SF TravelDesicionSurvey .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Supervised Learning:  The Waze Carpool - SF Pilot\n",
    "- In this project, I'm trying to build machine learning models based on Kaggle and SMFTA's data: I came up with WAZE Rider San Francisco Pilot to study features of a carpool modelling and make prdedictions that could help the business.\n",
    "Waze is going the corporate route. The pilot is starting with more than 25,000 employees of select companies that schlep workers from city.\n",
    "\n",
    "Commute choice: dataset about carpool desicions, e.g., drive alone, car share, shuttle, uber, public transportation\n",
    "Location: information about users location\n",
    "Income: total income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model, cross_validation\n",
    "\n",
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "pd.set_option('display.max_columns', 10)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(os.path.join('..', 'FinalProject-GA', 'TDS_Data1.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RESP_NUM</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>TRIPS</th>\n",
       "      <th>Q4_TOTAL</th>\n",
       "      <th>...</th>\n",
       "      <th>ZONE</th>\n",
       "      <th>INTERVIEW_DAY</th>\n",
       "      <th>INTERVIEW_DATE</th>\n",
       "      <th>AGECO</th>\n",
       "      <th>WEIGHT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10/6/2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1.043120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1.115464</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>10/6/2014</td>\n",
       "      <td>7</td>\n",
       "      <td>1.048985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>10/6/2014</td>\n",
       "      <td>10</td>\n",
       "      <td>0.930278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10/6/2014</td>\n",
       "      <td>4</td>\n",
       "      <td>0.870378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1920.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11/9/2014</td>\n",
       "      <td>2</td>\n",
       "      <td>1.115464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1921.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11/10/2014</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11/10/2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1.043120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11/10/2014</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1924.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11/10/2014</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>767 rows × 224 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RESP_NUM  Q2   Q3  TRIPS  Q4_TOTAL    ...     ZONE  INTERVIEW_DAY  \\\n",
       "0         2.0   9  NaN      2       2.0    ...      5.0              2   \n",
       "1         3.0   9  NaN     11       6.0    ...      5.0              2   \n",
       "2         4.0   1  1.0    214       NaN    ...      NaN              2   \n",
       "3         5.0   3  1.0    594       NaN    ...      NaN              2   \n",
       "4         6.0   9  NaN      6       4.0    ...      4.0              2   \n",
       "..        ...  ..  ...    ...       ...    ...      ...            ...   \n",
       "762    1920.0   9  NaN      6       4.0    ...      5.0              1   \n",
       "763    1921.0   9  NaN      9       4.0    ...      5.0              2   \n",
       "764    1922.0   9  NaN      5       5.0    ...      5.0              2   \n",
       "765    1923.0   9  NaN      7       4.0    ...      2.0              2   \n",
       "766    1924.0   9  NaN     10       3.0    ...      1.0              2   \n",
       "\n",
       "     INTERVIEW_DATE  AGECO    WEIGHT  \n",
       "0         10/6/2014      1  1.043120  \n",
       "1         10/6/2014      2  1.115464  \n",
       "2         10/6/2014      7  1.048985  \n",
       "3         10/6/2014     10  0.930278  \n",
       "4         10/6/2014      4  0.870378  \n",
       "..              ...    ...       ...  \n",
       "762       11/9/2014      2  1.115464  \n",
       "763      11/10/2014      3  0.865878  \n",
       "764      11/10/2014      1  1.043120  \n",
       "765      11/10/2014      3  0.865878  \n",
       "766      11/10/2014      3  0.865878  \n",
       "\n",
       "[767 rows x 224 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "   The travel desicion survey within the City and County of San Francisco as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma and Solano. A description of the dataset is as follows:\n",
    "    \n",
    "RESP-NUM: per capita crime rate by town\n",
    "Q2: In which Bay Area county do you live? 1=Alameda 2=Contra Costa 3=Marin 4=Napa 5=San Mateo 6=Santa Clara 7=Solano 8=Sonoma 9=San Francisco 0=Do not live in Bay Area  \n",
    "Q3: Have you gone into San Francisco within the past month (30 days)? 1=Yes 2=No \n",
    "TRIPS: Charles River binary/dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "Q4_Total: nitric oxides concentration (parts per 10 million)\n",
    "Q4aP: (SF Residents) Where did you go? 1=Work 2=School 3=Shopping 4=Out to eat/Social/Entertainment/Recreation 5=Home 6=Other 7=Don't know / Don't remember 8=Religious/Volunteer/Political\n",
    "Q18M: 1=Drove my vehicle alone2=Drove my vehicle with others3=Drove car share4=Uber, Lyft, Sidecar or similar transportation network company5=Regular taxi6=Public transportation (Muni, BART, AC Transit, VTA, Amtrak, Paratransit, etc.)7=Private bus or van, including corporate shuttle, campus shuttle or similar (including UCSF, CPMC, Art Institute Leap, and RidePal)8=Bicycle9=Walk 10=Scooter/Motorcycle11=Other (specify)12=Don't know / Don't remember \n",
    "Zone: (San Francisco Residents) San Francisco Zone (Based on Zip code)\n",
    "Q27: Annual Income (1=$35,000 or less 2=$36,000 through $75,000 3=$76,000 through $100,000 4=Over $100,000)\n",
    "Interview_Day: Date of the interview\n",
    "Interview_Date: Day of the week on which the interview was conducted (1=Sunday2=Monday3=Tuesday4=Wednesday5=Thursday6=Friday7=Saturday)\n",
    "Adeco: Age code (used for weighting) 1=18-24 (Inside SF)2=25-34 (Inside SF)3=35-44 (Inside SF)4=45-54 (Inside SF)5=55 and older/Refused (Inside SF)6=18-24 (Outside SF)7=25-34 (Outside SF)8=35-44 (Outside SF)9=45-54 (Outside SF)10=55 and older/Refused (Outside SF)\n",
    "Weight: Weighting factor applied to record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'RESP_NUM', u'Q2', u'Q3', u'TRIPS', u'Q4_TOTAL', u'Q5_TOTAL', u'Q4AP',\n",
       "       u'Q4BP', u'Q4CP', u'Q4DP',\n",
       "       ...\n",
       "       u'Q26-5', u'Q27', u'Q28', u'SAMPLE_TYPE', u'LANGUAGE', u'ZONE',\n",
       "       u'INTERVIEW_DAY', u'INTERVIEW_DATE', u'AGECO', u'WEIGHT'],\n",
       "      dtype='object', length=224)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESP_NUM          767\n",
      "Q2                767\n",
      "Q3                392\n",
      "TRIPS             767\n",
      "Q4_TOTAL          375\n",
      "                 ... \n",
      "ZONE              375\n",
      "INTERVIEW_DAY     767\n",
      "INTERVIEW_DATE    767\n",
      "AGECO             767\n",
      "WEIGHT            767\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning and Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##Remove columns\n",
    "# Selecting only Home and Work - base on carpool purpose , Commute Way - Carpool\n",
    "df.drop(['SCH', 'SHOP', 'SOC', 'OTH-P-SF', 'DK-P-SF','SHTTLE', 'BIKE', 'WALK', 'SCOOT', 'REL', 'OTH-M-SF', 'DK-M-SF', \n",
    "'SF-D1','INTERVIEW_DAY', 'INTERVIEW_DATE', 'LANGUAGE', 'Q4BP', 'Q4CP', 'Q4DP', 'Q4EP', 'Q4FP', 'Q4GP', 'Q4HP', 'Q5BP',\n",
    "'Q5CP', 'Q5DP', 'Q5EP', 'Q5FP', 'Q5GP', 'Q5HP', 'Q18AP', 'Q18BP', 'Q18CP', 'Q18DP', 'Q18EP', 'Q18FP', 'Q18GP', 'Q18HP', \n",
    "'SCH1', 'SHOP1', 'SOC1', 'OTH1-P', 'DK1-P', 'SCH2', 'SHOP2', 'SOC2', 'OTH2-P', 'DK2-P', 'REL1', 'REL2', 'SHTTLE1', 'BIKE1',\n",
    "'WALK1', 'SCOOT1', 'SHTTLE2', 'BIKE2', 'WALK2', 'SCOOT2', 'OTH1-M', 'OTH2-M', 'DK1-M', 'DK2-M', 'Q18AD', 'Q18BD', 'Q18CD', \n",
    "'Q18DD', 'Q18ED', 'Q18FD', 'Q18GD', 'Q18HD', 'OSF1-D1', 'OSF1-D3', 'OSF2-D1', 'OSF2-D3', 'Q22', 'Q26-1', 'Q26-2', 'Q26-3', \n",
    "'Q26-4', 'Q26-5', 'SAMPLE_TYPE', 'Q3', 'TRIPS', 'Q4_TOTAL', 'Q5_TOTAL', 'Q4AM', 'Q4BM', 'Q4CM', 'Q4DM', 'Q4EM', 'Q4FM', \n",
    "'Q4GM','Q4HM', 'Q5AM', 'Q5BM', 'Q5CM', 'Q5DM', 'Q5EM', 'Q5FM', 'Q5GM', 'Q5HM', 'Q4AD', 'Q4BD', 'Q4CD', 'Q4DD', 'Q4ED',\n",
    "'Q4FD', 'Q4GD','Q4HD', 'Q5AD', 'Q5BD', 'Q5CD', 'Q5DD', 'Q5ED', 'Q5FD', 'Q5GD', 'Q5HD', 'Q8', 'Q11AP', 'Q11BP', 'Q11CP', \n",
    "'Q11DP','Q11EP', 'Q11FP', 'Q11GP', 'Q11HP', 'Q11AM', 'Q11BM', 'Q11CM', 'Q11DM','Q11EM', 'Q11FM', 'Q11GM',\n",
    "'Q11HM', 'Q14M', 'Q16M-ADJ', 'Q18AM', 'Q18BM', 'Q18CM', 'Q18DM', \n",
    "'Q18EM', 'Q18FM', 'Q18GM', 'Q18HM', 'SF-D3'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Q27': 'Income'})\n",
    "df = df.rename(columns={'RESP_NUM': 'Resp_num'})\n",
    "df = df.rename(columns={'Q2': 'Location'})\n",
    "df = df.rename(columns={'Q28': 'Gender'})\n",
    "df = df.rename(columns={'WORK': 'Work'})\n",
    "df = df.rename(columns={'HOME': 'Home'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Resp_num', u'Location', u'Q4AP', u'Q5AP', u'Work', u'Home',\n",
       "       u'SFINTMODE', u'DA', u'DO', u'CRSHRE', u'TNC', u'TAXI', u'TRANS',\n",
       "       u'SF-D2', u'Q6', u'Q10', u'Q11_TOTAL', u'Q12', u'Q19', u'Q13', u'Q15',\n",
       "       u'Q16M', u'Q17', u'Q18_TOTAL', u'Q9M', u'Q7P', u'9P-ADJ', u'Q14P',\n",
       "       u'Q16P-ADJ', u'WORK1', u'WORK2', u'HOME1', u'HOME2', u'Q7M', u'Q9M-ADJ',\n",
       "       u'DA1', u'DA2', u'DO1', u'DO2', u'CRSHRE1', u'CRSHRE2', u'TNC1',\n",
       "       u'TNC2', u'TAXI1', u'TAXI2', u'TRANS1', u'TRANS2', u'Q7D', u'Q9D',\n",
       "       u'Q11AD', u'Q11BD', u'Q11CD', u'Q11DD', u'Q11ED', u'Q11FD', u'Q11GD',\n",
       "       u'Q11HD', u'Q14D', u'Q16D', u'OSF1-D2', u'OSF2-D2', u'OSF-D1',\n",
       "       u'OSF-D2', u'OSF-D3', u'Q20A', u'Q21A', u'Q21B', u'Q21C', u'Q21D',\n",
       "       u'Q23', u'Q24-SF', u'Q24-NSF', u'Q24A', u'Q25', u'Income', u'Gender',\n",
       "       u'ZONE', u'AGECO', u'WEIGHT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Dropping additional columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.drop(['Q4AP', 'Q5AP', 'SFINTMODE', 'Q6', 'Q10', 'Q11_TOTAL', 'Q12', 'Q19', 'Q15', 'Q16M',\n",
    "'Q18_TOTAL', 'Q9M', 'Q7P', '9P-ADJ', 'Q14P', 'Q16P-ADJ', 'Q7M', 'Q9M-ADJ',\n",
    "'Q7D', 'Q9D', 'Q13', 'Q17', 'Q11AD', 'Q11BD', 'Q11CD', 'Q11DD', 'Q11ED', 'Q11FD',\n",
    "       'Q11GD', 'Q11HD', 'Q14D', 'Q16D', 'Q20A', 'Q21B', 'Q21D','Q23', 'TAXI', 'TAXI1', 'TAXI2'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Resp_num', u'Location', u'Work', u'Home', u'DA', u'DO', u'CRSHRE',\n",
       "       u'TNC', u'TRANS', u'SF-D2', u'WORK1', u'WORK2', u'HOME1', u'HOME2',\n",
       "       u'DA1', u'DA2', u'DO1', u'DO2', u'CRSHRE1', u'CRSHRE2', u'TNC1',\n",
       "       u'TNC2', u'TRANS1', u'TRANS2', u'OSF1-D2', u'OSF2-D2', u'OSF-D1',\n",
       "       u'OSF-D2', u'OSF-D3', u'Q21A', u'Q21C', u'Q24-SF', u'Q24-NSF', u'Q24A',\n",
       "       u'Q25', u'Income', u'Gender', u'ZONE', u'AGECO', u'WEIGHT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select San Francisco Location from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Eliminating non-san francisco variables\n",
    "df.drop(['WORK1', 'WORK2', 'HOME1', 'HOME2',\n",
    "       'DA1', 'DA2', 'DO1', 'DO2', 'CRSHRE1', 'CRSHRE2', 'TNC1',\n",
    "       'TNC2', 'TRANS1', 'TRANS2'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Eliminating trips less than 1 mile\n",
    "df.drop(['OSF1-D2', 'OSF2-D2', 'OSF-D1' ], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Resp_num', u'Location', u'Work', u'Home', u'DA', u'DO', u'CRSHRE',\n",
       "       u'TNC', u'TRANS', u'SF-D2', u'OSF-D2', u'OSF-D3', u'Q21A', u'Q21C',\n",
       "       u'Q24-SF', u'Q24-NSF', u'Q24A', u'Q25', u'Income', u'Gender', u'ZONE',\n",
       "       u'AGECO', u'WEIGHT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "     Resp_num  Location  Work  Home   DA    ...     Income  Gender  ZONE  \\\n",
       "0         2.0         9   0.0   1.0  0.0    ...          1       1   5.0   \n",
       "1         3.0         9   4.0   2.0  2.0    ...          4       2   5.0   \n",
       "2         4.0         1   NaN   NaN  NaN    ...          5       2   NaN   \n",
       "3         5.0         3   NaN   NaN  NaN    ...          4       1   NaN   \n",
       "4         6.0         9   0.0   2.0  0.0    ...          4       1   4.0   \n",
       "..        ...       ...   ...   ...  ...    ...        ...     ...   ...   \n",
       "762    1920.0         9   1.0   2.0  2.0    ...          2       1   5.0   \n",
       "763    1921.0         9   2.0   3.0  0.0    ...          3       2   5.0   \n",
       "764    1922.0         9   0.0   2.0  0.0    ...          1       2   5.0   \n",
       "765    1923.0         9   0.0   2.0  0.0    ...          2       2   2.0   \n",
       "766    1924.0         9   0.0   2.0  0.0    ...          5       2   1.0   \n",
       "\n",
       "     AGECO    WEIGHT  \n",
       "0        1  1.043120  \n",
       "1        2  1.115464  \n",
       "2        7  1.048985  \n",
       "3       10  0.930278  \n",
       "4        4  0.870378  \n",
       "..     ...       ...  \n",
       "762      2  1.115464  \n",
       "763      3  0.865878  \n",
       "764      1  1.043120  \n",
       "765      3  0.865878  \n",
       "766      3  0.865878  \n",
       "\n",
       "[767 rows x 23 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 1, 3, 8, 5, 2, 6, 7, 4])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Location.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select 5-6-9 Location numbers meaning San Francisco and South Bay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    Resp_num  Location  Work  Home  DA    ...     Income  Gender  ZONE  AGECO  \\\n",
       "12      16.0         5   NaN   NaN NaN    ...          3       2   NaN      7   \n",
       "26      32.0         5   NaN   NaN NaN    ...          3       1   NaN     10   \n",
       "48      59.0         5   NaN   NaN NaN    ...          4       2   NaN      7   \n",
       "49      61.0         5   NaN   NaN NaN    ...          4       1   NaN     10   \n",
       "52      64.0         5   NaN   NaN NaN    ...          5       2   NaN      7   \n",
       "\n",
       "      WEIGHT  \n",
       "12  1.048985  \n",
       "26  0.930278  \n",
       "48  1.048985  \n",
       "49  0.930278  \n",
       "52  1.048985  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df = df[(df.Location == 5)]\n",
    "\n",
    "subset_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "    Resp_num  Location  Work  Home  DA    ...     Income  Gender  ZONE  AGECO  \\\n",
       "43      53.0         6   NaN   NaN NaN    ...          5       1   NaN      8   \n",
       "63      76.0         6   NaN   NaN NaN    ...          5       1   NaN      7   \n",
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       "95     112.0         6   NaN   NaN NaN    ...          3       2   NaN     10   \n",
       "\n",
       "      WEIGHT  \n",
       "43  0.938616  \n",
       "63  1.048985  \n",
       "86  0.930278  \n",
       "88  0.930278  \n",
       "95  0.930278  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df = df[(df.Location == 6)]\n",
    "\n",
    "subset_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.115464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.870378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Resp_num  Location  Work  Home   DA    ...     Income  Gender  ZONE  AGECO  \\\n",
       "0       2.0         9   0.0   1.0  0.0    ...          1       1   5.0      1   \n",
       "1       3.0         9   4.0   2.0  2.0    ...          4       2   5.0      2   \n",
       "4       6.0         9   0.0   2.0  0.0    ...          4       1   4.0      4   \n",
       "7      10.0         9   3.0   2.0  5.0    ...          3       1   3.0      3   \n",
       "9      12.0         9   0.0   1.0  1.0    ...          1       1   5.0      3   \n",
       "\n",
       "     WEIGHT  \n",
       "0  1.043120  \n",
       "1  1.115464  \n",
       "4  0.870378  \n",
       "7  0.865878  \n",
       "9  0.865878  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df = df[(df.Location == 9)]\n",
    "\n",
    "subset_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Observing the data - work base trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   4.,  nan,   3.,   2.,   1.,   8.,   5.])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Work.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    192\n",
       "2.0     76\n",
       "1.0     68\n",
       "3.0     23\n",
       "4.0     14\n",
       "8.0      1\n",
       "5.0      1\n",
       "Name: Work, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Work.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>767 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Work\n",
       "0     0.0\n",
       "1     4.0\n",
       "2     NaN\n",
       "3     NaN\n",
       "4     0.0\n",
       "..    ...\n",
       "762   1.0\n",
       "763   2.0\n",
       "764   0.0\n",
       "765   0.0\n",
       "766   0.0\n",
       "\n",
       "[767 rows x 1 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ [df.columns.get_loc('Work')] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Getting rid of rows with Location that is not equal to 9 - San Francisco\n",
    "df = df.drop(df[df.Location < 9 ].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resp_num</th>\n",
       "      <th>Location</th>\n",
       "      <th>Work</th>\n",
       "      <th>Home</th>\n",
       "      <th>DA</th>\n",
       "      <th>...</th>\n",
       "      <th>Income</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ZONE</th>\n",
       "      <th>AGECO</th>\n",
       "      <th>WEIGHT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.043120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.115464</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.870378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1920.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.115464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1921.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.043120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1924.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Resp_num  Location  Work  Home   DA    ...     Income  Gender  ZONE  \\\n",
       "0         2.0         9   0.0   1.0  0.0    ...          1       1   5.0   \n",
       "1         3.0         9   4.0   2.0  2.0    ...          4       2   5.0   \n",
       "4         6.0         9   0.0   2.0  0.0    ...          4       1   4.0   \n",
       "7        10.0         9   3.0   2.0  5.0    ...          3       1   3.0   \n",
       "9        12.0         9   0.0   1.0  1.0    ...          1       1   5.0   \n",
       "..        ...       ...   ...   ...  ...    ...        ...     ...   ...   \n",
       "762    1920.0         9   1.0   2.0  2.0    ...          2       1   5.0   \n",
       "763    1921.0         9   2.0   3.0  0.0    ...          3       2   5.0   \n",
       "764    1922.0         9   0.0   2.0  0.0    ...          1       2   5.0   \n",
       "765    1923.0         9   0.0   2.0  0.0    ...          2       2   2.0   \n",
       "766    1924.0         9   0.0   2.0  0.0    ...          5       2   1.0   \n",
       "\n",
       "     AGECO    WEIGHT  \n",
       "0        1  1.043120  \n",
       "1        2  1.115464  \n",
       "4        4  0.870378  \n",
       "7        3  0.865878  \n",
       "9        3  0.865878  \n",
       "..     ...       ...  \n",
       "762      2  1.115464  \n",
       "763      3  0.865878  \n",
       "764      1  1.043120  \n",
       "765      3  0.865878  \n",
       "766      3  0.865878  \n",
       "\n",
       "[375 rows x 23 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Transforming data to integer\n",
    "df[['Work', 'Home']] = df[['Work', 'Home']].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Selecting Home and Work - base on carpool purpose   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROJECT 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resp_num</th>\n",
       "      <th>Location</th>\n",
       "      <th>Work</th>\n",
       "      <th>Home</th>\n",
       "      <th>DA</th>\n",
       "      <th>...</th>\n",
       "      <th>Income</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ZONE</th>\n",
       "      <th>AGECO</th>\n",
       "      <th>WEIGHT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.0</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>889.388000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.954667</td>\n",
       "      <td>2.045333</td>\n",
       "      <td>1.514667</td>\n",
       "      <td>...</td>\n",
       "      <td>2.757333</td>\n",
       "      <td>1.522667</td>\n",
       "      <td>2.997333</td>\n",
       "      <td>3.298667</td>\n",
       "      <td>0.999963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>593.352081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.212931</td>\n",
       "      <td>1.275257</td>\n",
       "      <td>2.241842</td>\n",
       "      <td>...</td>\n",
       "      <td>1.461555</td>\n",
       "      <td>0.500153</td>\n",
       "      <td>1.637624</td>\n",
       "      <td>1.352903</td>\n",
       "      <td>0.110880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.865878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>406.500000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.870378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>807.500000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.043120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1425.500000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.088087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1924.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.115464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Resp_num  Location        Work        Home          DA     ...      \\\n",
       "count   375.000000     375.0  375.000000  375.000000  375.000000     ...       \n",
       "mean    889.388000       9.0    0.954667    2.045333    1.514667     ...       \n",
       "std     593.352081       0.0    1.212931    1.275257    2.241842     ...       \n",
       "min       2.000000       9.0    0.000000    0.000000    0.000000     ...       \n",
       "25%     406.500000       9.0    0.000000    1.000000    0.000000     ...       \n",
       "50%     807.500000       9.0    0.000000    2.000000    0.000000     ...       \n",
       "75%    1425.500000       9.0    2.000000    3.000000    2.500000     ...       \n",
       "max    1924.000000       9.0    8.000000    8.000000   13.000000     ...       \n",
       "\n",
       "           Income      Gender        ZONE       AGECO      WEIGHT  \n",
       "count  375.000000  375.000000  375.000000  375.000000  375.000000  \n",
       "mean     2.757333    1.522667    2.997333    3.298667    0.999963  \n",
       "std      1.461555    0.500153    1.637624    1.352903    0.110880  \n",
       "min      1.000000    1.000000    1.000000    1.000000    0.865878  \n",
       "25%      1.000000    1.000000    2.000000    2.000000    0.870378  \n",
       "50%      3.000000    2.000000    3.000000    3.000000    1.043120  \n",
       "75%      4.000000    2.000000    4.000000    5.000000    1.088087  \n",
       "max      5.000000    2.000000    9.000000    5.000000    1.115464  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resp_num</th>\n",
       "      <th>Location</th>\n",
       "      <th>Work</th>\n",
       "      <th>Home</th>\n",
       "      <th>DA</th>\n",
       "      <th>...</th>\n",
       "      <th>Income</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ZONE</th>\n",
       "      <th>AGECO</th>\n",
       "      <th>WEIGHT</th>\n",
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       "  </thead>\n",
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       "      <td>-0.032020</td>\n",
       "      <td>-0.034172</td>\n",
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       "      <td>-0.023714</td>\n",
       "      <td>0.029722</td>\n",
       "      <td>0.121043</td>\n",
       "      <td>0.021179</td>\n",
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       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.060105</td>\n",
       "      <td>0.217064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182311</td>\n",
       "      <td>-0.084247</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>-0.071567</td>\n",
       "      <td>-0.101878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home</th>\n",
       "      <td>-0.032020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.060105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116378</td>\n",
       "      <td>-0.121089</td>\n",
       "      <td>-0.080602</td>\n",
       "      <td>-0.059011</td>\n",
       "      <td>-0.084258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DA</th>\n",
       "      <td>-0.034172</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.185105</td>\n",
       "      <td>-0.102240</td>\n",
       "      <td>0.074661</td>\n",
       "      <td>0.031170</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>-0.060553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.182311</td>\n",
       "      <td>0.116378</td>\n",
       "      <td>0.185105</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085727</td>\n",
       "      <td>-0.062830</td>\n",
       "      <td>0.092192</td>\n",
       "      <td>-0.161931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.023714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.084247</td>\n",
       "      <td>-0.121089</td>\n",
       "      <td>-0.102240</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050673</td>\n",
       "      <td>-0.021886</td>\n",
       "      <td>-0.018800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZONE</th>\n",
       "      <td>0.029722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>-0.080602</td>\n",
       "      <td>0.074661</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062830</td>\n",
       "      <td>0.050673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000846</td>\n",
       "      <td>0.015433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGECO</th>\n",
       "      <td>0.121043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.071567</td>\n",
       "      <td>-0.059011</td>\n",
       "      <td>0.031170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092192</td>\n",
       "      <td>-0.021886</td>\n",
       "      <td>-0.000846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEIGHT</th>\n",
       "      <td>0.021179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.101878</td>\n",
       "      <td>-0.084258</td>\n",
       "      <td>-0.078441</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.161931</td>\n",
       "      <td>-0.018800</td>\n",
       "      <td>0.015433</td>\n",
       "      <td>-0.067236</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Resp_num  Location      Work      Home        DA    ...     \\\n",
       "Resp_num  1.000000       NaN -0.065795 -0.032020 -0.034172    ...      \n",
       "Location       NaN       NaN       NaN       NaN       NaN    ...      \n",
       "Work     -0.065795       NaN  1.000000  0.060105  0.217064    ...      \n",
       "Home     -0.032020       NaN  0.060105  1.000000  0.300448    ...      \n",
       "DA       -0.034172       NaN  0.217064  0.300448  1.000000    ...      \n",
       "...            ...       ...       ...       ...       ...    ...      \n",
       "Income   -0.060553       NaN  0.182311  0.116378  0.185105    ...      \n",
       "Gender   -0.023714       NaN -0.084247 -0.121089 -0.102240    ...      \n",
       "ZONE      0.029722       NaN -0.000061 -0.080602  0.074661    ...      \n",
       "AGECO     0.121043       NaN -0.071567 -0.059011  0.031170    ...      \n",
       "WEIGHT    0.021179       NaN -0.101878 -0.084258 -0.078441    ...      \n",
       "\n",
       "            Income    Gender      ZONE     AGECO    WEIGHT  \n",
       "Resp_num -0.060553 -0.023714  0.029722  0.121043  0.021179  \n",
       "Location       NaN       NaN       NaN       NaN       NaN  \n",
       "Work      0.182311 -0.084247 -0.000061 -0.071567 -0.101878  \n",
       "Home      0.116378 -0.121089 -0.080602 -0.059011 -0.084258  \n",
       "DA        0.185105 -0.102240  0.074661  0.031170 -0.078441  \n",
       "...            ...       ...       ...       ...       ...  \n",
       "Income    1.000000 -0.085727 -0.062830  0.092192 -0.161931  \n",
       "Gender   -0.085727  1.000000  0.050673 -0.021886 -0.018800  \n",
       "ZONE     -0.062830  0.050673  1.000000 -0.000846  0.015433  \n",
       "AGECO     0.092192 -0.021886 -0.000846  1.000000 -0.067236  \n",
       "WEIGHT   -0.161931 -0.018800  0.015433 -0.067236  1.000000  \n",
       "\n",
       "[22 rows x 22 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a11b190>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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8Ty+++OKU5f/1X/8149ds3bp1yv/fe++9lyxnScpms34izblE3N/sJxGPmvkZyBy8sOWd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11336d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[ ['Work', 'Home'] ].plot(kind = 'scatter', x = 'Work', y = 'Home', s = 100, figsize = (8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a1043d0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GnPC4HmAyng8IEmEesEzKUpudnHCddjupeSnzPuWaTkS1JJOacJ2OTErpBCcvhB/PBwSJ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\n4Kjf6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a12b110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[ ['DA', 'DO'] ].plot(kind = 'scatter', x = 'DA', y = 'DO', s = 100, figsize = (8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a170550>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r1q2qr69XZWWl5s2bpw8++CDTI5mzfv16xeNx/epXv9IVV1yhP/7xj5keqUdw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\ndgAADPk/HPK2pP4zMSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a184bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[ ['Work', 'CRSHRE'] ].plot(kind = 'scatter', x = 'Work', y = 'CRSHRE', s = 100, figsize = (8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a1ebf10>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QqVSq1nHqViz8QYEzZwCzEafKAQAwhOIGAMAQihsAAEMobgAADKG4AQAwhOIGAMAQihsA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9d9e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[ ['Work', 'DA'] ].plot(kind = 'scatter', x = 'Work', y = 'DA', s = 100, figsize = (8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fc23110>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2SuwcaMm0sz+DHobi4mJVV1ervLxcDodDS5YsUXp6ujZu3KhwOKwxY8aoqKhosGcBAP5r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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9d9790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Work.plot(kind = 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C9ff3KzfXZh0uqCvp/v5+xWKx8Y+j0ajS6bTDRO7k5eUpPz9fAwMDOnv2rCoqKlxHcubu\n3bsqLCzUhg0bXEdxrqenR21tbTp27JgOHDigc+fOuY7kTH5+vl68eKGjR4/q/Pnz+uSTT1xHeieb\nLx0zFIvFNDg4OP5xOp1WTs6Ceh36U16+fKmamhpt375dW7ZscR3HmTt37ignJ0dNTU1KpVKqra1V\nZWWlli9f7jpa1sXjca1atUrRaFSJREKe5+nNmzcqLCx0HS3r6uvrtXHjRlVUVKi7u1tVVVWqqakx\nd0VtK817+vjjj/Xw4UOVlZWptbVVH330ketIzrx69UqnT5/W/v37tX79etdxnKqqqprw64MHDy7K\ngpaktWvX6tatW9q5c6e6u7s1PDyseDzuOpYTBQUF48eAsVhMvu+bfOe9oH7A0thXdzx//lySdOTI\nEZOfrc2Gy5cv6/79+xP+/SdOnJDneQ5TuTdW0ov1eSFJ33//vR4/fixJqqioWLTHQIODg6qrq9Or\nV680OjqqHTt2mHzHuaBKGgAWmsV7YAsA8wAlDQCGUdIAYBglDQCGUdIAYBglDQCGUdIAYBglDQCG\n/Rfn5wIovue7FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fdb5250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.06010457],\n",
       "       [ 0.06010457,  1.        ]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(df.Home, df.Work)\n",
    "plt.show()\n",
    "np.corrcoef(df.Home, df.Work)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11dd1fbd0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SnblAZTVQMRpKZbWp12YAE0kmhIjsMhbaxJzSTggBnPAAx4Kz2tAywqnOxE8uKIwN28rR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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cfc1cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = 'DA', y = 'Work', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Dropping the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120068e10>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAECCAYAAAA8SCbXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmAycAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120081050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Work.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping outliers\n",
      "- n (before) = 375\n",
      "- Q1         = 0.0 ($M)\n",
      "- Q2/Median  = 0.0 ($M)\n",
      "- Q3         = 2.0 ($M)\n",
      "- n (after)  = 374\n"
     ]
    }
   ],
   "source": [
    "def drop_outliers(df):\n",
    "    print 'Dropping outliers'\n",
    "    print '- n (before) =', len(df)\n",
    "\n",
    "    Q1 = df.Work.quantile(.25)\n",
    "    Q2 = df.Work.quantile(.5)\n",
    "    Q3 = df.Work.quantile(.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    print '- Q1         =', Q1, '($M)'\n",
    "    print '- Q2/Median  =', Q2, '($M)'\n",
    "    print '- Q3         =', Q3, '($M)'\n",
    "\n",
    "    df.drop(df[df.Work > Q3 + 1.5 * IQR].index, inplace = True)\n",
    "\n",
    "    print '- n (after)  =', len(df)\n",
    "    \n",
    "drop_outliers(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping outliers\n",
      "- n (before) = 374\n",
      "- Q1         = 1.0 ($M)\n",
      "- Q2/Median  = 2.0 ($M)\n",
      "- Q3         = 3.0 ($M)\n",
      "- n (after)  = 372\n"
     ]
    }
   ],
   "source": [
    "def drop_outliers(df):\n",
    "    print 'Dropping outliers'\n",
    "    print '- n (before) =', len(df)\n",
    "\n",
    "    Q1 = df.Home.quantile(.25)\n",
    "    Q2 = df.Home.quantile(.5)\n",
    "    Q3 = df.Home.quantile(.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    print '- Q1         =', Q1, '($M)'\n",
    "    print '- Q2/Median  =', Q2, '($M)'\n",
    "    print '- Q3         =', Q3, '($M)'\n",
    "\n",
    "    df.drop(df[df.Home > Q3 + 1.5 * IQR].index, inplace = True)\n",
    "\n",
    "    print '- n (after)  =', len(df)\n",
    "    \n",
    "drop_outliers(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping outliers\n",
      "- n (before) = 372\n",
      "- Q1         = 0.0 ($M)\n",
      "- Q2/Median  = 0.0 ($M)\n",
      "- Q3         = 2.0 ($M)\n",
      "- n (after)  = 344\n"
     ]
    }
   ],
   "source": [
    "def drop_outliers(df):\n",
    "    print 'Dropping outliers'\n",
    "    print '- n (before) =', len(df)\n",
    "\n",
    "    Q1 = df.DA.quantile(.25)\n",
    "    Q2 = df.DA.quantile(.5)\n",
    "    Q3 = df.DA.quantile(.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    print '- Q1         =', Q1, '($M)'\n",
    "    print '- Q2/Median  =', Q2, '($M)'\n",
    "    print '- Q3         =', Q3, '($M)'\n",
    "\n",
    "    df.drop(df[df.DA > Q3 + 1.5 * IQR].index, inplace = True)\n",
    "\n",
    "    print '- n (after)  =', len(df)\n",
    "    \n",
    "drop_outliers(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping outliers\n",
      "- n (before) = 344\n",
      "- Q1         = 0.0 ($M)\n",
      "- Q2/Median  = 0.0 ($M)\n",
      "- Q3         = 0.0 ($M)\n",
      "- n (after)  = 341\n"
     ]
    }
   ],
   "source": [
    "def drop_outliers(df):\n",
    "    print 'Dropping outliers'\n",
    "    print '- n (before) =', len(df)\n",
    "\n",
    "    Q1 = df.CRSHRE.quantile(.25)\n",
    "    Q2 = df.CRSHRE.quantile(.5)\n",
    "    Q3 = df.CRSHRE.quantile(.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    print '- Q1         =', Q1, '($M)'\n",
    "    print '- Q2/Median  =', Q2, '($M)'\n",
    "    print '- Q3         =', Q3, '($M)'\n",
    "\n",
    "    df.drop(df[df.CRSHRE > Q3 + 1.5 * IQR].index, inplace = True)\n",
    "\n",
    "    print '- n (after)  =', len(df)\n",
    "    \n",
    "drop_outliers(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12025af10>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12025a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = 'DA', y = 'Work', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidying up the CommuteChoice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df[ ['Work', 'Home' ] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ys = pd.get_dummies(df.Work, prefix = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5\n",
       "0    1.0  0.0  0.0  0.0  0.0  0.0\n",
       "1    0.0  0.0  0.0  0.0  1.0  0.0\n",
       "4    1.0  0.0  0.0  0.0  0.0  0.0\n",
       "7    0.0  0.0  0.0  1.0  0.0  0.0\n",
       "9    1.0  0.0  0.0  0.0  0.0  0.0\n",
       "..   ...  ...  ...  ...  ...  ...\n",
       "762  0.0  1.0  0.0  0.0  0.0  0.0\n",
       "763  0.0  0.0  1.0  0.0  0.0  0.0\n",
       "764  1.0  0.0  0.0  0.0  0.0  0.0\n",
       "765  1.0  0.0  0.0  0.0  0.0  0.0\n",
       "766  1.0  0.0  0.0  0.0  0.0  0.0\n",
       "\n",
       "[341 rows x 6 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fbf76d0>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zn/YBLEnjXlnPdeY15AU8WECGLM8/6kl7LpK/vTOvsVLAgwWkMOFv/Rciuv6L\nZWl3x8CM+Z/tGNDE9N+GpQIv8dHRUdXW1k5+nUgkVC7zVymdOQd+9iHE6XTkCspFtMXesDx//7C/\n/P3Ddud/I6L5B0ZKUw6hT2df/7AGRiL6LA6XJPDjMrW1tRofH5/8ulwuKx6/+HMJv4cWTHYg7++o\nxEixJNddPMvTBM/2/IdO9PlabmyiEsnHw8GBX/taLqrr/1jPcV/LeXFHrts8y9NUhyj+nb/fAi/x\nZcuW6eDBg/roRz+q7u5uLV7s78Fow7mRupS/1VGXSkTy38P2/DVOzN9yyVgk89clE/6Wi+j6T5T9\n7WE7ZS+S+c/FOfEqPSe+cuVKJZNJbdmyRbt379bnPve5oEeoWs1pR1k3PeMyrW5abkQvbFloef6W\njL/8LRm78y+MaP7GuoRWtGRmXGZlS0aNdf6e7MAOgT8aYrGYNm3aFPTdGqEhJa3LNunImxe+OnVt\ntkkR7TDVW56/1pHWXyT/umyTaiK6DU8n/a3/dETXfyou3d7aqMP5kQvmv621UUnLXi+OmfFmL1Xo\n6KCn5zrz6jjrIp9WN621lrxO1vb83b99nfS5+ddZ9Dpxm9d/35CnZzsGtO+si9xWtmR0m2WvE+dw\nur/D6ZR4lRrypNyQp5FiSXWphNx6J7J7oNMZ9qQ3LM4/Vpb6hzyNTVRUkzzzjm1R3QOfzkjpTP53\n1v/CjBPZPfDpTFSkgUJJXtyR89t3bLNtD5wSp8QjgT9k8pOf/DayObtUxRe2AQCA9wclDgCAoShx\nAAAMRYkDAGAoShwAAENR4gAAGIoSBwDAUJQ4AACGosQBADAUJQ4AgKEocQAADEWJAwBgKEocAABD\nUeIAABiKEgcAwFCUOAAAhqLEAQAwFCUOAIChKHEAAAxFiQMAYChKHAAAQ1HiAAAYihIHAMBQlDgA\nAIaixAEAMBQlDgCAoShxAAAMRYkDAGAoShwAAENR4gAAGIoSBwDAUJQ4AACGosQBADAUJQ4AgKEo\ncQAADEWJAwBgKEocAABDUeIAABiKEgcAwFCUOAAAhqLEAQAwFCUOAIChKHEAAAxFiQMAYChKHAAA\nQ4VW4vv27dOTTz4Z1t0DAGA8J4w7feaZZ3To0CFdddVVYdw9AACREMqe+LJly7Rp06Yw7hoAgMiY\n1T3xn/zkJ/rhD3+oWCymSqWiWCymu+66SzfeeKO6urpm864BAIi8WKVSqYRxx11dXfrxj3+se++9\nN4y7BwDJfd4GAAAGwklEQVTAeFydDgCAoShxAAAMFdrhdAAA8N6wJw4AgKEocQAADEWJAwBgqFDe\nse1S7Nu3T//1X/+le+65J+xRAlGpVPTtb39bfX19SiaT+vznP6/GxsawxwpcT0+P2tvb1dbWFvYo\ngSmVSvrmN7+pEydOyPM83XLLLfrIRz4S9liBKZfLevrpp5XL5RSPx7Vp0ya1tLSEPVbgTp8+rQcf\nfFBbtmyR67phjxOoBx54QLW1tZKkBQsW6M477wx5omC9+OKLOnDggEqlklavXq1PfvKTF1zWiBK3\n8W1a9+/fr4mJCW3fvl09PT363ve+p82bN4c9VqBeeuklvfbaa5o7d27YowTqZz/7mTKZjL7whS+o\nUCho8+bNVpX4wYMHFYvFtG3bNnV1dam9vd26v/1SqaRvfetbmjNnTtijBG5iYkKSrHrifrauri51\nd3dr+/btGh8f1w9+8IMZlzficLqNb9N65MgRZbNZSdLVV1+t3t7ekCcKXlNTk770pS+FPUbgbrzx\nRt16662SzhyRSSQSIU8UrBUrVugv/uIvJElvvvmm0ul0yBMFb/fu3Vq9erXmz58f9iiB6+vr09tv\nv60dO3Zo27Zt6unpCXukQHV2dmrRokXauXOndu7cqeXLl8+4fFXtifM2rb8zOjo6eThJkhKJhMrl\nsuJxI553vS9WrlypEydOhD1G4N7Z+xobG9Pjjz+utWvXhjxR8OLxuJ566int379fX/ziF8MeJ1B7\n9+5VfX29rr32Wr3wwgthjxO4VCqlm2++WatWrdLx48f1t3/7t3riiSes2fYNDw/rrbfe0oMPPqiB\ngQHt3LlT3/jGNy64fFWV+KpVq7Rq1aqwx6gKtbW1Gh8fn/zatgK33VtvvaWvfe1rWrNmjT72sY+F\nPU4o7r77bp0+fVoPPfSQvv71ryuVSoU9UiB++tOfKh6P6/Dhwzp27Jieeuopbd68WfPmzQt7tEC4\nrqumpiZJUnNzszKZjE6dOqXLLrss5MmCkclktHDhQiUSCbmuq2QyqaGhIdXX10+7PK1QpZYtW6af\n//znkqTu7m4tXrw45InCY9v7EZ06dUo7duzQ+vXr9YlPfCLscQL32muv6cUXX5QkJZNJxeNxxWKx\nkKcKziOPPKK2tja1tbXpqquu0t13321NgUtnjsh+//vflySdPHlSY2NjamhoCHmq4FxzzTU6dOiQ\npDP5i8WiMpnMBZevqj1x/M7KlSv1P//zP9qyZYskWXd15tls2oBLZ65MHR0d1fPPP6/nn39ekvTQ\nQw8pmUyGPFkwbrjhBu3atUttbW0ql8vasGGDNdlx5ojsrl27tHXrVsViMd15551WHYVcvny5Xn/9\ndX35y1+WJG3cuHHGbSBvuwoAgKHseXoDAEDEUOIAABiKEgcAwFCUOAAAhqLEAQAwFCUOAIChKHHA\nAl/5ylf08ssvT359/Phx3XrrrXruuecmbxsaGtK6des0Njbm63d2dXXpvvvue99nBeAfJQ5YIJvN\nTvn8gYMHD+r666/XgQMHJm/7xS9+oWuuuUY1NTW+f69tb8QDVBvesQ2wQGtrq/71X/918uuDBw9q\n7dq1euKJJ/Tmm29qwYIFOnz4sJYvX67+/n595zvfUaFQUCwW06c//Wn90R/9kbq6uvTd735Xc+fO\n1dtvv63169dP/r4jR47o7//+73Xvvfdq6dKlYUQErESJAxZoampSJpNRX1+frrjiCuVyOS1dulTZ\nbFYHDhzQTTfdpF/84he66aab9Nhjj+nP/uzPtGLFCg0ODuqhhx5Sc3OzJKm/v1//8A//oMsvv3xy\nz/6Xv/ylnn76aT344INatGhRmDEB63A4HbBENpvVL3/5S3V0dOjaa6+VJF1//fU6dOjQ5Ee+xmIx\neZ6nFStWSJLmz5+vG264QZ2dnZKkyy+/XJdffvnk7/zNb36jxx57TCtXrqTAgRBQ4oAl3jkv/vOf\n/1zXX3+9JOnDH/6wjh07NnkovVKpnPepceVyWaVSSZI0d+7cKd9LJBL6m7/5G+3du1e/+tWvggkC\nYBIlDljiQx/6kPr6+vT666/ruuuukySlUil94AMf0I9+9CMtX7588vOL9+3bJ+nMRyH+93//9+Se\n+7kaGhq0dOlS3X777XryySdVLBYDywOAEgeskUql1NzcLNd1p1yBvnz5cuXzeX3oQx9SIpHQ/fff\nr5dffln333+/duzYoc9+9rP64Ac/OOPv/uM//mMtXLhw8nOgAQSDjyIFAMBQ7IkDAGAoShwAAENR\n4gAAGIoSBwDAUJQ4AACGosQBADAUJQ4AgKEocQAADPX/hkIqsxhME6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dd1f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[ ['Work', 'Home'] ].plot(kind = 'scatter', x = 'Work', y = 'Home', s = 100, figsize = (8, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROJECT 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature matrix and label vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DA</th>\n",
       "      <th>DO</th>\n",
       "      <th>CRSHRE</th>\n",
       "      <th>TRANS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      DA   DO  CRSHRE  TRANS\n",
       "0    0.0  0.0     0.0    2.0\n",
       "1    2.0  1.0     0.0    4.0\n",
       "4    0.0  2.0     0.0    0.0\n",
       "7    5.0  4.0     0.0    0.0\n",
       "9    1.0  1.0     0.0    0.0\n",
       "..   ...  ...     ...    ...\n",
       "762  2.0  4.0     0.0    0.0\n",
       "763  0.0  2.0     0.0    4.0\n",
       "764  0.0  0.0     0.0    5.0\n",
       "765  0.0  0.0     0.0    4.0\n",
       "766  0.0  0.0     0.0   10.0\n",
       "\n",
       "[341 rows x 4 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[ ['DA', 'DO', 'CRSHRE', 'TRANS'] ]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>765</th>\n",
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       "</table>\n",
       "<p>341 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5\n",
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       "..   ...  ...  ...  ...  ...  ...\n",
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       "\n",
       "[341 rows x 6 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys = pd.get_dummies(df.Work, prefix = None)\n",
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df.Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import neighbors, metrics, grid_search, cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = neighbors.KNeighborsClassifier(n_neighbors = 5, weights = 'uniform')\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_second_classifier(row):\n",
    "    if row.Work < 2:\n",
    "        return 'DA'\n",
    "    elif row.Home < 5:\n",
    "        return 'DO'\n",
    "    else:\n",
    "        return 'CRSHRE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_hat = df.apply(my_second_classifier, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>DA</th>\n",
       "      <th>CRSHRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>763</th>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>765</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Income  Location   DA  CRSHRE\n",
       "0         1         9  0.0     0.0\n",
       "1         4         9  2.0     0.0\n",
       "4         4         9  0.0     0.0\n",
       "7         3         9  5.0     0.0\n",
       "9         1         9  1.0     0.0\n",
       "..      ...       ...  ...     ...\n",
       "762       2         9  2.0     0.0\n",
       "763       3         9  0.0     0.0\n",
       "764       1         9  0.0     0.0\n",
       "765       2         9  0.0     0.0\n",
       "766       5         9  0.0     0.0\n",
       "\n",
       "[341 rows x 4 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Income', 'Location', 'DA', 'CRSHRE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## As the income increases the carpole usage decrease dramatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## future steps will be creating visualization in order to show the results"
   ]
  }
 ],
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