Chapter 6 - NYC Taxi Full Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 6 - NYC Taxi Full Example\n",
    "\n",
    "http://chriswhong.com/open-data/foil_nyc_taxi/\n",
    "\n",
    "http://www.andresmh.com/nyctaxitrips/\n",
    "\n",
    "Potential models\n",
    "\n",
    "* Will the passenger tip the driver?\n",
    "* Will the trip take longer than average?\n",
    "* Will the fare be higher than average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N = 5e6\n",
    "data = pandas.read_csv(\"trip_data_1.csv\", nrows=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medallion</th>\n",
       "      <th>hack_license</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_time_in_secs</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th> payment_type</th>\n",
       "      <th> fare_amount</th>\n",
       "      <th> surcharge</th>\n",
       "      <th> mta_tax</th>\n",
       "      <th> tip_amount</th>\n",
       "      <th> tolls_amount</th>\n",
       "      <th> total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 89D227B655E5C82AECF13C3F540D4CF4</td>\n",
       "      <td> BA96DE419E711691B9445D6A6307C170</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-01 15:11:48</td>\n",
       "      <td> 2013-01-01 15:18:10</td>\n",
       "      <td> 4</td>\n",
       "      <td>  382</td>\n",
       "      <td>  1.0</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.757977</td>\n",
       "      <td>-73.989838</td>\n",
       "      <td> 40.751171</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  6.5</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td>  7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0BD7C8F5BA12B88E0B67BED28BEA73D8</td>\n",
       "      <td> 9FD8F69F0804BDB5549F40E9DA1BE472</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-06 00:18:35</td>\n",
       "      <td> 2013-01-06 00:22:54</td>\n",
       "      <td> 1</td>\n",
       "      <td>  259</td>\n",
       "      <td>  1.5</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.731781</td>\n",
       "      <td>-73.994499</td>\n",
       "      <td> 40.750660</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  6.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td>  7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0BD7C8F5BA12B88E0B67BED28BEA73D8</td>\n",
       "      <td> 9FD8F69F0804BDB5549F40E9DA1BE472</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-05 18:49:41</td>\n",
       "      <td> 2013-01-05 18:54:23</td>\n",
       "      <td> 1</td>\n",
       "      <td>  282</td>\n",
       "      <td>  1.1</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.737770</td>\n",
       "      <td>-74.009834</td>\n",
       "      <td> 40.726002</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  5.5</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td>  7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> DFD2202EE08F7A8DC9A57B02ACB81FE2</td>\n",
       "      <td> 51EE87E3205C985EF8431D850C786310</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-07 23:54:15</td>\n",
       "      <td> 2013-01-07 23:58:20</td>\n",
       "      <td> 2</td>\n",
       "      <td>  244</td>\n",
       "      <td>  0.7</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.759945</td>\n",
       "      <td>-73.984734</td>\n",
       "      <td> 40.759388</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  5.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> DFD2202EE08F7A8DC9A57B02ACB81FE2</td>\n",
       "      <td> 51EE87E3205C985EF8431D850C786310</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-07 23:25:03</td>\n",
       "      <td> 2013-01-07 23:34:24</td>\n",
       "      <td> 1</td>\n",
       "      <td>  560</td>\n",
       "      <td>  2.1</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.748528</td>\n",
       "      <td>-74.002586</td>\n",
       "      <td> 40.747868</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  9.5</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 20D9ECB2CA0767CF7A01564DF2844A3E</td>\n",
       "      <td> 598CCE5B9C1918568DEE71F43CF26CD2</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-07 15:27:48</td>\n",
       "      <td> 2013-01-07 15:38:37</td>\n",
       "      <td> 1</td>\n",
       "      <td>  648</td>\n",
       "      <td>  1.7</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.764252</td>\n",
       "      <td>-73.983322</td>\n",
       "      <td> 40.743763</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  9.5</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 496644932DF3932605C22C7926FF0FE0</td>\n",
       "      <td> 513189AD756FF14FE670D10B92FAF04C</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-08 11:01:15</td>\n",
       "      <td> 2013-01-08 11:08:14</td>\n",
       "      <td> 1</td>\n",
       "      <td>  418</td>\n",
       "      <td>  0.8</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.743977</td>\n",
       "      <td>-74.007416</td>\n",
       "      <td> 40.744343</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  6.0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td>  6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 0B57B9633A2FECD3D3B1944AFC7471CF</td>\n",
       "      <td> CCD4367B417ED6634D986F573A552A62</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-07 12:39:18</td>\n",
       "      <td> 2013-01-07 13:10:56</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1898</td>\n",
       "      <td> 10.7</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.756775</td>\n",
       "      <td>-73.865250</td>\n",
       "      <td> 40.770630</td>\n",
       "      <td> CSH</td>\n",
       "      <td> 34.0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 4.8</td>\n",
       "      <td> 39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 2C0E91FF20A856C891483ED63589F982</td>\n",
       "      <td> 1DA2F6543A62B8ED934771661A9D2FA0</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-07 18:15:47</td>\n",
       "      <td> 2013-01-07 18:20:47</td>\n",
       "      <td> 1</td>\n",
       "      <td>  299</td>\n",
       "      <td>  0.8</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.743137</td>\n",
       "      <td>-73.982712</td>\n",
       "      <td> 40.735336</td>\n",
       "      <td> CSH</td>\n",
       "      <td>  5.5</td>\n",
       "      <td> 1.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td>  7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 2D4B95E2FA7B2E85118EC5CA4570FA58</td>\n",
       "      <td> CD2F522EEE1FF5F5A8D8B679E23576B3</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "      <td> 2013-01-07 15:33:28</td>\n",
       "      <td> 2013-01-07 15:49:26</td>\n",
       "      <td> 2</td>\n",
       "      <td>  957</td>\n",
       "      <td>  2.5</td>\n",
       "      <td>...</td>\n",
       "      <td> 40.786983</td>\n",
       "      <td>-73.952919</td>\n",
       "      <td> 40.806370</td>\n",
       "      <td> CSH</td>\n",
       "      <td> 13.0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 0.5</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          medallion                      hack_license  \\\n",
       "0  89D227B655E5C82AECF13C3F540D4CF4  BA96DE419E711691B9445D6A6307C170   \n",
       "1  0BD7C8F5BA12B88E0B67BED28BEA73D8  9FD8F69F0804BDB5549F40E9DA1BE472   \n",
       "2  0BD7C8F5BA12B88E0B67BED28BEA73D8  9FD8F69F0804BDB5549F40E9DA1BE472   \n",
       "3  DFD2202EE08F7A8DC9A57B02ACB81FE2  51EE87E3205C985EF8431D850C786310   \n",
       "4  DFD2202EE08F7A8DC9A57B02ACB81FE2  51EE87E3205C985EF8431D850C786310   \n",
       "5  20D9ECB2CA0767CF7A01564DF2844A3E  598CCE5B9C1918568DEE71F43CF26CD2   \n",
       "6  496644932DF3932605C22C7926FF0FE0  513189AD756FF14FE670D10B92FAF04C   \n",
       "7  0B57B9633A2FECD3D3B1944AFC7471CF  CCD4367B417ED6634D986F573A552A62   \n",
       "8  2C0E91FF20A856C891483ED63589F982  1DA2F6543A62B8ED934771661A9D2FA0   \n",
       "9  2D4B95E2FA7B2E85118EC5CA4570FA58  CD2F522EEE1FF5F5A8D8B679E23576B3   \n",
       "\n",
       "  vendor_id  rate_code store_and_fwd_flag      pickup_datetime  \\\n",
       "0       CMT          1                  N  2013-01-01 15:11:48   \n",
       "1       CMT          1                  N  2013-01-06 00:18:35   \n",
       "2       CMT          1                  N  2013-01-05 18:49:41   \n",
       "3       CMT          1                  N  2013-01-07 23:54:15   \n",
       "4       CMT          1                  N  2013-01-07 23:25:03   \n",
       "5       CMT          1                  N  2013-01-07 15:27:48   \n",
       "6       CMT          1                  N  2013-01-08 11:01:15   \n",
       "7       CMT          1                  N  2013-01-07 12:39:18   \n",
       "8       CMT          1                  N  2013-01-07 18:15:47   \n",
       "9       CMT          1                  N  2013-01-07 15:33:28   \n",
       "\n",
       "      dropoff_datetime  passenger_count  trip_time_in_secs  trip_distance  \\\n",
       "0  2013-01-01 15:18:10                4                382            1.0   \n",
       "1  2013-01-06 00:22:54                1                259            1.5   \n",
       "2  2013-01-05 18:54:23                1                282            1.1   \n",
       "3  2013-01-07 23:58:20                2                244            0.7   \n",
       "4  2013-01-07 23:34:24                1                560            2.1   \n",
       "5  2013-01-07 15:38:37                1                648            1.7   \n",
       "6  2013-01-08 11:08:14                1                418            0.8   \n",
       "7  2013-01-07 13:10:56                3               1898           10.7   \n",
       "8  2013-01-07 18:20:47                1                299            0.8   \n",
       "9  2013-01-07 15:49:26                2                957            2.5   \n",
       "\n",
       "       ...        pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0      ...              40.757977         -73.989838         40.751171   \n",
       "1      ...              40.731781         -73.994499         40.750660   \n",
       "2      ...              40.737770         -74.009834         40.726002   \n",
       "3      ...              40.759945         -73.984734         40.759388   \n",
       "4      ...              40.748528         -74.002586         40.747868   \n",
       "5      ...              40.764252         -73.983322         40.743763   \n",
       "6      ...              40.743977         -74.007416         40.744343   \n",
       "7      ...              40.756775         -73.865250         40.770630   \n",
       "8      ...              40.743137         -73.982712         40.735336   \n",
       "9      ...              40.786983         -73.952919         40.806370   \n",
       "\n",
       "    payment_type  fare_amount   surcharge   mta_tax   tip_amount  \\\n",
       "0            CSH          6.5         0.0       0.5            0   \n",
       "1            CSH          6.0         0.5       0.5            0   \n",
       "2            CSH          5.5         1.0       0.5            0   \n",
       "3            CSH          5.0         0.5       0.5            0   \n",
       "4            CSH          9.5         0.5       0.5            0   \n",
       "5            CSH          9.5         0.0       0.5            0   \n",
       "6            CSH          6.0         0.0       0.5            0   \n",
       "7            CSH         34.0         0.0       0.5            0   \n",
       "8            CSH          5.5         1.0       0.5            0   \n",
       "9            CSH         13.0         0.0       0.5            0   \n",
       "\n",
       "    tolls_amount   total_amount  \n",
       "0            0.0            7.0  \n",
       "1            0.0            7.0  \n",
       "2            0.0            7.0  \n",
       "3            0.0            6.0  \n",
       "4            0.0           10.5  \n",
       "5            0.0           10.0  \n",
       "6            0.0            6.5  \n",
       "7            4.8           39.3  \n",
       "8            0.0            7.0  \n",
       "9            0.0           13.5  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_data = pandas.read_csv(\"trip_fare_1.csv\", nrows=N)\n",
    "fare_cols = [u' payment_type', u' fare_amount', u' surcharge', u' mta_tax', u' tip_amount', u' tolls_amount', u' total_amount']\n",
    "data = data.join(fare_data[fare_cols])\n",
    "del fare_data\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medallion</th>\n",
       "      <th>hack_license</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> CD847FE5884F10A28217E9FBA11B275B</td>\n",
       "      <td> 5FEFD00D9773268B72EE4E879852F190</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td> 598CCE5B9C1918568DEE71F43CF26CD2</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> A954A71B6D44265AE756BF807E069396</td>\n",
       "      <td> D5CA7D478A14BA3BBFC20153C5C88B1A</td>\n",
       "      <td> CMT</td>\n",
       "      <td> 1</td>\n",
       "      <td> N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> F6F7D02179BE915B23EF2DB57836442D</td>\n",
       "      <td> 088879B44B80CC9ED43724776C539370</td>\n",
       "      <td> VTS</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> BE386D8524FCD16B3727DCF0A32D9B25</td>\n",
       "      <td> 4EB96EC9F3A42794DEE233EC8A2616CE</td>\n",
       "      <td> VTS</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> E9FF471F36A91031FE5B6D6228674089</td>\n",
       "      <td> 72E0B04464AD6513F6A613AABB04E701</td>\n",
       "      <td> VTS</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          medallion                      hack_license  \\\n",
       "0  CD847FE5884F10A28217E9FBA11B275B  5FEFD00D9773268B72EE4E879852F190   \n",
       "1  20D9ECB2CA0767CF7A01564DF2844A3E  598CCE5B9C1918568DEE71F43CF26CD2   \n",
       "2  A954A71B6D44265AE756BF807E069396  D5CA7D478A14BA3BBFC20153C5C88B1A   \n",
       "3  F6F7D02179BE915B23EF2DB57836442D  088879B44B80CC9ED43724776C539370   \n",
       "4  BE386D8524FCD16B3727DCF0A32D9B25  4EB96EC9F3A42794DEE233EC8A2616CE   \n",
       "5  E9FF471F36A91031FE5B6D6228674089  72E0B04464AD6513F6A613AABB04E701   \n",
       "\n",
       "  vendor_id  rate_code store_and_fwd_flag  \n",
       "0       CMT          1                  N  \n",
       "1       CMT          1                  N  \n",
       "2       CMT          1                  N  \n",
       "3       VTS          1                  0  \n",
       "4       VTS          1                  0  \n",
       "5       VTS          1                  0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[:5, data.columns[:5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 300)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VvaJoz/nf9fHm9eM0hEaGFtdDpb8SNJWVcI8J5o829onkSaWyjI6+5G9fyS/5QvI6GlG/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\nA8rv580VjySvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba8fb7b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x=\"trip_time_in_secs\", y=\" total_amount\", kind=\"scatter\", s=2)\n",
    "xlim(0,1e4)\n",
    "ylim(0,300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ind = where(logical_and(data.trip_time_in_secs < 500, data[' total_amount'] > 30))[0]\n",
    "data = data.drop(ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vd2h/CnjLMllE/CNwxTh93QHcMdkYzcyst/y3uczG4ZqJlSyLmomZmVmVk0nmSl+3LT2+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ef2d68850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[logical_and(data.dropoff_latitude > 40.6,data.dropoff_latitude < 40.9)].dropoff_latitude.hist(bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fv/Wro+bS6+DyduCvgG8x8azaSDFA3A+cDowB74uII6nPx4ArgKMU02gPp/YV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\n/wO+yKZxuOf6gwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1f01fe3b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[logical_and(data.dropoff_longitude > -74.05,data.dropoff_longitude < -73.9)].dropoff_longitude.hist(bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H/hHwM7TrFXfR/mO/GTgbmAGuS4VpJK0HPgMcAO6IiC2pfxnwCHAi8FxE3J76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\nmZmV8v8BoX1IB466TqEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9703f871d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.vendor_id.value_counts().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8bud7n7mZWQK8z9z5rclPee1mbeGeuZlZhzVSzCVtl/ReSfslvaKe1LyeGOe3ML/J7Obz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96e2e20510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.rate_code.value_counts().plot(kind=\"bar\", logy=True, ylim=(1,1e8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UjKio1fQEIrpKhtEfKRgREVFJMoyGJcPotWQYEXUkw4iIiNpSMKKiVtMTiOgqGUZ/pGBE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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f971a1c9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.store_and_fwd_flag.value_counts().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9719af7810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.passenger_count.value_counts().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nn3swnBw9GPdrzKyMGe3BSPqqpAOSdvWMzZa0TdIzkrZKGum5b42kZyXtkXRFz/hFknal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\nP8t5tizEPC72AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9719f86590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.trip_time_in_secs[data.trip_time_in_secs < 4000].hist(bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MZWZmbVb3478fiogXJb0H2J5nI/0iIoqzgk5aDszMdg8wF2jk8vcHbNubPxs1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\nONG/efEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9718a4e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.trip_distance[data.trip_distance < 22].hist(bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OpwNw8fzzzdbZ+rozwfrMhJ+/0ZqJ8s5qnTNd6vdzZsLPH7ZmrLyzr8el5m9q3c1U6vtv\n5v+PJr5+srx116NMv0gpsbq6CnCxX25k7Jl5r5mfjIjreutbgBsjYl9vfTuwOyL2j7k/z8ybTOFa\n5BSuRU7hWuQUc1KLWZxnXroqZmbWM00zPw/s7FvvBM5NsoPyl/OXfO7apNIBKpJKB6hIKh2gIqns\nszd1Of8GY5bLgC8ArwMeB+4H9kbEQ2Pur4IxS6J/xnmJKebkT8iEa7Eu4VqsS7gW6xI11GLYmGWs\nZi7pHuAG4ErgAnBHRByXdBNwGNgBHI2IuyYIVEEzb8K8/KA2ksK1yClci5zCtcgpyjbzWXAzbziF\na5FTuBY5hWuRU8xJLaq80ZZn5jVJpQNUJJUOUJFUOkBFUtln9y1wR0nUMANrgmvRl8K1yClci5xi\nTmrhMcvMzMsPaiMpXIucwrXIKVyLnGI+xyxmZtYMz8ytJ5UOUJFUOkBFUukAFUlln90z81ESNczA\nmuBa9KVwLXIK1yKnmJNaeGY+M/Pyg9pICtcip3AtcgrXIqfwzNzMzIZreTNPpQNUJJUOUJFUOkBF\nUukAFUmlA4zU8mZuZjYfPDOfmueBfSlci5zCtcgpXIucwjNzMzMbruXNPJUOUJFUOkBFUukAFUml\nA1QklQ4wUssvGjIz2x580dDMeR7Yl8K1yClci5zCtcgpPDM3M7PhZtLMJe2RdLekE5LeMIvnaEYq\nHaAiqXSAiqTSASqSSgeoSCodYKTLZrHTiPgF4BckLQDvAX5tFs9jZmZdk7yh8zHgTcCF9Td17m1f\nJr8P6M9GxLv7HnsP8G8i4swG+/PMvMkUrkVO4VrkFK5FTjEntWhiZn4cWB7Y6Q7gSG/7tcBeSdeo\n693AL2/UyM3MrFljN/OIOAU8ObB5F3A2ItYi4hngBLAH+GHgdcAtkv5GU2Gbl0oHqEgqHaAiqXSA\niqTSASqSSgcYadqZ+VXAY33rc8DuiNgPfGCzL15ZWWFxcRGAhYUFlpaW6HQ6ABfPP99sna2vOxOs\nz0z4+RutmSjvrNY506V+P2c2eXzcNWPlnX09LjV/U+tuplLffzP/fzTx9ZPlrbseZfpFSonV1VWA\ni/1yIxOdZy5pETi5PjOXdDOwHBH7euvbyc18s315Zt5kCtcip3AtcgrXIqeYk1rM6jzz88DOvvVO\nuq/OzcxsC03bzE8DV0talHQ5cCtw77hfXP5y/pLPXZtUOkBFUukAFUmlA1QklX32pi7nl3QPcANw\nJXABuCMijku6iXxq4tGIuGvM/VUwZknU8J5+TXAt+lK4FjmFa5FTzEkt/B6gMzMvP6iNpHAtcgrX\nIqdwLXKKamfmUyk/ZjEz2x5818SREjX82dQE16IvhWuRU7gWOcWc1KLKV+ZmZtaMlr8yb8K8vOpo\nJIVrkVO4FjmFa5FT+JW5mZkN1/IDoCWfuzapdICKpNIBKpJKB6hIKvvsPgA6SqKGAxpNcC36UrgW\nOYVrkVPMSS18nvnMzMsPaiMpXIucwrXIKVyLnMIzczMzG67lzTyVDlCRVDpARVLpABVJpQNUJJUO\nMFLLm7mZ2XzwzHxqngf2pXAtcgrXIqdwLXIKz8zNzGy4ljfzVDpARVLpABVJpQNUJJUOUJFUOsBI\nLb9oyMxse/BFQzPneWBfCtcip3AtcgrXIqfwzNzMzIZrvJlLeqmkn5X00ab33bxUOkBFUukAFUml\nA1QklQ5QkVQ6wEiNN/OIeDQi3t70fs3MbLixmrmkY5KekPTAwPZlSQ9LekTSgdlEnKVO6QAV6ZQO\nUJFO6QAV6ZQOUJFO6QAjjfvK/Diw3L9B0g7gSG/7tcBeSdc0G8/MzMYxVjOPiFPAkwObdwFnI2It\nIp4BTgB7JL1I0k8DS/W/Wk+lA1QklQ5QkVQ6QEVS6QAVSaUDjHTZFF97FfBY3/ocsDsivgy8c5wd\nrKyssLi4CMDCwgJLS0t0Oh2Ai+efb7bO1tedCdZnJvz8jdZMlHdW65zpUr+fMxN+/rA1Y+WdfT0u\nNX9T626mUt9/M/9/NPH1k+Wtux5l+kVKidXVVYCL/XIjY59nLmkROBkR1/XWNwPLEbGvt76dbjPf\nP+b+fJ55kylci5zCtcgpXIucYk5qMYvzzM8DO/vWO+m+Ojczsy02TTM/DVwtaVHS5cCtwL2T7KD8\n5fwln7s2qXSAiqTSASqSSgeoSCr77E1czi/pHuAG4ErgAnBHRByXdBNwGNgBHI2Iu8YNVseYJVHD\ne/o1wbXoS+Fa5BSuRU4xJ7Xwe4DOzLz8oDaSwrXIKVyLnMK1yCmqnJlPrfyYxcxse/BdE0dK1PBn\nUxNci74UrkVO4VrkFHNSiypfmZuZWTNa/sq8CfPyqqORFK5FTuFa5BSuRU7hV+ZmZjZcyw+Alnzu\n2qTSASqSSgeoSCodoCKp7LP7AOgoiRoOaDTBtehL4VrkFK5FTjEntfB55jMzLz+ojaRwLXIK1yKn\ncC1yCs/MzcxsuJY381Q6QEVS6QAVSaUDVCSVDlCRVDrASC1v5mZm88Ez86l5HtiXwrXIKVyLnMK1\nyCk8Mzczs+Fa3sxT6QAVSaUDVCSVDlCRVDpARVLpACO1/KIhM7PtwRcNzZzngX0pXIucwrXIKVyL\nnMIzczMzG67xZi7phZJ+TtLdkt7c9P6blUoHqEgqHaAiqXSAiqTSASqSSgcYaRavzH8Q+EhEvAP4\nyzPYf4POlA5QEdcicy0y1yKruxZjNXNJxyQ9IemBge3Lkh6W9IikA73NVwGP9f79Bw1mnYGnSgeo\niGuRuRaZa5HVXYtxX5kfB5b7N0jaARzpbb8W2CvpGuAcsHPC/ZuZ2RTGarYRcQp4cmDzLuBsRKxF\nxDPACWAP8DHgZkk/BdzbZNjmrZUOUJG10gEqslY6QEXWSgeoyFrpACONfWqipEXgZERc11vfAtwY\nEft669uB3RGxf8z9lT5HyMxsW9ro1MTLptnfFF+7YRgzM7s008y0z5Nn4/T+fW66OGZmdimmaean\ngaslLUq6HLiV6mfkZmbzadxTE+8BPg18u6THJP1QRPw/4IeB/wB8Hvi3EfHQ7KKamdkwxe7NYlaa\npGXgioj46MD2W4CvRMSvlUlWnqQ/DrwG+K2I+I3SeWxzrWnmkl5L9y+Jl/c2fR74VxHxqXKptp6k\nk33LAPoPREdEVH7VbnMkfRr4/oi4MLD9T9A9c+uVZZJtPUmfAA5ExIOS/hTwWeAzwMuAD0XE+4oG\n3EKS/mhEfGXIY98VEae3OtM4pjmbZduQ9Ca6Fzj9RO9DwCuAo5L2R8QnSubbYu8lN/EPAW8nN/R2\n/GbPnj/YyAEi4ouSXlgiUEGLEfFg798/BPxqRLxF0hV0R6ytaebAJyV9b0R8uX+jpO8FjgHfUibW\naK1o5sA/pPsK7L/3bfuspNN0m3xrmnlEpPV/S3o6Iv5TwTilXSHpm3oXvV0k6ZuAFxTKVEp/DV5P\n9xc9EfFVSd8oE6mYnwE+JekN67/sezcN/EngjUWTjdCWZv6SgUYOQER8TtKLSwSyKnwMuLv319nT\nAL1Xov+y91ibnJO0n+4px68AfgVA0h+mPX0CgIj4kKTfA+6T9Aa6Z+q9E+hExFrRcCO05T/S/73E\nx+aOpBet/xPY0bcGYPBPyzn348A/BtYk/e/etm8FjgLvKpaqjLfRHUG+Hrg1ItZv37Gb7r2ZWiUi\n/rWk36d7q8TfAl4dEV8sHGukVhwAlfQV4D8PefjVEbGwlXlKkrRGno0PvvVKRMS3bXmownqvPl/W\nW56NiK+VzGNlDdwddhG4QH7RFxFx/ZaHGkNbmnlnxMPR8rlxq0l6CfC3gD/X2/Qg8FMR8US5VFtv\n4CynQW07y2lx1OO1jlpa0cxHkbQrIu4vnWOrSPozdM+hfqq3fi3w/XRvCXckIr5eMN6WkvQq4OeB\nn6N7RbOA7wTeCtwWEb9eMN6W8gue7a8VzVzS84AfoPun9IMR8UuSvovu0ekXR8RS0YBbSNL9dM/s\neVzSEvBJunX488DXI+LtRQNuIUn/FXhnRHx2YPsS8DMRsbtMsrJ659lT+4x4ViQ9OuLhakeRbTkA\nejfwUuB+4F2S3kb34qF/FBH/vmiyrfeCiHi89+/bgaMR8d7eL7znnPEz5755sJEDRMQZSd9cIlAp\n6r51/Z10L6zb0dv2B8AHIuJQyWwFfHffv4PubU9uBX4E+G9FEo2hLc38lcD1EfENSS8Afht4WUT8\nTuFcJfRf8fk64McAerUpk6ggSS/a4OKQF/HsOrXB3wNeBXx3RDwKIOnbgJ+W9Pcj4l8UTbeFIuJL\ncPEv+rcA/4DuWS1vjIjPl8w2Slve1u2ZiPgGQET8HvBoSxs5dC+G+Kik9wMLwH0Akv408PtFk229\n9wG/Kqkj6Yrex/fQPcf6cOFsW+0twJvXGzlARPwv4LbeY60h6XJJ7wQeAl4N7ImI22pu5NCemfnX\ngLN9m14G/Gbv39WeajQLkq4G/hrwNPCRiDjf2/4aun+9HCmZb6tJ+kvAAbrvYwvde/b884gYdXbH\n3JH0YER8x6SPzSNJ5+i+Gf1hYP36g/VbYEREVHlBWVvGLNcDL+G5b56xE/g/Wx+nqMPAj0XE5wa2\nP0X3zblb1cwj4heBXyydowLPXOJj8+g/0m3e1/c+BrmZF3QY+NHB80N7B7neB3xfiVCFvGSDRr5+\na4PFrY9TjqQ7+5b9d5AMgIj4iS0PVc71kr465LE/tKVJyntgYB3Al4Bf742eqtSWmflLImLwPxC9\npvbSAnlKGnW1a9tuLvW7dMdNT/f9O+he2n6gYK4tFxE7IuKKIR9tedG37grgj/R9XEH3+oNflrS3\nZLBR2jIzPxsRf3bSx+aRpBPAfRFx98D2fcDrI+LWMsnK6v2V9rfpNvKPAO/d6Pa41l69s5w+GRGv\nKJ1lI235jXta0juGNLC2vYvK3wU+Luk28vf+ncDz6V5Y1SqSrqR7Wt5twIeBv9B3kymziyLiyzWf\nvtuWV+Z/Evg48HU2aGAR0aqDoL0LRL4H+A66Y4X/ERH3lU219SS9h+4vsLvp3o9l2MzYjN5pqz8e\nEa8tnWUjrWjm4AZmz9V704Wvs/HZGhERrboK1LoG7pq47o/RPfPtLVHpG9e3ppmbmY1jg7O6Avid\n9TcwqZWbuZnZHGjLqYlmZnPNzdzMbA64mZuZzQE3czOzOfD/AVmc2bgtAPUNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f079c0aebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[' payment_type'].value_counts().plot(kind=\"bar\", logy=True, ylim=(1,1e8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 40)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FNaITU619MMeC18/VC7X0i2Pn4+qtlB2vkXT69OnIuE9mDCsbKUcz5ah27bUf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ZErMTX1EM6dexoLCwuYm5tDFEXo6dmC++67G4CVNoaGhsrAmOVl6/l0zz2fRxxHyPOsgC2Pymux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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba8fb7b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x=\"trip_time_in_secs\", y=\"trip_distance\", kind=\"scatter\", s=2)\n",
    "xlim(0,5000)\n",
    "ylim(0,40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-74.05, -73.9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2/0FEHzmO4x+xd0mAE4lGvKIhn+0QRg7UkfPf09ZT9MAjoC3kUiLQEUeJAyGJ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IJO0o6U0JMIMHf/ZcuXrG/CFpuUxRZHYi7Zvr1xOXkKxe/e76Zl5ztkgc5t7ymxHUNiKb7+H5tw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\n38vM9zPzG5n5zpzwu4JOCwDoiU/kxISe7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba7ed7f4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(16,8))\n",
    "plot(data[\"pickup_latitude\"], data[\"pickup_longitude\"], 'b,')\n",
    "xlim(40.6, 40.9)\n",
    "ylim(-74.05, -73.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nbFouOauqVUQkfRJ4OSKe4fBZyFukrrY7wWZmHTat5v1+FVgs6RPAO4F3S7oP2Cfp7IjY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\niJmZ1fb/AdShCqZ8tcUIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9203c5f9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data[' tip_amount'] < 15][' tip_amount'].hist(bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3633518"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)\n",
    "data = data[data[' payment_type'] != \"CSH\"]\n",
    "data.reset_index(inplace=True, drop=True)\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building first model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3538547\n",
       "0      94971\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup target\n",
    "data['tipped'] = (data[' tip_amount'] > 0).astype(\"int\")\n",
    "data['tipped'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feats1 = [u'rate_code', 'passenger_count', u'trip_time_in_secs', u'trip_distance', u'pickup_longitude', u'pickup_latitude', u'dropoff_longitude', u'dropoff_latitude', ' fare_amount', u' surcharge', u' mta_tax', ' tolls_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "M = len(data)\n",
    "rand_idx = arange(M)\n",
    "random.shuffle(rand_idx)\n",
    "train_idx = rand_idx[int(M*0.2):]\n",
    "test_idx = rand_idx[:int(M*0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2906815, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "data_scaled = sc.fit_transform(data[feats1])\n",
    "data_scaled[train_idx.tolist(),:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,\n",
       "       fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',\n",
       "       loss='modified_huber', n_iter=5, n_jobs=1, penalty='l2',\n",
       "       power_t=0.5, random_state=None, shuffle=False, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd = SGDClassifier(loss=\"modified_huber\")\n",
    "sgd.fit(data.ix[train_idx,feats1], data['tipped'].ix[train_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "preds = sgd.predict_proba(data.ix[test_idx,feats1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thr = roc_curve(data['tipped'].ix[test_idx], preds[:,1])\n",
    "auc = roc_auc_score(data['tipped'].ix[test_idx], preds[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5053715706684706"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f91eb2fb150>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ronkPLxKernq/u88ws37R6/e6+0wzewGYRni50DFVi4KIQHk5XHQRHHpCmh2vSPLf7ykl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91ee38dd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(fpr,tpr)\n",
    "plot(fpr,fpr)\n",
    "xlabel(\"False positive rate\")\n",
    "ylabel(\"True positive rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(0, inplace=True)\n",
    "count_nonzero(pandas.isnull(data.ix[train_idx,feats1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf1 = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "rf1.fit(data.ix[train_idx,feats1], data['tipped'].ix[train_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "preds1 = rf1.predict_proba(data.ix[test_idx,feats1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr1, tpr1, thr1 = roc_curve(data['tipped'].ix[test_idx], preds1[:,1])\n",
    "auc1 = roc_auc_score(data['tipped'].ix[test_idx], preds1[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print auc1\n",
    "rf1.score(data.ix[test_idx,feats1], data.ix[test_idx,'tipped'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f91f0b76e90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LVQxpWLoU9t8f9t477NecTaoSRCQumVYMSgyN+PFHOPZYWHddeOghWCOLi4ho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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91f0105250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(fpr1,tpr1)\n",
    "plot(fpr1,fpr1)\n",
    "xlabel(\"False positive rate\")\n",
    "ylabel(\"True positive rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>  dropoff_latitude</td>\n",
       "      <td> 0.165411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td> dropoff_longitude</td>\n",
       "      <td> 0.163337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>   pickup_latitude</td>\n",
       "      <td> 0.163068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>  pickup_longitude</td>\n",
       "      <td> 0.160285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td> trip_time_in_secs</td>\n",
       "      <td> 0.122214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>     trip_distance</td>\n",
       "      <td> 0.112020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>       fare_amount</td>\n",
       "      <td> 0.067795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>   passenger_count</td>\n",
       "      <td> 0.017850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>         surcharge</td>\n",
       "      <td> 0.014259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>         rate_code</td>\n",
       "      <td> 0.006974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>      tolls_amount</td>\n",
       "      <td> 0.004067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>           mta_tax</td>\n",
       "      <td> 0.002720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Feature  Importance\n",
       "0    dropoff_latitude    0.165411\n",
       "1   dropoff_longitude    0.163337\n",
       "2     pickup_latitude    0.163068\n",
       "3    pickup_longitude    0.160285\n",
       "4   trip_time_in_secs    0.122214\n",
       "5       trip_distance    0.112020\n",
       "6         fare_amount    0.067795\n",
       "7     passenger_count    0.017850\n",
       "8           surcharge    0.014259\n",
       "9           rate_code    0.006974\n",
       "10       tolls_amount    0.004067\n",
       "11            mta_tax    0.002720"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = zip(feats1, rf1.feature_importances_)\n",
    "fi.sort(key=lambda x: -x[1])\n",
    "pandas.DataFrame(fi, columns=[\"Feature\",\"Importance\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-c:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "data['trip_time_in_secs'][data['trip_time_in_secs'] < 1e-3] = -1\n",
    "data['speed'] = data['trip_distance'] / data['trip_time_in_secs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feats2 = feats1 + ['speed']\n",
    "feats2.remove('trip_time_in_secs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rf2 = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "rf2.fit(data.ix[train_idx,feats2], data['tipped'].ix[train_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "preds2 = rf2.predict_proba(data.ix[test_idx,feats2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr2, tpr2, thr2 = roc_curve(data['tipped'].ix[test_idx], preds2[:,1])\n",
    "auc2 = roc_auc_score(data['tipped'].ix[test_idx], preds2[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print auc2\n",
    "plot(fpr2,tpr2)\n",
    "plot(fpr2,fpr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fi2 = zip(feats2, rf2.feature_importances_)\n",
    "fi2.sort(key=lambda x: x[1])\n",
    "fi2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feats3 = feats1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'rate_code',\n",
       " 'passenger_count',\n",
       " u'trip_time_in_secs',\n",
       " u'trip_distance',\n",
       " u'pickup_longitude',\n",
       " u'pickup_latitude',\n",
       " u'dropoff_longitude',\n",
       " u'dropoff_latitude',\n",
       " ' fare_amount',\n",
       " u' surcharge',\n",
       " u' mta_tax',\n",
       " ' tolls_amount']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cat_to_num(data):\n",
    "    categories = unique(data)\n",
    "    features = {}\n",
    "    for cat in categories:\n",
    "        binary = (data == cat)\n",
    "        features[\"%s:%s\"%(data.name, cat)] = binary.astype(\"int\")\n",
    "    return pandas.DataFrame(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "payment_type_cats = cat_to_num(data[' payment_type'])\n",
    "vendor_id_cats = cat_to_num(data['vendor_id'])\n",
    "store_and_fwd_flag_cats = cat_to_num(data['store_and_fwd_flag'])\n",
    "rate_code = cat_to_num(data['rate_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.join(payment_type_cats)\n",
    "feats3 += payment_type_cats.columns\n",
    "data = data.join(vendor_id_cats)\n",
    "feats3 += vendor_id_cats.columns\n",
    "data = data.join(store_and_fwd_flag_cats)\n",
    "feats3 += store_and_fwd_flag_cats.columns\n",
    "data = data.join(rate_code)\n",
    "feats3 += rate_code.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'rate_code',\n",
       " 'passenger_count',\n",
       " u'trip_time_in_secs',\n",
       " u'trip_distance',\n",
       " u'pickup_longitude',\n",
       " u'pickup_latitude',\n",
       " u'dropoff_longitude',\n",
       " u'dropoff_latitude',\n",
       " ' fare_amount',\n",
       " u' surcharge',\n",
       " u' mta_tax',\n",
       " ' tolls_amount',\n",
       " ' payment_type:CRD',\n",
       " ' payment_type:DIS',\n",
       " ' payment_type:NOC',\n",
       " ' payment_type:UNK',\n",
       " 'vendor_id:CMT',\n",
       " 'vendor_id:VTS',\n",
       " 'store_and_fwd_flag:0',\n",
       " 'store_and_fwd_flag:N',\n",
       " 'store_and_fwd_flag:Y',\n",
       " 'rate_code:0',\n",
       " 'rate_code:1',\n",
       " 'rate_code:2',\n",
       " 'rate_code:210',\n",
       " 'rate_code:28',\n",
       " 'rate_code:3',\n",
       " 'rate_code:4',\n",
       " 'rate_code:5',\n",
       " 'rate_code:6',\n",
       " 'rate_code:8']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf3 = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "rf3.fit(data.ix[train_idx,feats3], data['tipped'].ix[train_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rf3.score(data.ix[test_idx,feats3], data.ix[test_idx,'tipped'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.655894612736\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f61abdf8150>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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876e+BBFJkpbEaEBVVWg+2nxzGDYs3pnNqhJEJJ/S2pRU9K65BhYvhkceiTcp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61c664a350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr3, tpr3, thr3 = roc_curve(data['tipped'].ix[test_idx], preds3[:,1])\n",
    "auc3 = roc_auc_score(data['tipped'].ix[test_idx], preds3[:,1])\n",
    "print auc3\n",
    "plot(fpr3,tpr3)\n",
    "plot(fpr3,fpr3)\n",
    "xlabel(\"False positive rate\")\n",
    "ylabel(\"True positive rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>     dropoff_latitude</td>\n",
       "      <td> 0.163023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>      pickup_latitude</td>\n",
       "      <td> 0.161114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>    dropoff_longitude</td>\n",
       "      <td> 0.160988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>     pickup_longitude</td>\n",
       "      <td> 0.158672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>    trip_time_in_secs</td>\n",
       "      <td> 0.111172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>        trip_distance</td>\n",
       "      <td> 0.106693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>          fare_amount</td>\n",
       "      <td> 0.067567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>      passenger_count</td>\n",
       "      <td> 0.019286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>            surcharge</td>\n",
       "      <td> 0.010330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>     payment_type:NOC</td>\n",
       "      <td> 0.008361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>     payment_type:CRD</td>\n",
       "      <td> 0.008247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>            rate_code</td>\n",
       "      <td> 0.004074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>         tolls_amount</td>\n",
       "      <td> 0.003869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>          rate_code:5</td>\n",
       "      <td> 0.003375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>     payment_type:DIS</td>\n",
       "      <td> 0.003146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>              mta_tax</td>\n",
       "      <td> 0.001742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>        vendor_id:VTS</td>\n",
       "      <td> 0.001604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td> store_and_fwd_flag:N</td>\n",
       "      <td> 0.001587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>        vendor_id:CMT</td>\n",
       "      <td> 0.001350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td> store_and_fwd_flag:0</td>\n",
       "      <td> 0.001072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>          rate_code:1</td>\n",
       "      <td> 0.000757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>     payment_type:UNK</td>\n",
       "      <td> 0.000625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>          rate_code:2</td>\n",
       "      <td> 0.000494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td> store_and_fwd_flag:Y</td>\n",
       "      <td> 0.000432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>          rate_code:3</td>\n",
       "      <td> 0.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>          rate_code:0</td>\n",
       "      <td> 0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>          rate_code:4</td>\n",
       "      <td> 0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>          rate_code:6</td>\n",
       "      <td> 0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>          rate_code:8</td>\n",
       "      <td> 0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>        rate_code:210</td>\n",
       "      <td> 0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>         rate_code:28</td>\n",
       "      <td> 0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Feature  Importance\n",
       "0       dropoff_latitude    0.163023\n",
       "1        pickup_latitude    0.161114\n",
       "2      dropoff_longitude    0.160988\n",
       "3       pickup_longitude    0.158672\n",
       "4      trip_time_in_secs    0.111172\n",
       "5          trip_distance    0.106693\n",
       "6            fare_amount    0.067567\n",
       "7        passenger_count    0.019286\n",
       "8              surcharge    0.010330\n",
       "9       payment_type:NOC    0.008361\n",
       "10      payment_type:CRD    0.008247\n",
       "11             rate_code    0.004074\n",
       "12          tolls_amount    0.003869\n",
       "13           rate_code:5    0.003375\n",
       "14      payment_type:DIS    0.003146\n",
       "15               mta_tax    0.001742\n",
       "16         vendor_id:VTS    0.001604\n",
       "17  store_and_fwd_flag:N    0.001587\n",
       "18         vendor_id:CMT    0.001350\n",
       "19  store_and_fwd_flag:0    0.001072\n",
       "20           rate_code:1    0.000757\n",
       "21      payment_type:UNK    0.000625\n",
       "22           rate_code:2    0.000494\n",
       "23  store_and_fwd_flag:Y    0.000432\n",
       "24           rate_code:3    0.000184\n",
       "25           rate_code:0    0.000127\n",
       "26           rate_code:4    0.000108\n",
       "27           rate_code:6    0.000002\n",
       "28           rate_code:8    0.000001\n",
       "29         rate_code:210    0.000000\n",
       "30          rate_code:28    0.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi3 = zip(feats3, rf3.feature_importances_)\n",
    "fi3.sort(key=lambda x: -x[1])\n",
    "pandas.DataFrame(fi3, columns=[\"Feature\",\"Importance\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feats4 = feats3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Datetime features (hour of day, day of week, week of year)\n",
    "pickup = pandas.to_datetime(data['pickup_datetime'])\n",
    "dropoff = pandas.to_datetime(data['dropoff_datetime'])\n",
    "data['pickup_hour'] = pickup.apply(lambda e: e.hour)\n",
    "data['pickup_day'] = pickup.apply(lambda e: e.dayofweek)\n",
    "#data['pickup_week'] = pickup.apply(lambda e: e.week)\n",
    "data['dropoff_hour'] = dropoff.apply(lambda e: e.hour)\n",
    "data['dropoff_day'] = dropoff.apply(lambda e: e.dayofweek)\n",
    "#data['dropoff_week'] = dropoff.apply(lambda e: e.week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feats4 += ['pickup_hour', 'pickup_day', 'dropoff_hour', 'dropoff_day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'rate_code',\n",
       " 'passenger_count',\n",
       " u'trip_time_in_secs',\n",
       " u'trip_distance',\n",
       " u'pickup_longitude',\n",
       " u'pickup_latitude',\n",
       " u'dropoff_longitude',\n",
       " u'dropoff_latitude',\n",
       " ' fare_amount',\n",
       " u' surcharge',\n",
       " u' mta_tax',\n",
       " ' tolls_amount',\n",
       " 'pickup_hour',\n",
       " 'pickup_day',\n",
       " 'pickup_week',\n",
       " 'dropoff_hour',\n",
       " 'dropoff_day',\n",
       " 'dropoff_week']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf4 = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "rf4.fit(data.ix[train_idx,feats4], data['tipped'].ix[train_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97507922768999167"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds4 = rf4.predict_proba(data.ix[test_idx,feats4])\n",
    "rf4.score(data.ix[test_idx,feats4], data.ix[test_idx,'tipped'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.662020800456\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f61add7f410>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TkJQhWrJjyVnHa5aqqX4J/dSzVk+uBsGv0BMKbgQiAADgU38e/VMjlozQu4ve1ba0bWcd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61addaffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr4, tpr4, thr4 = roc_curve(data['tipped'].ix[test_idx], preds4[:,1])\n",
    "auc4 = roc_auc_score(data['tipped'].ix[test_idx], preds4[:,1])\n",
    "print auc4\n",
    "figure(figsize=(14,8))\n",
    "plot(fpr4,tpr4, \"g-\", linewidth=3)\n",
    "plot(fpr4,fpr4, \"k-\", linewidth=1)\n",
    "xlabel(\"False positive rate\")\n",
    "ylabel(\"True positive rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>     dropoff_latitude</td>\n",
       "      <td> 1.342966e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>    dropoff_longitude</td>\n",
       "      <td> 1.332890e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>      pickup_latitude</td>\n",
       "      <td> 1.326898e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>     pickup_longitude</td>\n",
       "      <td> 1.314082e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>    trip_time_in_secs</td>\n",
       "      <td> 1.000610e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>        trip_distance</td>\n",
       "      <td> 9.924300e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>          fare_amount</td>\n",
       "      <td> 7.099682e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>         dropoff_hour</td>\n",
       "      <td> 3.942205e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>          pickup_hour</td>\n",
       "      <td> 3.924598e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>           pickup_day</td>\n",
       "      <td> 2.417833e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>          dropoff_day</td>\n",
       "      <td> 2.413835e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>      passenger_count</td>\n",
       "      <td> 2.300375e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>     payment_type:CRD</td>\n",
       "      <td> 9.137798e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>     payment_type:NOC</td>\n",
       "      <td> 8.063106e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>            surcharge</td>\n",
       "      <td> 5.429348e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>            rate_code</td>\n",
       "      <td> 4.786564e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>         tolls_amount</td>\n",
       "      <td> 4.155098e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>          rate_code:5</td>\n",
       "      <td> 2.718071e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>     payment_type:DIS</td>\n",
       "      <td> 2.717370e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>              mta_tax</td>\n",
       "      <td> 2.096613e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>        vendor_id:CMT</td>\n",
       "      <td> 1.639687e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td> store_and_fwd_flag:N</td>\n",
       "      <td> 1.561414e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td> store_and_fwd_flag:0</td>\n",
       "      <td> 1.335843e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>        vendor_id:VTS</td>\n",
       "      <td> 1.149871e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>          rate_code:1</td>\n",
       "      <td> 8.534049e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>     payment_type:UNK</td>\n",
       "      <td> 8.062925e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td> store_and_fwd_flag:Y</td>\n",
       "      <td> 6.048130e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>          rate_code:2</td>\n",
       "      <td> 5.845484e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>          rate_code:4</td>\n",
       "      <td> 1.482718e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>          rate_code:3</td>\n",
       "      <td> 1.346250e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>          rate_code:0</td>\n",
       "      <td> 1.015749e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>          rate_code:6</td>\n",
       "      <td> 1.928360e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>          rate_code:8</td>\n",
       "      <td> 8.636844e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>         rate_code:28</td>\n",
       "      <td> 3.379825e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>        rate_code:210</td>\n",
       "      <td> 9.253761e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Feature    Importance\n",
       "0       dropoff_latitude  1.342966e-01\n",
       "1      dropoff_longitude  1.332890e-01\n",
       "2        pickup_latitude  1.326898e-01\n",
       "3       pickup_longitude  1.314082e-01\n",
       "4      trip_time_in_secs  1.000610e-01\n",
       "5          trip_distance  9.924300e-02\n",
       "6            fare_amount  7.099682e-02\n",
       "7           dropoff_hour  3.942205e-02\n",
       "8            pickup_hour  3.924598e-02\n",
       "9             pickup_day  2.417833e-02\n",
       "10           dropoff_day  2.413835e-02\n",
       "11       passenger_count  2.300375e-02\n",
       "12      payment_type:CRD  9.137798e-03\n",
       "13      payment_type:NOC  8.063106e-03\n",
       "14             surcharge  5.429348e-03\n",
       "15             rate_code  4.786564e-03\n",
       "16          tolls_amount  4.155098e-03\n",
       "17           rate_code:5  2.718071e-03\n",
       "18      payment_type:DIS  2.717370e-03\n",
       "19               mta_tax  2.096613e-03\n",
       "20         vendor_id:CMT  1.639687e-03\n",
       "21  store_and_fwd_flag:N  1.561414e-03\n",
       "22  store_and_fwd_flag:0  1.335843e-03\n",
       "23         vendor_id:VTS  1.149871e-03\n",
       "24           rate_code:1  8.534049e-04\n",
       "25      payment_type:UNK  8.062925e-04\n",
       "26  store_and_fwd_flag:Y  6.048130e-04\n",
       "27           rate_code:2  5.845484e-04\n",
       "28           rate_code:4  1.482718e-04\n",
       "29           rate_code:3  1.346250e-04\n",
       "30           rate_code:0  1.015749e-04\n",
       "31           rate_code:6  1.928360e-06\n",
       "32           rate_code:8  8.636844e-07\n",
       "33          rate_code:28  3.379825e-08\n",
       "34         rate_code:210  9.253761e-09"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi4 = zip(feats4, rf4.feature_importances_)\n",
    "fi4.sort(key=lambda x: -x[1])\n",
    "pandas.DataFrame(fi4, columns=[\"Feature\",\"Importance\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    987113\n",
       "1        21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[data[' payment_type:CSH'] == 1,'tipped'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-74.05, -73.9)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3vZZWRrBntXUG8RpBr/MFRYj5PyJSjRVE2LbKSUTF7zmi48i9pNh0DCkFSCLRdSKCahm5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t6qNeMhzwGpfy5G74VCyL0MpVK+PHfVlxSst4uR32eti4WFd8bbEuCtGwZGNafLH0B4WDxg2617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I0G8f09KV9og06QvWQCr3r60bPtcs1jal4GJN6nT0iwsAAIcCAbwSJa8bisxl7shFj/Z9kUsn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\nvUHaz4nIA+f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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1f003f4810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(16,8))\n",
    "plot(data[data['tipped'] == True][\"dropoff_latitude\"], data[data['tipped'] == True][\"dropoff_longitude\"], 'b,')\n",
    "plot(data[data['tipped'] == False][\"dropoff_latitude\"], data[data['tipped'] == False][\"dropoff_longitude\"], 'r,')\n",
    "xlim(40.6, 40.9)\n",
    "ylim(-74.05, -73.9)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}