-null  float64 \n",
      " 5   fips                       28458 non-null  int64   \n",
      " 6   latitude                   28458 non-null  float64 \n",
      " 7   longitude                  28458 non-null  float64 \n",
      " 8   lot_size                   28458 non-null  float64 \n",
      " 9   regionidcity               28458 non-null  float64 \n",
      " 10  regionidcounty             28458 non-null  float64 \n",
      " 11  regionidzip                28458 non-null  float64 \n",
      " 12  roomcnt                    28458 non-null  float64 \n",
      " 13  unit_count                 28458 non-null  float64 \n",
      " 14  age                        28458 non-null  float64 \n",
      " 15  structure_tax_value        28458 non-null  float64 \n",
      " 16  tax_value                  28458 non-null  float64 \n",
      " 17  assessmentyear             28458 non-null  float64 \n",
      " 18  land_tax_value             28458 non-null  float64 \n",
      " 19  taxamount                  28458 non-null  float64 \n",
      " 20  transactiondate            28458 non-null  object  \n",
      " 21  heating_system             28458 non-null  object  \n",
      " 22  county                     28458 non-null  object  \n",
      " 23  age_bin                    28458 non-null  category\n",
      " 24  taxrate                    28458 non-null  float64 \n",
      " 25  acres                      28458 non-null  float64 \n",
      " 26  acres_bin                  28458 non-null  float64 \n",
      " 27  tax_value_bin              28458 non-null  category\n",
      " 28  land_tax_value_bin         27221 non-null  category\n",
      " 29  sqft_bin                   28458 non-null  float64 \n",
      " 30  structure_dollar_per_sqft  28458 non-null  float64 \n",
      " 31  structure_dollar_sqft_bin  28458 non-null  float64 \n",
      " 32  land_dollar_per_sqft       28458 non-null  float64 \n",
      " 33  lot_dollar_sqft_bin        28458 non-null  category\n",
      " 34  bath_bed_ratio             28458 non-null  float64 \n",
      " 35  cola                       28458 non-null  int64   \n",
      "dtypes: category(4), float64(26), int64(3), object(3)\n",
      "memory usage: 7.3+ MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>logerror</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28,458.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  logerror\n",
       "count            28,458.00\n",
       "mean                  0.02\n",
       "std                   0.17\n",
       "min                  -4.66\n",
       "25%                  -0.02\n",
       "50%                   0.01\n",
       "75%                   0.04\n",
       "max                   3.18"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Test: square feet/log error  <br>\n",
    "𝐻0: There is no difference in log error based on a properties square footage. <font color = 'red'>  --> REJECTED  <font color = 'black'>\n",
    "    \n",
    "𝐻𝑎: Properties with a square footage less than 2800 square footage have a lower log error than larger properties. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'r' correlation coefficient is 0.03656900596871775\n",
      "p-value (probability of seeing the data given the null hypothesis is true) is  6.794488946994873e-10\n",
      "We reject the null hypothesis that there is no difference in log error based on a properties square footage.\n"
     ]
    }
   ],
   "source": [
    "α = .05\n",
    "\n",
    "x = X_train.square_feet\n",
    "y = y_train.logerror\n",
    "\n",
    "r, p = stats.pearsonr(x,y)\n",
    "\n",
    "\n",
    "print(\"'r' correlation coefficient is\", r)\n",
    "print(\"p-value (probability of seeing the data given the null hypothesis is true) is \", p)\n",
    "\n",
    "if p < α:\n",
    "    print(\"We reject the null hypothesis that there is no difference in log error based on a properties square footage.\")\n",
    "else:\n",
    "    print(\"We fail to reject the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Test: age/log error  <br>\n",
    "𝐻0 : There is no difference in median log error in properties built in different years. <font color = 'red'>  --> REJECTED  <font color = 'black'>\n",
    "    \n",
    "𝐻𝑎 : Properties that were built after 1975 have a higher median log error than properties built before 1975.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'r' correlation coefficient is -0.02144078340490189\n",
      "p-value (probability of seeing the data given the null hypothesis is true) is  0.000297796079538452\n",
      "We reject the null hypothesis that there is no difference in log error based on a home's age.\n"
     ]
    }
   ],
   "source": [
    "α = .05\n",
    "\n",
    "x = X_train.age\n",
    "y = y_train.logerror\n",
    "\n",
    "r, p = stats.pearsonr(x,y)\n",
    "\n",
    "\n",
    "print(\"'r' correlation coefficient is\", r)\n",
    "print(\"p-value (probability of seeing the data given the null hypothesis is true) is \", p)\n",
    "\n",
    "if p < α:\n",
    "    print(\"We reject the null hypothesis that there is no difference in log error based on a home's age.\")\n",
    "else:\n",
    "    print(\"We fail to reject the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Test: latitude/log error \n",
    "\n",
    "𝐻0 : There is no correlation between latitude and log error. <font color = 'red'>  --> REJECTED  <font color = 'black'>\n",
    "\n",
    "𝐻𝑎 : Properties that are more southern have a higher log error.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'r' correlation coefficient is -0.023547632085190603\n",
      "p-value (probability of seeing the data given the null hypothesis is true) is  7.104942590256166e-05\n",
      "We reject the null hypothesis that there is no difference in log error based on a home's latitude.\n"
     ]
    }
   ],
   "source": [
    "α = .05\n",
    "\n",
    "x = X_train.latitude\n",
    "y = y_train.logerror\n",
    "\n",
    "r, p = stats.pearsonr(x,y)\n",
    "\n",
    "\n",
    "print(\"'r' correlation coefficient is\", r)\n",
    "print(\"p-value (probability of seeing the data given the null hypothesis is true) is \", p)\n",
    "\n",
    "if p < α:\n",
    "    print(\"We reject the null hypothesis that there is no difference in log error based on a home's latitude.\")\n",
    "else:\n",
    "    print(\"We fail to reject the null hypothesis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Drop Columns not needed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28458 entries, 41364 to 42732\n",
      "Data columns (total 36 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   parcelid                   28458 non-null  int64   \n",
      " 1   bathrooms                  28458 non-null  float64 \n",
      " 2   bedrooms                   28458 non-null  float64 \n",
      " 3   buildingquality            28458 non-null  float64 \n",
      " 4   square_feet                28458 non-null  float64 \n",
      " 5   fips                       28458 non-null  int64   \n",
      " 6   latitude                   28458 non-null  float64 \n",
      " 7   longitude                  28458 non-null  float64 \n",
      " 8   lot_size                   28458 non-null  float64 \n",
      " 9   regionidcity               28458 non-null  float64 \n",
      " 10  regionidcounty             28458 non-null  float64 \n",
      " 11  regionidzip                28458 non-null  float64 \n",
      " 12  roomcnt                    28458 non-null  float64 \n",
      " 13  unit_count                 28458 non-null  float64 \n",
      " 14  age                        28458 non-null  float64 \n",
      " 15  structure_tax_value        28458 non-null  float64 \n",
      " 16  tax_value                  28458 non-null  float64 \n",
      " 17  assessmentyear             28458 non-null  float64 \n",
      " 18  land_tax_value             28458 non-null  float64 \n",
      " 19  taxamount                  28458 non-null  float64 \n",
      " 20  transactiondate            28458 non-null  object  \n",
      " 21  heating_system             28458 non-null  object  \n",
      " 22  county                     28458 non-null  object  \n",
      " 23  age_bin                    28458 non-null  category\n",
      " 24  taxrate                    28458 non-null  float64 \n",
      " 25  acres                      28458 non-null  float64 \n",
      " 26  acres_bin                  28458 non-null  float64 \n",
      " 27  tax_value_bin              28458 non-null  category\n",
      " 28  land_tax_value_bin         27221 non-null  category\n",
      " 29  sqft_bin                   28458 non-null  float64 \n",
      " 30  structure_dollar_per_sqft  28458 non-null  float64 \n",
      " 31  structure_dollar_sqft_bin  28458 non-null  float64 \n",
      " 32  land_dollar_per_sqft       28458 non-null  float64 \n",
      " 33  lot_dollar_sqft_bin        28458 non-null  category\n",
      " 34  bath_bed_ratio             28458 non-null  float64 \n",
      " 35  cola                       28458 non-null  int64   \n",
      "dtypes: category(4), float64(26), int64(3), object(3)\n",
      "memory usage: 8.5+ MB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age of Home in years')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(20,5))\n",
    "p = sns.barplot(X_train. age_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Age of Home v. Log Error')\n",
    "plt.xlabel(\"Age of Home in years\")\n",
    "\n",
    "\n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'City of Los Angeles v. Log Error')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(10,5))\n",
    "p = sns.barplot(X_train.cola, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('City of Los Angeles v. Log Error')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Acres v. Log Error')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(10,5))\n",
    "p = sns.barplot(X_train.acres_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Acres v. Log Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Binned Square Footage v. Log Error')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(10,5))\n",
    "p = sns.barplot(X_train.sqft_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Binned Square Footage v. Log Error')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Structure Dollar Per Square Foot v. Log Error')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(10,5))\n",
    "p = sns.barplot(X_train.structure_dollar_sqft_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Structure Dollar Per Square Foot v. Log Error')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lot Dollar Per Square Footage v. Log Error')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(10,5))\n",
    "p = sns.barplot(X_train.lot_dollar_sqft_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Lot Dollar Per Square Footage v. Log Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'County v. Log Error')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(10,5))\n",
    "p = sns.barplot(X_train.county, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('County v. Log Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tax Value v. Log Error')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(15,5))\n",
    "p = sns.barplot(X_train.tax_value_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Tax Value v. Log Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Land Tax Value v. Log Error')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plt.figure(figsize=(15,5))\n",
    "p = sns.barplot(X_train.land_tax_value_bin, y_train.logerror, data=X_train, alpha=.8, color='lightseagreen', edgecolor = 'darkblue')\n",
    "overall_rate = y_train.logerror.mean()\n",
    "p = plt.axhline(overall_rate, ls='--', color='red', )\n",
    "plt.title('Land Tax Value v. Log Error')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Scale'></a>\n",
    "### Scale Data\n",
    "\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Model'></a>\n",
    "\n",
    "## Model\n",
    "\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Concl'></a>\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "[back to top](#back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}