sfbay_modeling_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling rental prices in the SF Bay Area\n",
    "\n",
    "This notebook will develop a predictive model for rents in the Bay Area using rental listings from Craigslist. \n",
    "\n",
    "\n",
    "**Data:**\n",
    "The dataset used here is Craigslist listings merged with Census and point of interest data. It includes all unique listings in the 'apartments/housing' section of Craigslist from 11/13/2016 - 3/17/2017. \n",
    "\n",
    "Features include: \n",
    "- property characteristics (rent, sq ft, # bedrooms, # bathrooms, coordinates)\n",
    "- neighborhood characteristics at the Census block and block group levels (e.g., med hh income, race)\n",
    "- jobs accessibility (e.g., jobs within a given radius)\n",
    "- date listed on craigslist\n",
    "\n",
    "The data has been cleaned and filtered, but features have not yet been carefully selected.\n",
    "\n",
    "**Goal:**\n",
    "Develop a model to predict rent as accurately as possible.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DATA_DIR = os.path.join('..','data','urbansim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16560, 70)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisa/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  after removing the cwd from sys.path.\n"
     ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>date</th>\n",
       "      <th>region</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>rent</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>sqft</th>\n",
       "      <th>rent_sqft</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>pctrent</th>\n",
       "      <th>pctblack</th>\n",
       "      <th>pctwhite</th>\n",
       "      <th>pctasian</th>\n",
       "      <th>y17jan</th>\n",
       "      <th>y17feb</th>\n",
       "      <th>y17mar</th>\n",
       "      <th>bgpopden</th>\n",
       "      <th>bgjobden</th>\n",
       "      <th>highlowinc1500m</th>\n",
       "    </tr>\n",
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       "      <td>5873897352</td>\n",
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       "      <td>dublin / pleasanton / livermore</td>\n",
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       "      <th>1</th>\n",
       "      <td>5875136804</td>\n",
       "      <td>2016-11-14</td>\n",
       "      <td>sfbay</td>\n",
       "      <td>dublin / pleasanton / livermore</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2395.0</td>\n",
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       "      <td>817.0</td>\n",
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       "      <td>37.708458</td>\n",
       "      <td>...</td>\n",
       "      <td>64.434687</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10.073916</td>\n",
       "      <td>7.875687</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>sfbay</td>\n",
       "      <td>dublin / pleasanton / livermore</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>687.0</td>\n",
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       "      <td>37.708458</td>\n",
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       "      <td>7.875687</td>\n",
       "      <td>1.504727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5886665547</td>\n",
       "      <td>2016-11-21</td>\n",
       "      <td>sfbay</td>\n",
       "      <td>dublin / pleasanton / livermore</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "          pid        date region                     neighborhood    rent  \\\n",
       "0  5873897352  2016-11-13  sfbay  dublin / pleasanton / livermore  2250.0   \n",
       "1  5875136804  2016-11-14  sfbay  dublin / pleasanton / livermore  1995.0   \n",
       "2  5869197992  2016-11-15  sfbay  dublin / pleasanton / livermore  2395.0   \n",
       "3  5885759159  2016-11-20  sfbay  dublin / pleasanton / livermore  2250.0   \n",
       "4  5886665547  2016-11-21  sfbay  dublin / pleasanton / livermore  1995.0   \n",
       "\n",
       "   bedrooms   sqft  rent_sqft   longitude   latitude       ...        \\\n",
       "0       1.0  687.0   3.275109 -121.888125  37.708458       ...         \n",
       "1       1.0  687.0   2.903930 -121.888125  37.708458       ...         \n",
       "2       1.0  817.0   2.931457 -121.888125  37.708458       ...         \n",
       "3       1.0  687.0   3.275109 -121.888125  37.708458       ...         \n",
       "4       1.0  629.0   3.171701 -121.888125  37.708458       ...         \n",
       "\n",
       "     pctrent  pctblack pctwhite  pctasian  y17jan  y17feb  y17mar   bgpopden  \\\n",
       "0  64.434687       0.0      0.0       0.0   False   False   False  10.073916   \n",
       "1  64.434687       0.0      0.0       0.0   False   False   False  10.073916   \n",
       "2  64.434687       0.0      0.0       0.0   False   False   False  10.073916   \n",
       "3  64.434687       0.0      0.0       0.0   False   False   False  10.073916   \n",
       "4  64.434687       0.0      0.0       0.0   False   False   False  10.073916   \n",
       "\n",
       "   bgjobden highlowinc1500m  \n",
       "0  7.875687        1.504727  \n",
       "1  7.875687        1.504727  \n",
       "2  7.875687        1.504727  \n",
       "3  7.875687        1.504727  \n",
       "4  7.875687        1.504727  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this particular dataset is a subset of all the features available\n",
    "infile = 'ba_block_small.csv'\n",
    "df = pd.read_csv(os.path.join(DATA_DIR,infile))\n",
    "df = df.ix[:,1:]  # exclude first column, which was empty\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What kind of features do we have?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> **Characteristics of the rental unit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rent</th>\n",
       "      <th>sqft</th>\n",
       "      <th>rent_sqft</th>\n",
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       "      <th>bathrooms</th>\n",
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       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>8253.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2994.327597</td>\n",
       "      <td>978.960870</td>\n",
       "      <td>3.214822</td>\n",
       "      <td>1.617754</td>\n",
       "      <td>1.481764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1056.350152</td>\n",
       "      <td>356.909985</td>\n",
       "      <td>1.002537</td>\n",
       "      <td>0.823385</td>\n",
       "      <td>0.564599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>705.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2299.000000</td>\n",
       "      <td>734.000000</td>\n",
       "      <td>2.568345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2783.000000</td>\n",
       "      <td>946.000000</td>\n",
       "      <td>3.067740</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3495.000000</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>3.676923</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12500.000000</td>\n",
       "      <td>4645.000000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               rent          sqft     rent_sqft      bedrooms    bathrooms\n",
       "count  16560.000000  16560.000000  16560.000000  16560.000000  8253.000000\n",
       "mean    2994.327597    978.960870      3.214822      1.617754     1.481764\n",
       "std     1056.350152    356.909985      1.002537      0.823385     0.564599\n",
       "min      705.000000    180.000000      0.452830      0.000000     0.000000\n",
       "25%     2299.000000    734.000000      2.568345      1.000000     1.000000\n",
       "50%     2783.000000    946.000000      3.067740      2.000000     1.000000\n",
       "75%     3495.000000   1150.000000      3.676923      2.000000     2.000000\n",
       "max    12500.000000   4645.000000      9.750000      6.000000     4.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at some summary stats for key features\n",
    "# property characteristics\n",
    "df[['rent','sqft','rent_sqft','bedrooms','bathrooms']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> **Sociodemographics of the neighborhood**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>16559.000000</td>\n",
       "      <td>16559.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2278.621558</td>\n",
       "      <td>1677.654083</td>\n",
       "      <td>2619.841486</td>\n",
       "      <td>0.046135</td>\n",
       "      <td>2.121377</td>\n",
       "      <td>86422.977959</td>\n",
       "      <td>0.593939</td>\n",
       "      <td>0.436069</td>\n",
       "      <td>0.035347</td>\n",
       "      <td>0.220206</td>\n",
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       "      <td>0.336906</td>\n",
       "      <td>45.766697</td>\n",
       "      <td>31.276702</td>\n",
       "      <td>33.690634</td>\n",
       "      <td>59.393853</td>\n",
       "      <td>3.534688</td>\n",
       "      <td>43.606874</td>\n",
       "      <td>22.020556</td>\n",
       "      <td>17.852474</td>\n",
       "      <td>14.368919</td>\n",
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       "      <th>std</th>\n",
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       "      <td>7599.460446</td>\n",
       "      <td>3897.735448</td>\n",
       "      <td>0.209784</td>\n",
       "      <td>0.623896</td>\n",
       "      <td>29934.893921</td>\n",
       "      <td>0.234371</td>\n",
       "      <td>0.310481</td>\n",
       "      <td>0.096435</td>\n",
       "      <td>0.234738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092543</td>\n",
       "      <td>7.242487</td>\n",
       "      <td>12.433276</td>\n",
       "      <td>9.254301</td>\n",
       "      <td>23.437098</td>\n",
       "      <td>9.643516</td>\n",
       "      <td>31.048116</td>\n",
       "      <td>23.473798</td>\n",
       "      <td>17.947365</td>\n",
       "      <td>29.362617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1311.000000</td>\n",
       "      <td>78.938239</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63050.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271248</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>21.786834</td>\n",
       "      <td>27.124774</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.577624</td>\n",
       "      <td>1.708691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1746.000000</td>\n",
       "      <td>150.049915</td>\n",
       "      <td>748.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>84000.000000</td>\n",
       "      <td>0.603254</td>\n",
       "      <td>0.492701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.155556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.338614</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>30.257009</td>\n",
       "      <td>33.861386</td>\n",
       "      <td>60.325407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.270073</td>\n",
       "      <td>15.555556</td>\n",
       "      <td>13.071077</td>\n",
       "      <td>5.760017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2409.000000</td>\n",
       "      <td>326.435557</td>\n",
       "      <td>3085.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>104000.000000</td>\n",
       "      <td>0.789809</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.405316</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39.347768</td>\n",
       "      <td>40.531561</td>\n",
       "      <td>78.980892</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>67.567568</td>\n",
       "      <td>38.095238</td>\n",
       "      <td>23.933577</td>\n",
       "      <td>16.003817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10491.000000</td>\n",
       "      <td>42198.002649</td>\n",
       "      <td>19110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>203000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.701342</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>81.132075</td>\n",
       "      <td>70.134228</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.322581</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>125.664465</td>\n",
       "      <td>517.608658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              bgpop       bgacres        bgjobs     bgmedkids      bgmedhhs  \\\n",
       "count  16560.000000  16560.000000  16560.000000  16560.000000  16560.000000   \n",
       "mean    2278.621558   1677.654083   2619.841486      0.046135      2.121377   \n",
       "std     1692.329312   7599.460446   3897.735448      0.209784      0.623896   \n",
       "min        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
       "25%     1311.000000     78.938239    247.000000      0.000000      2.000000   \n",
       "50%     1746.000000    150.049915    748.000000      0.000000      2.000000   \n",
       "75%     2409.000000    326.435557   3085.000000      0.000000      2.000000   \n",
       "max    10491.000000  42198.002649  19110.000000      1.000000      4.000000   \n",
       "\n",
       "            bgmedinc      proprent     propwhite     propblack     propasian  \\\n",
       "count   16560.000000  16560.000000  16560.000000  16560.000000  16560.000000   \n",
       "mean    86422.977959      0.593939      0.436069      0.035347      0.220206   \n",
       "std     29934.893921      0.234371      0.310481      0.096435      0.234738   \n",
       "min     21300.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%     63050.000000      0.450000      0.000000      0.000000      0.000000   \n",
       "50%     84000.000000      0.603254      0.492701      0.000000      0.155556   \n",
       "75%    104000.000000      0.789809      0.675676      0.037037      0.380952   \n",
       "max    203000.000000      1.000000      1.000000      0.903226      1.000000   \n",
       "\n",
       "           ...           prop2per    bgmedagehd       pct1per       pct2per  \\\n",
       "count      ...       16560.000000  16560.000000  16560.000000  16560.000000   \n",
       "mean       ...           0.336906     45.766697     31.276702     33.690634   \n",
       "std        ...           0.092543      7.242487     12.433276      9.254301   \n",
       "min        ...           0.000000     33.000000      0.000000      0.000000   \n",
       "25%        ...           0.271248     40.000000     21.786834     27.124774   \n",
       "50%        ...           0.338614     46.500000     30.257009     33.861386   \n",
       "75%        ...           0.405316     52.000000     39.347768     40.531561   \n",
       "max        ...           0.701342     64.000000     81.132075     70.134228   \n",
       "\n",
       "            pctrent      pctblack      pctwhite      pctasian      bgpopden  \\\n",
       "count  16560.000000  16560.000000  16560.000000  16560.000000  16559.000000   \n",
       "mean      59.393853      3.534688     43.606874     22.020556     17.852474   \n",
       "std       23.437098      9.643516     31.048116     23.473798     17.947365   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       45.000000      0.000000      0.000000      0.000000      6.577624   \n",
       "50%       60.325407      0.000000     49.270073     15.555556     13.071077   \n",
       "75%       78.980892      3.703704     67.567568     38.095238     23.933577   \n",
       "max      100.000000     90.322581    100.000000    100.000000    125.664465   \n",
       "\n",
       "           bgjobden  \n",
       "count  16559.000000  \n",
       "mean      14.368919  \n",
       "std       29.362617  \n",
       "min        0.003252  \n",
       "25%        1.708691  \n",
       "50%        5.760017  \n",
       "75%       16.003817  \n",
       "max      517.608658  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# census features\n",
    "census_features = ['bgpop', 'bgacres', 'bgjobs', 'bgmedkids', 'bgmedhhs',\n",
    "       'bgmedinc', 'proprent', 'propwhite','propblack', 'propasian', 'pumahhden', 'prop1per', 'prop2per', 'bgmedagehd',\n",
    "    'pct1per', 'pct2per', 'pctrent','pctblack', 'pctwhite', 'pctasian', 'bgpopden', 'bgjobden']\n",
    "df[census_features].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--> **accessibility**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lowinc1500m</th>\n",
       "      <th>highinc1500m</th>\n",
       "      <th>lnjobs5000m</th>\n",
       "      <th>lnjobs30km</th>\n",
       "      <th>lnpop400m</th>\n",
       "      <th>lnpop800m</th>\n",
       "      <th>lnjobs800m</th>\n",
       "      <th>lntcpuw3000m</th>\n",
       "      <th>pumajobden</th>\n",
       "      <th>lnjobs40km</th>\n",
       "      <th>lnret3000m</th>\n",
       "      <th>lnfire3000m</th>\n",
       "      <th>lnserv3000m</th>\n",
       "      <th>highlowinc1500m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "      <td>16560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.677033</td>\n",
       "      <td>6.074045</td>\n",
       "      <td>9.956677</td>\n",
       "      <td>12.693569</td>\n",
       "      <td>6.640413</td>\n",
       "      <td>7.507952</td>\n",
       "      <td>6.162258</td>\n",
       "      <td>5.626036</td>\n",
       "      <td>11.706303</td>\n",
       "      <td>13.053952</td>\n",
       "      <td>7.106154</td>\n",
       "      <td>5.885792</td>\n",
       "      <td>8.170593</td>\n",
       "      <td>1.117190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.386705</td>\n",
       "      <td>1.305157</td>\n",
       "      <td>1.265675</td>\n",
       "      <td>0.802371</td>\n",
       "      <td>1.669962</td>\n",
       "      <td>1.748253</td>\n",
       "      <td>1.823210</td>\n",
       "      <td>1.675362</td>\n",
       "      <td>22.086445</td>\n",
       "      <td>0.681933</td>\n",
       "      <td>1.703126</td>\n",
       "      <td>1.556564</td>\n",
       "      <td>1.280461</td>\n",
       "      <td>0.570569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100059</td>\n",
       "      <td>0.627523</td>\n",
       "      <td>4.230915</td>\n",
       "      <td>10.523860</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068592</td>\n",
       "      <td>10.822515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.406504</td>\n",
       "      <td>3.202994</td>\n",
       "      <td>0.633968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.313185</td>\n",
       "      <td>5.740918</td>\n",
       "      <td>9.374096</td>\n",
       "      <td>12.428492</td>\n",
       "      <td>6.383507</td>\n",
       "      <td>7.027315</td>\n",
       "      <td>5.185174</td>\n",
       "      <td>4.509885</td>\n",
       "      <td>1.295310</td>\n",
       "      <td>13.007757</td>\n",
       "      <td>6.380528</td>\n",
       "      <td>5.047726</td>\n",
       "      <td>7.619956</td>\n",
       "      <td>0.975782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.830030</td>\n",
       "      <td>6.299926</td>\n",
       "      <td>10.000783</td>\n",
       "      <td>13.065360</td>\n",
       "      <td>7.015712</td>\n",
       "      <td>7.921173</td>\n",
       "      <td>6.351109</td>\n",
       "      <td>5.814377</td>\n",
       "      <td>3.821264</td>\n",
       "      <td>13.357129</td>\n",
       "      <td>7.332416</td>\n",
       "      <td>5.707664</td>\n",
       "      <td>8.017110</td>\n",
       "      <td>1.058767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.413313</td>\n",
       "      <td>6.709478</td>\n",
       "      <td>10.415682</td>\n",
       "      <td>13.215977</td>\n",
       "      <td>7.596392</td>\n",
       "      <td>8.391403</td>\n",
       "      <td>7.335358</td>\n",
       "      <td>6.623755</td>\n",
       "      <td>10.404358</td>\n",
       "      <td>13.445881</td>\n",
       "      <td>7.971928</td>\n",
       "      <td>6.544980</td>\n",
       "      <td>8.590465</td>\n",
       "      <td>1.183156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.997189</td>\n",
       "      <td>8.520856</td>\n",
       "      <td>12.594025</td>\n",
       "      <td>13.490251</td>\n",
       "      <td>8.947025</td>\n",
       "      <td>10.003016</td>\n",
       "      <td>9.829233</td>\n",
       "      <td>9.110520</td>\n",
       "      <td>82.100619</td>\n",
       "      <td>13.672037</td>\n",
       "      <td>10.514658</td>\n",
       "      <td>10.297959</td>\n",
       "      <td>11.515756</td>\n",
       "      <td>22.039128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lowinc1500m  highinc1500m   lnjobs5000m    lnjobs30km     lnpop400m  \\\n",
       "count  16560.000000  16560.000000  16560.000000  16560.000000  16560.000000   \n",
       "mean       5.677033      6.074045      9.956677     12.693569      6.640413   \n",
       "std        1.386705      1.305157      1.265675      0.802371      1.669962   \n",
       "min        0.100059      0.627523      4.230915     10.523860      0.000000   \n",
       "25%        5.313185      5.740918      9.374096     12.428492      6.383507   \n",
       "50%        5.830030      6.299926     10.000783     13.065360      7.015712   \n",
       "75%        6.413313      6.709478     10.415682     13.215977      7.596392   \n",
       "max        8.997189      8.520856     12.594025     13.490251      8.947025   \n",
       "\n",
       "          lnpop800m    lnjobs800m  lntcpuw3000m    pumajobden    lnjobs40km  \\\n",
       "count  16560.000000  16560.000000  16560.000000  16560.000000  16560.000000   \n",
       "mean       7.507952      6.162258      5.626036     11.706303     13.053952   \n",
       "std        1.748253      1.823210      1.675362     22.086445      0.681933   \n",
       "min        0.000000      0.000000      0.000000      0.068592     10.822515   \n",
       "25%        7.027315      5.185174      4.509885      1.295310     13.007757   \n",
       "50%        7.921173      6.351109      5.814377      3.821264     13.357129   \n",
       "75%        8.391403      7.335358      6.623755     10.404358     13.445881   \n",
       "max       10.003016      9.829233      9.110520     82.100619     13.672037   \n",
       "\n",
       "         lnret3000m   lnfire3000m   lnserv3000m  highlowinc1500m  \n",
       "count  16560.000000  16560.000000  16560.000000     16560.000000  \n",
       "mean       7.106154      5.885792      8.170593         1.117190  \n",
       "std        1.703126      1.556564      1.280461         0.570569  \n",
       "min        0.000000      0.406504      3.202994         0.633968  \n",
       "25%        6.380528      5.047726      7.619956         0.975782  \n",
       "50%        7.332416      5.707664      8.017110         1.058767  \n",
       "75%        7.971928      6.544980      8.590465         1.183156  \n",
       "max       10.514658     10.297959     11.515756        22.039128  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accessibility features\n",
    "access_features = ['lowinc1500m', 'highinc1500m', 'lnjobs5000m','lnjobs30km', 'lnpop400m', 'lnpop800m', 'lnjobs800m','lntcpuw3000m',\n",
    "       'pumajobden', 'lnjobs40km', 'lnret3000m', 'lnfire3000m', 'lnserv3000m','highlowinc1500m']\n",
    "df[access_features].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def log_var(x):\n",
    "    \"\"\"Return log of x, but NaN if zero.\"\"\"\n",
    "    if x==0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return np.log(x)\n",
    "\n",
    "# add ln rent\n",
    "df['lnrent'] = df.rent.apply(log_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting the columns, some are obviously not useful or redundant. E.g., 'median_income' is just the median income for the entire region. \n",
    "\n",
    "Define the columns we'll use..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y variable: rent_sqft\n",
      "Using 49 covariates\n"
     ]
    }
   ],
   "source": [
    "cols_to_use = ['rent','rent_sqft','lnrent', 'sqft','bedrooms','bathrooms','longitude', 'latitude','bgpop','bgjobs', 'bgmedkids', 'bgmedhhs','bgmedinc', 'proprent', 'lowinc1500m', 'highinc1500m', 'lnjobs5000m',\n",
    "       'lnjobs30km', 'lnpop400m', 'lnpop800m', 'lnjobs800m', 'propwhite','propblack', 'propasian', 'pumahhden', 'lnbasic3000m', 'lntcpuw3000m',\n",
    "       'pumajobden', 'lnjobs40km', 'lnret3000m', 'lnfire3000m', 'lnserv3000m','prop1per', 'prop2per', 'bgmedagehd', 'puma1', 'puma2', 'puma3',\n",
    "       'puma4', 'northsf', 'pct1per', 'pct2per', 'pctrent','pctblack', 'pctwhite', 'pctasian', 'y17jan', 'y17feb', 'y17mar',\n",
    "       'bgpopden', 'bgjobden', 'highlowinc1500m']\n",
    "\n",
    "x_cols = ['sqft','bedrooms','bathrooms','longitude', 'latitude','bgpop','bgjobs', 'bgmedkids', 'bgmedhhs',\n",
    "       'bgmedinc', 'proprent', 'lowinc1500m', 'highinc1500m', 'lnjobs5000m','lnjobs30km', 'lnpop400m', 'lnpop800m', 'lnjobs800m', 'propwhite',\n",
    "       'propblack', 'propasian', 'pumahhden', 'lnbasic3000m', 'lntcpuw3000m','pumajobden', 'lnjobs40km', 'lnret3000m', 'lnfire3000m', 'lnserv3000m',\n",
    "       'prop1per', 'prop2per', 'bgmedagehd', 'puma1', 'puma2', 'puma3','puma4', 'northsf', 'pct1per', 'pct2per', 'pctrent',\n",
    "       'pctblack', 'pctwhite', 'pctasian', 'y17jan', 'y17feb', 'y17mar','bgpopden', 'bgjobden', 'highlowinc1500m']\n",
    "y_col = 'rent_sqft'\n",
    "\n",
    "print('Y variable:',y_col)\n",
    "print('Using {} covariates'.format(len(x_cols)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total rows: 16560\n",
      "excluding NAs: 8253\n"
     ]
    }
   ],
   "source": [
    "df = df[cols_to_use]\n",
    "print('total rows:',len(df))\n",
    "df_notnull = df.dropna(how='any')\n",
    "print('excluding NAs:',len(df_notnull))\n",
    "df = df_notnull"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About half of rows have bathrooms missing. Turns out this is because bathrooms was only added to the scraper at the end of December. Is it better to exclude the bathrooms feature and use all the data? Or better to include the bathrooms feature and only use data collected beginning in January? I tried it both ways and the model is more accurate (and more interesting) when we include the bathrooms feature. Plus, all the data we collect from now on will have bathrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable distributions\n",
    "\n",
    "\n",
    "Look at distributions to get a better sense of what the variables mean and to look for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M/BHgVZKuGuJ+WMH5KiozMxsVJPVGhE+LjxJuljMzs8KTtBZwXz+zvxQRN7cyHis+t+CY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c100b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_rows = math.ceil(len(cols_to_use)/2)\n",
    "\n",
    "f, axes = plt.subplots(plot_rows,2, figsize=(8,35))\n",
    "sns.despine(left=True)\n",
    "\n",
    "for i,col in enumerate(cols_to_use):\n",
    "    row_position = math.floor(i/2)\n",
    "    col_position = i%2\n",
    "    sns.distplot(df_notnull[col], ax=axes[row_position, col_position],kde=False)\n",
    "    axes[row_position, col_position].set_title('{}'.format(col)) \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b91ab00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KpNaKi555qY90xmHr+lr9Se5Bs9drefPV63j4qVe4/7ETTKezvPfNW/SayprR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b290860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['rent_sqft'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# $/sqft as a function of sqft \n",
    "#plt.scatter(df.sqft, df.rent_sqft)\n",
    "#plt.ylabel('rent per sq ft')\n",
    "#plt.xlabel('sqft')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean rent: $2995  | Median: $2743\n",
      "Mean rent per square foot: $3.24  | Median: $3.10\n",
      "Mean sqft: 969.24  | Median: 925.00\n"
     ]
    }
   ],
   "source": [
    "# Some useful summary stats\n",
    "print('Mean rent: ${:.0f}'.format(df.rent.mean()),' | Median: ${:.0f}'.format(df.rent.median()))\n",
    "print('Mean rent per square foot: ${:.2f}'.format(df.rent_sqft.mean()),' | Median: ${:.2f}'.format(df.rent_sqft.median()))\n",
    "print('Mean sqft: {:.2f}'.format(df.sqft.mean()),' | Median: {:.2f}'.format(df.sqft.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create some additional features that might help\n",
    "\n",
    "(Second iteration)\n",
    "\n",
    "In the first iteration of models, there appeared to be an interaction effect with # bedrooms and # bathrooms; i.e., the number of bathrooms per bedroom seems to matter. E.g., a property with 4 bedrooms is probably not very desirable if it only has 1 bathroom, but might be much more desirable if it has 4 bedrooms and 3 bathrooms. So let's try adding a feature for bathrooms per bedroom.  (Turns out this improved the linear model but not GB.. as expected.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bath_var(row):\n",
    "    \"\"\"Make bathrooms/bedroom variable. \"\"\"\n",
    "    # Avoid 0 in demoninator. When bedrooms = 0, it's probably a studio. so for practical purposes, br=1\n",
    "    if row['bedrooms']==0:\n",
    "        br = 1\n",
    "    else:\n",
    "        br = row['bedrooms']\n",
    "    return row['bathrooms']/br\n",
    "\n",
    "# add a variable bath_bed (bathrooms per bedroom)\n",
    "df['bath_bed'] = df.apply(bath_var, axis=1)\n",
    "cols_to_use.append('bath_bed')\n",
    "x_cols.append('bath_bed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also appears the relationship between rent and sqft is not linear. Let's add a sqft2 term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['sqft2'] = df['sqft']**2\n",
    "cols_to_use.append('sqft2')\n",
    "x_cols.append('sqft2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear model\n",
    "\n",
    "Try a linear model first, using rent/sqft as Y.\n",
    "(I previously tried using lnrent as dependent variable, but rent/sqft results in a more accurate predictive model, and is more conceptual correct.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model, model_selection\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the data\n",
    "df_y = df[y_col]\n",
    "df_X = df[x_cols]\n",
    "#sns.distplot(df['rent_sqft'])\n",
    "#Downside of using $/sqft rather than lnrent is that the distribution is not as normally distributed. It's not that bad though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split into training and test sets\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(df[x_cols],df[y_col], test_size = .3, random_state = 201)\n",
    "\n",
    "# fit the model\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.2770\n",
      "RMSE: 0.5263\n",
      "Variance score: 0.7326\n",
      "R2: 0.7326\n"
     ]
    }
   ],
   "source": [
    "# See mean square error, using test data\n",
    "print(\"Mean squared error: {:.4f}\".format(np.mean((regr.predict(X_test) - y_test) ** 2)))\n",
    "print(\"RMSE: {:.4f}\".format(np.sqrt(np.mean((regr.predict(X_test) - y_test) ** 2))))\n",
    "# Explained variance score: 1 is perfect prediction. \n",
    "print('Variance score: {:.4f}'.format(regr.score(X_test, y_test)))\n",
    "print('R2: {:.4f}'.format(r2_score(y_test, regr.predict(X_test))))  # ok, R2 is same as variance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Intercept\n",
    "#print('Intercept:', regr.intercept_)\n",
    "\n",
    "# The coefficients\n",
    "#print('Coefficients:')\n",
    "#pd.Series(regr.coef_, index=x_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These coefficients aren't very useful without the standard errors. If I were interested in building a model for interpretation, I would use the statsmodels package, which has more functionality for statistical tests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This variance (0.66) is a bit lower than the linear model using lnrent as Y (0.69), which is what we'd expect when we take the most important covariate and put it into the dependent variable. Still not a bad score here. Not great either, though. \n",
    "\n",
    "Added sqft as feature, with rent_sqft still as Y: \n",
    "- Mean squared error: 0.32\n",
    "- RMSE: 0.56438373549\n",
    "- Variance score: 0.69\n",
    "\n",
    "Added bath/bedrooms feature\n",
    "\n",
    "- Mean squared error: 0.31\n",
    "- RMSE: 0.559876928818\n",
    "- Variance score: 0.70\n",
    "\n",
    "Added sqft^2 feature\n",
    "\n",
    "- Mean squared error: 0.28\n",
    "- RMSE: 0.526323190891\n",
    "- Variance score: 0.73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bmjalr/Jy4LHHTLz8so5evUKvo6ys7usRxSqGAezfT9W4u3cDmzd7gsUpiWAP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\npdgNHRp6rcXFwMaNOjZuTG1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VV3Wcfz7Fm0tKgGeeMTBvHvWoTsQrTMb7TTfpCKkku008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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d1bd8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It's always a good idea to plot the residuals to look for gross violations of linear regression.\n",
    "# Plot predicted values vs. observed\n",
    "plt.scatter(regr.predict(X_train),y_train, color='blue',s=1, alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpEz2W/nw68G6LB/pjb1CyQ2LX5n4mT/jVYsY9GA5ixYqmBNwg6gzQSgF2OUZ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\nEATBhfw/axNEp4B7bp0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122820240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot residuals vs predicted values\n",
    "plt.scatter(regr.predict(X_train), regr.predict(X_train)- y_train, color='blue',s=1, alpha=.5)\n",
    "plt.scatter(regr.predict(X_test), regr.predict(X_test)- y_test, color='green',s=1, alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set. Mean squared error: 0.24817 | Variance score: 0.74952\n",
      "Test set. Mean squared error: 0.27702 | Variance score: 0.73259\n"
     ]
    }
   ],
   "source": [
    "# Look for overfitting\n",
    "print(\"Training set. Mean squared error: {:.5f}\".format(np.mean((regr.predict(X_train) - y_train) ** 2)), '| Variance score: {:.5f}'.format(regr.score(X_train, y_train)))\n",
    "print(\"Test set. Mean squared error: {:.5f}\".format(np.mean((regr.predict(X_test) - y_test) ** 2)), '| Variance score: {:.5f}'.format(regr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does not look like severe overfitting. (If there were, we'd probably have a much smaller error on training set and a larger error on test set.')\n",
    "We do have a problem with colinearity and if we were using this model for interpretation we'd have to do better feature selection..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Gradient boosting model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble.partial_dependence import plot_partial_dependence\n",
    "from sklearn.ensemble.partial_dependence import partial_dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def RMSE(y_actual, y_predicted):\n",
    "    return np.sqrt(mean_squared_error(y_actual, y_predicted))\n",
    "\n",
    "def cross_val_gb(X,y,cv_method='kfold',k=5, **params):\n",
    "    \"\"\"Estimate gradient boosting regressor using cross validation and return RMSE.\n",
    "    \n",
    "    Args: \n",
    "        X (DataFrame): features data\n",
    "        y (Series): target data\n",
    "        cv_method (str): how to split the data ('kfold' (default) or 'timeseries')\n",
    "        k (int): number of folds (default=5)\n",
    "        **params: keyword arguments for regressor\n",
    "    Returns: \n",
    "        float: mean error (RMSE) across all training/test sets.\n",
    "    \"\"\"\n",
    "    if cv_method == 'kfold':\n",
    "        kf = KFold(n_splits=k, shuffle=True, random_state=2012016)  # use random seed for reproducibility. \n",
    "    \n",
    "    E = np.ones(k)   # this array will hold the errors. \n",
    "    i=0\n",
    "    for train, test in kf.split(X, y): \n",
    "        train_data_x = X.iloc[train]\n",
    "        train_data_y = y.iloc[train]    \n",
    "        test_data_x = X.iloc[test]\n",
    "        test_data_y = y.iloc[test]\n",
    "\n",
    "        # n_estimators is number of trees to build. \n",
    "        grad_boost = GradientBoostingRegressor(loss='ls',criterion='mse', **params)\n",
    "        grad_boost.fit(train_data_x,train_data_y)\n",
    "        predict_y=grad_boost.predict(test_data_x)\n",
    "        E[i] = RMSE(test_data_y, predict_y)\n",
    "        i+=1\n",
    "    return np.mean(E)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8253, 49)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the data\n",
    "# because GB can model nonlinear relationships and interaction effects, we don't need sqft^2 or bed_bath as features. \n",
    "x_cols = ['sqft','bedrooms','bathrooms','longitude', 'latitude','bgpop','bgjobs', 'bgmedkids', 'bgmedhhs',\n",
    "       'bgmedinc', 'proprent', 'lowinc1500m', 'highinc1500m', 'lnjobs5000m','lnjobs30km', 'lnpop400m', 'lnpop800m', 'lnjobs800m', 'propwhite',\n",
    "       'propblack', 'propasian', 'pumahhden', 'lnbasic3000m', 'lntcpuw3000m','pumajobden', 'lnjobs40km', 'lnret3000m', 'lnfire3000m', 'lnserv3000m',\n",
    "       'prop1per', 'prop2per', 'bgmedagehd', 'puma1', 'puma2', 'puma3','puma4', 'northsf', 'pct1per', 'pct2per', 'pctrent',\n",
    "       'pctblack', 'pctwhite', 'pctasian', 'y17jan', 'y17feb', 'y17mar','bgpopden', 'bgjobden', 'highlowinc1500m']\n",
    "\n",
    "df_X = df[x_cols]\n",
    "y_col = 'rent_sqft'\n",
    "df_y = df[y_col]\n",
    "df_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52580919721081787"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit initial model (no paramter tuning)\n",
    "params = {'n_estimators':100,\n",
    "          'learning_rate':0.1,\n",
    "          'max_depth':1,\n",
    "          'min_samples_leaf':4\n",
    "          }\n",
    "grad_boost = GradientBoostingRegressor(loss='ls',criterion='mse', **params)\n",
    "grad_boost.fit(df_X,df_y)\n",
    "cross_val_gb(df_X,df_y, **params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without parameter tuning, error is about the same as linear model. RMSE: 0.5258\n",
    "\n",
    "**Tune the parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# find the optimal parameters\n",
    "param_grid = {'learning_rate':[.5 ,.1, .05],\n",
    "              'max_depth':[2,4,6,12],\n",
    "              'min_samples_leaf': [5,9,17],\n",
    "              'max_features': [1, .3, .1]\n",
    "              }\n",
    "\n",
    "est= GradientBoostingRegressor(n_estimators = 500)\n",
    "gs_cv = GridSearchCV(est,param_grid).fit(df_X,df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'max_depth': 2, 'max_features': 0.3, 'min_samples_leaf': 5}\n",
      "0.649303767746\n"
     ]
    }
   ],
   "source": [
    "# get optimal parameters\n",
    "print(gs_cv.best_params_)\n",
    "print(gs_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37294082132692341"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model with best parameters\n",
    "params = {'n_estimators':500,\n",
    "          'learning_rate':0.05,\n",
    "          'max_depth':4,\n",
    "          'min_samples_leaf':9,\n",
    "          'max_features':.3\n",
    "          }\n",
    "grad_boost = GradientBoostingRegressor(loss='ls',criterion='mse', **params)\n",
    "grad_boost.fit(df_X,df_y)\n",
    "cross_val_gb(df_X,df_y, **params, k=3)\n",
    "\n",
    "#RMSE: 0.37115"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.361721542416\n"
     ]
    }
   ],
   "source": [
    "# best parameters, using 1000 estimators\n",
    "params = {'n_estimators':1000,\n",
    "          'learning_rate':0.05,\n",
    "          'max_depth':4,\n",
    "          'min_samples_leaf':9,\n",
    "          'max_features':.3\n",
    "          }\n",
    "grad_boost = GradientBoostingRegressor(loss='ls',criterion='mse', **params)\n",
    "grad_boost.fit(df_X,df_y)\n",
    "rmse_gb = cross_val_gb(df_X,df_y, **params, k=3)\n",
    "print(rmse_gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**without sqft as covariate**\n",
    "- RMSE with 500 estimators: 0.4708\n",
    "- RMSE with 1000 estimators: 0.4658\n",
    "- linear model: 0.5965\n",
    "\n",
    "\n",
    "**with sqft as covariate**\n",
    "- RMSE with 500 estimators: 0.3712\n",
    "- RMSE with 1000 estimators: 0.3620\n",
    "- linear model: 0.5644\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation and interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approx. percent error: 11.2%\n",
      "Expected prediction error for the average housing unit: $351\n"
     ]
    }
   ],
   "source": [
    "# how \"well\" does the model do? \n",
    "\n",
    "print('Approx. percent error: {:.1f}%'.format(rmse_gb/df.rent_sqft.mean()*100))\n",
    "\n",
    "print('Expected prediction error for the average housing unit: ${:.0f}'.format(rmse_gb*df.sqft.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7Ek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\nvkw9O71GTibFcKHMFmPRQf0lOvi2RCDy3njuCOSz/G7MtxWIwEuklO/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QBoXXgEPMbFN3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\nkkkmmWSSCQD/D2usB2jC4Od0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1230196d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the importances (Relative influence of each variable)\n",
    "gb_o = pd.DataFrame({'features':x_cols,'importance':grad_boost.feature_importances_})\n",
    "gb_o= gb_o.sort_values(by='importance',ascending=False)\n",
    "\n",
    "\n",
    "plt.figure(1,figsize=(12, 6))\n",
    "plt.xticks(range(len(gb_o)), gb_o.features,rotation=45)\n",
    "plt.plot(range(len(gb_o)),gb_o.importance,\"o\")\n",
    "plt.title('Feature importances')\n",
    "plt.savefig(os.path.join(IMAGE_DIR, 'ft_importances.png'), dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# choose features for partial dependent plots\n",
    "#for i,col in enumerate(df_X.columns): \n",
    "#    print(i,col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qq2Hh43m5uSxYoxosTYnNppl9D+EJnYrh7p1SiHckUNvdB5rZAOB24IgwXg3g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\nygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12030f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = [0,1,2,3,4,9,46,14,31,48,10]\n",
    "names = df_X.columns\n",
    "fig, axs = plot_partial_dependence(grad_boost, df_X, features,feature_names=names, grid_resolution=50, figsize = (10,10))\n",
    "fig.suptitle('Partial dependence of rental price features')\n",
    "plt.subplots_adjust(top=0.9)  \n",
    "plt.savefig(os.path.join(IMAGE_DIR, 'partial_depend.png'), dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The partial dependence plots show how predicted values vary with the given covariate, \"controlling for\" the influence of other covariates ([Friedman, 2001](http://www.jstor.org/stable/pdf/2699986.pdf)). \n",
    "\n",
    "The first plot suggests a slightly nonlinear relationship between sqft and rent/sqft, which is what we predicted. The effect of additional floor area diminishes as the housing unit grows larger. There's an interesting bump around 1500sqft, which is hard to explain.\n",
    "\n",
    "The lnjobs30km (log of number of jobs within 30km) also has some interesting non-linearities. There seem to be thresholds at which the concentration of jobs is high enough to have an effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dJ6B5fX2Nr1MWqT79Ty+PAWv7FebcubNER0ej1+sZOHAQ8fEJHD6cR0NDA42N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KqqlpPw3we+UCZ8XJmw48qEtdamZNZfjB79Cb8J38zMmpJfxGoNa/XqNX1dBDMzs5rwWpDWsF55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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122796ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = [(0,1),(0,2),(1,2),(3,4),(5,46)]\n",
    "names = df_X.columns\n",
    "fig, axs = plot_partial_dependence(grad_boost, df_X, features,feature_names=names, grid_resolution=50, figsize = (9,6))\n",
    "fig.suptitle('Partial dependence of rental price features')\n",
    "plt.subplots_adjust(top=0.9)  # tight_layout causes overlap with suptitle\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These partial dependence contour plots show the interactions between covariates. We can see how this works in the third plot with bathrooms and bedrooms. For a house with 1 bedroom and 1 bathroom, adding an additional bathroom doesn't increase price that much. But a house with 3 bedrooms does get a price lift from having 2 bathrooms rather than 1. This all makes sense becuase you'd rarely need more bathrooms than bedrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error analysis\n",
    "- plot errors\n",
    "- map errors by long/lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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94+za5eOf/inCsGGqMP/bbwf45BOV7HLEEeq+7f9sqeKkk6I88YRSvx/+0GTy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\nzP8HN5bIP6uNNG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d202f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = grad_boost.predict(df_X)\n",
    "# plot predicted vs. actual\n",
    "\n",
    "plt.scatter(y_pred,df_y, color='blue',s=1, alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YwLhxMs4+O4gpUxR07Wqirs6D2loPDhxQ0KyZhdWrI4s9/PGPBnw+4LbbIrOU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170e4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot errors vs. predicted\n",
    "plt.scatter(y_pred, y_pred-df_y, color='blue',s=1,alpha=.5 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1235be630>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mmJgudrMkkXXTgiDpS5VauKqz/Y68VKlTbzS6WZbIuijIpS9Vgo481WZHHm6spXFyiSIF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\n/wOkCxr4JWbZlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235196a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_pred-df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rcGhh0eJe1g0cyEe7znt1m++24+ISOiEZdwvBK3PwYRhVuVICXvDWR11CpNT4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\nBvZUvoYTreNfgwuZSGm/sTkk+VnVanb54rwnUca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/pDHmT7mMHQaOf6D3UWt9Knn2HS4W\nMmWDEEIUmbw61SOEECL7JPELIUSRkcQvhBBFRhK/EEIUGUn8QghRZCTxCyFEkZHEL4QQRUYSvxBC\nFJn/BwdFk+OZr68VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120d079e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import Normalize\n",
    "# map the errors. \n",
    "x = df_X['longitude']\n",
    "y = df_X['latitude']\n",
    "z = y_pred-df_y\n",
    "\n",
    "norm = Normalize(vmin=-1,vmax=1)  # zoom in on middle of error range\n",
    "\n",
    "plt.figure(figsize=(6,9))\n",
    "plt.scatter(x,y, c=z, cmap='jet',s=8,alpha=.5,edgecolors='face', norm=norm)\n",
    "#plt.xlim(-122.6,-122)\n",
    "#plt.ylim(37.6,38)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# obs where abs(error)>0.8: 168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lisa/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \n",
      "/Users/lisa/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:9: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
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KSEvZQMx/eZW/FDtiuDTtgqRuis9oCUWkpbRV/xsDQZqFSN0VlimBi7SUhXi8\nLY55CTgl7WKkropPKYGLtJwj8Dgi7SKk7grLmy+BG2NmAl8DZlD5B+FfWmvvS24EEZEWUHyyKRP4\nXwI/s9b+ozHGAP8KHL+/E9TARSRzCgc3XwIH/gEYGnHyYOIjiIi4rrg+3QRujHkv8OG9Hr7MWvtL\nY8x8KkspV413HTVwEcmctBO4tfYm4Ka9HzfGrAC+CXzEWvtfkx9BRKRFpZ3AR2OMORL4DvAn1tpf\n13KOGriIZE7h0KZcA78e6AA+V3kOkx3W2vMSHUFExHXFdc2XwMdr1qNRAxeRzCkc1pQJvAlHEBFp\nMsXfNF8Cnww1cMmEMIa7BgfIs5kzOhbj6UaDmVYwSuAizvjnXeu5c3AbJa+NJ3b/kA/2XpF2SZKi\n4hNK4CLOeH7oCSIW4lNmS1n3Gsw6JXARh1zWdTD/sOsp8GNOzD2fdjmSMiVwEYcsm7aCf5q2gnI0\nSM5/R9rlSMoKRyiBizgn53ekXYI0geJjSuAiTa/EAxC8AOFp/G7PHck8bWos0uRyTzDMT/CDEpG3\nHYa1dCIVhaO0hCLS5IZGfN4ikUsSUXxESygiza18LHlCotImGH5T2tVIE1ECF2l6Hm28GYZ3pV2I\nNBklcBERRxVWKIGLZN5Wr0xX7NOBmzdXeeTR7zI4/Cy9s09n2bKT0y6nYYoPK4GLZNp9wRC/DIZp\nAy4Y6GJ27FZT2LLlBQaH7gE6eGXzDzPVwAtHZySBG2NywI2AAWLgCuBaYH71kKXAGmvthaOcOw94\nADjLWrsuoZpFmsKLuTJteMTEPJ8rM7vkVgPv7JxJHLcDEZ43I+1yGqr4kFs/q7HU8r+IcwGstQVj\nzCrguld3jjDG9AA/Z9/dlTHGBMCXgIHEqhVpIseGAfe2DTEt9jmy5N4Nsrq6ujnk4Kvoe+U3LF9+\nWtrlNFThmIwkcGvt7caYO6pfLgG2j/j2J4DPW2tfGuXUvwduAK6ZcpUiTeiQcsAhA+k17rbf/ju5\n7RaWngizzpjUNebMXsic2QsTrqz5FR/MTgLHWlsyxtwCnA+8C15bHnkzo6fv9wB91to7jTE1NfCe\nni7y+eb8Q+3t7U67hLrS/BxUHoYnnoDONtj0S3oPfiv4zfn3JwlJ/wxbJYF7cRzXfLAxZj6wFjgS\nuBTosdZeN8px91BZL4+BY4H1wNustZvGunZf367aC2mg3t5u+vpa93XEmp+j4piOdV/B3/0c0w48\ngr6FF42IbLBqAAAMm0lEQVR9bFSi6+X/gV96icGZ76Y0482NqzMBo/0Me3u7p/Syn5V/XKp7v1n9\nnXzdX5pUy5OYFwOLrLXXA/1AVP04E/jUaOdYa98w4vy7gSv217xFZII8j0HzZ3jhDqYtXAyb94x5\naLDrhwSDvwIvR+eOW9jlWAOvh8JxrZHAaxnhNuDmaqoOgKustQPGGAM8M/JAY8ytwLXW2ueSL1XE\nLY89uobOabM4ePnh9RnAzxG3zwbP3+9h5bbDiPHxojJR+7z61OKY4q9aY7mplicx9wAXjPL4UaM8\ndskoj62abHEirrrn+/+Mt/YXbPN8dr79Io47Mb17sUSdR7B7/mfIDz7B8Mx3plZHMykcn50ELiIT\nNLzpBdryAcQRO55dD6M08C0DUNyYY35XzMkLorrWE3WuYLhzRV3HcEnxlxlJ4CIycYtOP4cXf/gN\nvLY2jnvT6Kn3jmfyvNzv89hmmNsZsnxWUz6P35IKJyiBi8gYDj/iOA4/4rj9HuN78OqLwPKjLGNv\n9crcsuFrrAq+S4TPc6X3cf6Sc+tQbfYU71cCF5EpePshJe7bmGP+tJiDZuybvh/PlzicXzAt348H\neIN3Un1jtEyREriITEl3G/zB0rEbyUFln5+Gyziscz0xHtvKhzWwutamBC4idbUkCnj3oR/jrpdW\n0kUnlx12StoltYzCSUrgIlJnnXics0DbwSWuvi/6aRg1cBHJnOJ9WkIREXFS4fVaQhERcVJxtRK4\niIiTlMBFRBylBC4i4qjCqUrgIiJOKhaVwEVEnFRYqQQuIuIkJXARyaY4rHz4XWlXMmmFghK4iGSM\nV95Kx66v4MUDDHeeSamjkHZJk6IELiKZkwvX4cUheAH5cL2zDVwJXEQypxQcTX74Qfx4N2Hb8WmX\nM2lK4CKSPbnpDM74QNpVTJkSuIiIo4rFUfawc5AauIhkTqFQasAoQd1HUAMXaSXRIO3D38aLBxlq\n+yPi3KK0K2pKxaKXdgmJUAMXaSH50lpy5efB82gL72Eod9G45zwy/FEenp5jejTA2YNX05lf0IBK\n09WYNfD6UwMXaSHl3GIIY4hLlP35NZ2zrqvMoNdNf66Lx73rOZF/qnOV6SsW47RLSIQauEgLiXPL\nGei4HI/dRP7y6oNl2svfwmczw/4ZlP3X/d45c0t72JTrpZ1hFgwVwN03WNasUGiNTTHVwEVaTJw7\ngJgDXvs6Fz9GLnoavBxBVNyngb+x/I8s3fFlerw30NO1stHlpkIJXEScEHEQeHlgkIh917dzuQ6W\n5/6i8YWlqhGvQqk/NXCRFhf7PfTzQXy2EnmL0y6nKRTcvAPAPtTARbLAn07E9LSraBrFohK4iIiT\nCgW9E1NExEnFYph2CYlQAxeRzCkUdDdCEREnFYtDaZeQCDVwEcmcQqE1Wl9rzEJEZAKUwEXEOfcU\nH2D1vQ+xdNkiLrzgLWmXk5pCof63em0ENXCRDPnhv/8X4XCZF57to3DqMSxe3Pp3HhxNsTiYdgmJ\nUAMXyZCOjjaGBwcIghzd3dl9Y0+h0JZ2CYlQAxdpcpH3PORexCudgDfFXV6u/OCf8p8/W83rVhhm\nzepOqEL3FIv9aZewD2PMNOAbQA8wDFxqrX1xf+eogYs0sch7hbjzX4Ec5J7FG/qTKV1vXm8PF114\nTjLFOaxQ6Ei7hNFcDjxgrf2kMeY9wEeBD+3vBDVwkWbm7az8Bw+8gZSLaR3F4p60S9iHtfYfjTGv\nvsPoIGD7eOeogYs0MS86GMITwd8Mwy1yC70mkHYCN8a8F/jwXg9fZq39pTHmLmAFcNZ411EDF2li\nHh5eeEbaZbScYnF3quNba28Cbhrje2cYYw4HfggcvL/rqIGLSOYUCs23b5wx5hrgBWvtvwC7gXF3\nXlYDF5HfiSPay9/G5yVCfyUl//VpV1QXxeKutEsYzVeBW6rLKzngsvFOUAMXkdf48YvkoifA6yCI\n7ptQA78/P8wLuTIrSgGHlpu7tRQK09IuYR/W2peBCb09trn/lEWkoSKvl9ifgRfvJOKQms/b6ZW5\nr22YNjy2+cMcOtDcraVY3Jl2CYlo7j9lEWksr4OB3Afx4i3EXu1vs2+PfdrwiInpjGvb7SaKQiJ2\nk/d7JlvtFGhLNRFpRV47sXfghE5px+OCgQ425MocVRq/rYTRJob8PyKf62dHeDxz/G9MttpJKRRa\n412oauAikog5cY45pdp2utnF15jm7wFydPjr6lvYKIrFcd8j4wQ1cBFpuG7ezVD0dfK5AYYiw7QG\n7zFcKMxo7IB1ogYuIg0X+PPJRfdTjrYz2+9t+PjF4raGj1kPauAikgrfD/BpfPMGKBRmpjJu0tTA\nRSRzisUtaZeQCDVwEcmcQmFW2iUkQg1cRDKnWNycdgmJUAMXkcwpFGanXUIi1MBFJHOKxb60S0iE\nGriIZE6hMCftEhKhBi4imVMsvpJ2CYlQAxeRzCkU5qZdQiLUwEUkc4rFTWmXkAg1cBHJnEIhnXeA\nJm3cBl7d5v5GwAAxcAVwLTC/eshSYI219sIR5wRUtgdaCrQDn7LWfj/JwkVEJitLCfxcAGttwRiz\nCrjOWnsegDGmB/g58OG9znk3sMVae7ExZjbwMKAGLiJNoVCYl3YJiRi3gVtrbzfG3FH9cgkw8ka6\nnwA+b619aa/TvgN8t/q5R6tsfyEiLaFY3LtluammNXBrbckYcwtwPvAuAGPMPODN7Ju+sdburh7T\nTaWRXzveGD09XeTztd0MvtF6e1tj946xaH7ua/U5Jj2/QuGARK+XFi+O45oPNsbMB9YCRwKXAj3W\n2uvGOHYx8D3gi9bar4537b6+XbUX0kC9vd309e1Ku4y60fzc1+pzHG1+vb3d3lSuuXLl1+veb1av\n/tMp1ViLWp7EvBhYZK29HugHourHmcCnxjjnAOCnwAestT9LrlwRkSSU0y4gEbUsodwG3GyMuQcI\ngKustQPGGAM8M/JAY8ytVJZL/groAT5ujPl49dtnW2sHkitdRGRyCoUFaZeQiFqexNwDXDDK40eN\n8tgl1U8/VP0QEWk6xeLzaZeQCL2RR0Qyp1BYmHYJiWjwXtAiIpKUCb0KRUREmocSuIiIo9TARUQc\npQYuIuIoNXAREUepgYuIOEoNXETEUZl/I48x5nzgj621F1W/fjOVe7yEwCvAJdbafmPMZ4DTqPyZ\nfdlae+Ne1zkEuAFoA4aAC621Wxo3k9ElNb8R1/sYcPTIDTzSlODPb9TzGjeTsSU4x1OAz1G5vfNP\nrbWfaOA0xlTr/KrfOwT4nrV2xSjXORP4NJX5/ae1dty7oLou0wncGPM54Hp+/8/hi8DbrbVvAJ4E\n3meMeRNwiLX2VCp/Qa6ubmYx0peBa6vn3QAcVvcJjCPh+WGMORs4p/6V1ybh+e1zXt0nUIOE53gD\ncFH1+683xhxX9wmMo9b5VY+9GPgmMNZ+aJ8BLgFOBVYZY/Zp8q0m0w0cWA38+V6PrbLWvlz9PA8M\nAvcBf1Z9LAZyVNIBAMaYTmAecK4x5m4qv0D316/smiUyP3gt+bwf+Nu6VTtxic1vjPOaQVK/ozOA\ndmvt09baGLiTyh1F01br/AC2AW/cz7UeAmZTueleB61yy8H9yMQSijHmvey78cRl1tpvVbeJe82r\nuwsZY94BvAn4uLV2EBis7vV5C5V/nu4ecdps4Cjgg1TuxvgVKvdLH/c+6Emo9/yMMdOBL1BJN0fU\nbSJjaMDPb9Tz6jGXsTRgjjOAnSO+3gUsT3YWY5vq/KqP31F9fKxhHgXuALYAjwDrEiq/aWWigVtr\nbwJuqvV4Y8yHqew89JbqX4xX9//8LnB39d7oI20Fdllrf1499g7gLBrUwBswvz+gson1t4BZwIHG\nmP9urf10EvWPpwHzG/O8RmnAHHcCI7e16eb3t0esqyTmN87xs4BrgKOstS8aY/4Pldtaf2aSJTsh\n60so+zDG/A1wOnCmtXZz9bFO4GfAV621/3Pvc6r3OV9vjDm9+tAbgMcbVPKETHJ+t1lrj7HWrgKu\nAu5qVPOeqMnMb6zzmtUkf4Y7gWFjzMHGGA/4Q+AXDSy7ZpP8WQwAu6sfAC9R2ZOgpWUigdequpPQ\n3wIPAj+u/lPtW1TW05YDlxtjLq8efhmwDDjNWvtJ4L3AF4wxeWADcHWDyx/XFOfX9CY7P+BLo51n\nrf1/DZ1ADab4M7wC+DqV9fGfWmvXNrj8cY01v7F+FsaYM6jOzxjzV8BPjTGDVP518Z7GVJ0e3Y1Q\nRMRRWkIREXGUGriIiKPUwEVEHKUGLiLiKDVwERFHqYGLiDhKDVxExFFq4CIijvr/OaigcUaJRd8A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1203ae208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# it's hard to see a pattern in the above, but looks like errors are larger in the central city areas. \n",
    "# only show large errors, to make them more clear. \n",
    "# map the errors. \n",
    "\n",
    "err_cutoff = .8\n",
    "print('# obs where abs(error)>{}:'.format(err_cutoff),len(z[abs(z)>err_cutoff]))\n",
    "z_large_err = z[abs(z)>err_cutoff]\n",
    "x_large_err = x.ix[z_large_err.index,]\n",
    "y_large_err = y.ix[z_large_err.index,]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6,9))\n",
    "plt.scatter(x_large_err,y_large_err, c=z_large_err, cmap='jet',s=8,alpha=.5,edgecolors='face')\n",
    "\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A3H31gM0/cvf+kf2PgMXAI8DAv442/nT0P6TNm3eUGnpDaG9vS1TMSYsXFHMt\nJC1eqNCHTwLKLNm4BmZ2iZldEz3dTbiIbBE4F7h/mK5nAw8O2vaAmZ0e/XwOsI5wtH6WmbWa2UTg\nBOD50g9BRKTK+sp81EApI/MVwJ1m9giQB+a5+x4zM2DDwIZmthy43t1fITz7ZcOgfX0GWGxmBWAT\n8Gl3f9PMbgPWEH64XOfuXWUdlYhIBXUc1vg180wQJPL+aUESv+olKeakxQuKuRaSFi9Ae3tbptx9\nnHlrb1mJ8rEvNJUdQxxdNCQiEicBNXMlcxGROErmIiIpoGRef01bHyHX/V/0HHAmwX5H1jscEUmg\njimNPwGa6mSe2fMKzVtXQbaZlsIbdB35mXqHJCIJ1LmhthcNRadpfw+YADQDX3T3nw3XJ/Y880TL\njoNMEwQBQa613tGISFLV/jzzLwIPuvu7CRctvD2uQ6pH5kHLwex528fI7fkdvRPPqHc4IpJUta+Z\nfwPoX2ywCYi99ibVyRwgGHcUveOOqncYIpJgHUdXr2ZuZp9k7zWurnD3J6NVZb9HuCjhKH+DiIiE\nqjgyd/c7gDsGbzezk4DvA19294fj9qNkLiISo/PFmk+ATgN+APy1uz9bSh8lcxGROLWvmd9EeLOe\nW8NlsNjef1OgfVEyFxGJU+NkHpe4h6JkLiISo8N00ZCISOJ1/ipFdxoSERmztDaLiEgKKJmLiKSA\nkrmISPJ1nKgJUBGRxOt8ThOgIiLJpzKLiEgKKJmLiCRfx8mqmYuIJF7n06qZi4gkn8osIiIpoGQu\nIpICSuYiIsnX8Q5NgIqIJF7nk5oAFRFJPpVZRERSQMlcRCQFlMxFRJKv43RNgIqIJF7nz1IwAWpm\nOWAJYEAAXAVcD0yOmkwF1rr7nAF9Lgcuj562AqdG7acCiwm/tHQDl7r7a2Z2KzAT2BH1me3u20d/\nWCIiFZSSMsuFAO7eYWazgEXuPhvAzCYBq4H5Azu4+13AXVGb24Gl7r4tStqfc/dnzOxKYAHwRWA6\ncL67b6nEQYmIVFQakrm7rzSz+6KnU4BtA16+EVjs7huH6mtmM4AT3f3qaNOcAW2bgC4zywLHAt81\ns0OBO9x96SiORUSkKjre1fg180wQBCU1NLNlwEXAxe7+YzM7hHBUfrK7D3mkZraCMNmvHrT9TOAO\n4GygC/gCcAuQi/b5CXd/bphwSgtaRAQy5e7gzI7esnLOY51NZccQp+SPC3e/zMwWAI+b2TTgYuCe\nYRL5AYD1oeZCAAAH/ElEQVQNkcj/GrgO+KC7b45q8re6++7o9VXAKcBwyZzNm3cM93LDaW9vS1TM\nSYsXFHMtJC1eCGMuWwKGj9m4BmZ2iZldEz3dDRSjx7nA/cN0PRt4cNC+Pg58Fpjl7huizccBnWaW\nM7M84UToUyM6ChGRMS42mQMrgNPM7BHgAWCeu+8hPLtlw8CGZrbczI7sfzrw9WgEfhvQBqwws4fM\n7EZ3fwG4G1gLPAwsd/dflnlcIiJjSsk18wYTJPGrXpJiTlq8oJhrIWnxArS3t5Vdr/7bv+0uK1He\nfHNL49TMRUTGqs7OUooY9aVkLiISq9xTE/MViWI4SuYiIrF66x1ALCVzEZFYjX8JqJK5iEiMjo7G\nP1FEyVxEJEZnp0bmIiIpoJq5iEgKaGQuIpJ4HR0puDmFiMhY19nZXe8QYimZi4jEUplFRCQFapvM\nzWw8cA8wCegBLnP33w/Xp/EXHBARqbveMh8jNhdY5+5nA98DvhLXQSNzEZEYHR2tNf197v7NaNlw\ngCP509t1DknJXEQkRmfnrqrt28w+CcwftPkKd38yuvPaScB5cftRMhcRiVW9i4bc/Q7CeyIP9dp7\nzex44D+Ao4fbj5K5iEismk+AXgO86u53AztLCUDJXEQkVs1PTVwKLItKMDngirgOSuYiIjE6Ovav\n6e9z99eA942kj5K5iEiMzs7Yk0nqTslcRCSWrgAVEUkBJXMRkcTr6JhY7xBiKZmLiMTo7Hy93iHE\nUjIXEYmlOw2JiKSAauYiIimgZC4ikngdHQfXO4RYSuYiIjE6OzfVO4RYSuYiIrFUZhERSQElcxGR\nFEjBqYnRrYuWAAYEwFXA9cDkqMlUYK27zxnQ53Lg8uhpK3Bq1P5g4K5oP88DV7t70czmAlcSvmML\n3f2+8g5LRKRyOjreVu8QYpUyMr8QwN07zGwWsMjdZwOY2SRgNYNueeTudxEmbczsdmCpu28zs+XA\n9e7+kJl9B5htZj8DPg/MIEz8j5rZT9y9uwLHJyJSts7OV+sdQqxsXAN3Xwl8Ono6hT+9seiNwGJ3\n3zhUXzObAZzo7t+NNk0HHo5+vh84Fzgd6HT3bnffDrwMnDzSAxERqZ7eMh/VV1LN3N17zWwZcBFw\nMYCZHQKcw943Ih3oWsKE3y/j7kH08w5gIjAB2D6gTf92EZGG0NFxWL1DiFXyBKi7X2ZmC4DHzWwa\nYVK/x92HnOY1swMAc/fVAzYXB/zcRjjKfzP6efD24WTa29timjSepMWctHhBMddC0uKthJtvPj9T\n7xjixJZZzOyS6OaiALsJE3KRsERy/zBdzwYeHLTt6ajuDvB+YA3wBHCWmbWa2UTgBMLJURERKVFs\nMgdWAKeZ2SPAA8A8d99DeHbLhoENzWy5mR3Z/3Tw68CXgBujSc9m4Ifuvgm4jTCxrwKuc/eu0R6Q\niMhYlAmCIL6ViIg0tFJG5iIi0uCUzEVEUkDJXEQkBRpmbRYzuwj4K3f/WPT8HGAhUAD+AFzq7rvN\n7GZgJmHs33X3JUPs52vA/4s23eDuD5vZDcAHCc/gn+fuTzRKzAP2dy1wcv/SCGb2r4RLIBSAPe7+\n/gTEXNH3uYJ/F2cR/l0EwMPuviDa3rDv8TAxN+p7vK9+jfwe76tfxfNFtTXEyNzMbgVu4k/j+Rbw\nIXc/G3gJ+JSZvQc4xt3fRfgPtCBaUmCg6cBX3H1W9HjYzN4BvBt4JzAHuL3BYsbM3k/4xzPQscDM\n6Dgq8R+gqjFX+n2ucLzfBOa4+xnA6WZ2WrS9kd/jvWJu8Pd4r37R9kZ+j4fqV/F8UQsNkcyBx4DP\nDNo2y91fi35uArqAnwGfiLYFQI7wE3Wg6cAnzGyNmX3dzJoI/yF/7O6Bu78CNJlZe6PEbGbHEC40\ndsOAbYcCBwD/bmaPmtkFZcZb9Zip/Ptcyb+Ld7r7b8xsf8IrjHc2+ns8VMw09nu8V78EvMdD9atG\nvqi6mpZZzOyT7H35/xXufu+Ai4kA6F/vxcw+DLwH+Gp0/nmXmeWBZYRfm3YO2t9PgJXAb4DvEK7y\nOAF4fUCb/iUDNtc75ug/6u3ApYQXTPVrBr4O3AocCHSa2RPu/ocGjnlU73Mt/i6iJSnOAL4P/Ap4\nlfCrf0O+x8PE3Mjv8V79gHYa+z0eKuYvM8p8UU81TebufgdwR6ntzWw+4bIB7+u/kCj6mvRD4CF3\nv2mIbkvdfVvU9l+BjwDPMvIlA2oV818SLg98L+EI5s/M7O8I/wN8x917gT+Y2dOEF2LF/ieoY8yj\nWZqhVn8XuPtaYKqZLQT+jrBW2qjv8b5ifp0Gfo8H9zOzTTT4ezxEzKP6O663Rimz7MXMrgPOAs51\n9y3Rtv0IlwhY6u5/P0SfDPCcmR0ebToHWAd0AuebWdbCK1Sz/fusd8zuvsLdT3H3WcA8YJW7/0/C\n5RJ+EO1jf+DPgRcaPOaqv8+j/buIym799dIdvLUkRUO+x8PE3JDv8b760cDv8TAx1yRfVFrDnM0y\nUFRnuwF4CrjfzCAcBbYCRwFzLbyhBcAVwNsJJ1j+h5l9ClhhZnsIv5oucfeCma0hrKFlgasbKeah\n9ufu95vZ+Wa2lvA/8bVV+E9b6ZjXVfN9LvPv4mtRn25gI/Apd9/ZyO/xMDE33HsM/NNQ/dz92436\nHsfEXNV8UQ26nF9EJAUatswiIiKlUzIXEUkBJXMRkRRQMhcRSQElcxGRFFAyFxFJASVzEZEUUDIX\nEUmB/w9jDkLTCkn5PAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12029e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.scatter(x_large_err,y_large_err, c=z_large_err, cmap='jet',s=8,alpha=.5,edgecolors='face')\n",
    "plt.xlim(-122.5,-122.2)\n",
    "plt.ylim(37.7,37.9)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can't find a clear spatial pattern with the errors, which is good. It would still be a good idea to map these on a real map to see if there are spatial patterns the model hasn't accounted for even with the lat/long coordinates. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Ways to improve the model\n",
    "\n",
    "- More considered feature selection. \n",
    "- Add features representing rental unit characteristics.\n",
    "- Add dummies for administrative boundaries (cities, school districts). It's not clear this would improve the model much though, since we already have lat/long.\n",
    "- Add features to represent crime and school quality\n",
    "- Account for time series, as add more data. In short term, can add features for the day of week and day of month. (Does it matter when the listing was posted?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results from previous iterations\n",
    "### Linear model, using lnrent as Y\n",
    "\n",
    "Scores for full dataset w/o bathrooms feature\n",
    "- Mean squared error: 0.03\n",
    "- RMSE: 0.186277629605\n",
    "- Variance score: 0.68\n",
    "\n",
    "\n",
    "Scores w/ bathrooms feature, dropping missing values - slightly better\n",
    "\n",
    "- Mean squared error: 0.03\n",
    "- RMSE: 0.181372180936\n",
    "- Variance score: 0.69\n",
    "\n",
    "Scores w/ bath_bed and sqft^2 features added\n",
    "\n",
    "- Mean squared error: 0.03\n",
    "- RMSE: 0.175778312162\n",
    "- Variance score: 0.71\n",
    "\n",
    "### Gradient boosting model with lnrent as Y\n",
    "\n",
    "Best RMSE when using no bathrooms: 0.1308920\n",
    "\n",
    "Best RMSE with bathrooms feature: 0.132212\n",
    "\n",
    "\n",
    "\n",
    "I also tried a **random forest** model. The GB performs better. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}