notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1b1c30b-5503-4896-a6ae-3fde8c58a857",
    "_execution_state": "idle",
    "_uuid": "6253fc3613cae37331065f8a7b510ab3ee469e8a",
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fastest_routes_test.csv\n",
      "fastest_routes_train_part_1.csv\n",
      "fastest_routes_train_part_2.csv\n",
      "test.csv\n",
      "train.csv\n",
      "weather_data_nyc_centralpark_2016.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "import xgboost\n",
    "%matplotlib inline\n",
    "\n",
    "from subprocess import check_output\n",
    "print(check_output([\"ls\", \"input/\"]).decode(\"utf8\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "2731cf63-63f6-493e-8aec-ad0b8510b11e",
    "_execution_state": "idle",
    "_uuid": "7c3dd1c5411eb81518d34c98ed6843c12b6238bc",
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.11792755126953\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('input/train.csv')\n",
    "df.passenger_count = df.passenger_count.astype(np.uint8)\n",
    "df.vendor_id = df.vendor_id.astype(np.uint8)\n",
    "df.trip_duration = df.trip_duration.astype(np.uint32)\n",
    "for c in [c for c in df.columns if c.endswith('tude')]:\n",
    "    df.loc[:,c] = df[c].astype(np.float32)\n",
    "print(df.memory_usage().sum()/2**20)\n",
    "df.pickup_datetime=pd.to_datetime(df.pickup_datetime)\n",
    "df.dropoff_datetime=pd.to_datetime(df.dropoff_datetime)\n",
    "df['pu_hour'] = df.pickup_datetime.dt.hour\n",
    "df['yday'] = df.pickup_datetime.dt.dayofyear\n",
    "df['wday'] = df.pickup_datetime.dt.dayofweek\n",
    "df['month'] = df.pickup_datetime.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b477a032860>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gISHBWDrh4OBAZmamycryP9nOTSvLIiIiIqZynSQnJSUxc+ZMEhMTiYmJITo6mg4dOgBQ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ng8HvT19eHp6YlFixYxSNW8/Px8uWevC/vvv/8e3bp1Y5CKEPJHUZFM2l1eXh62bduG4cOHs47S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b476c0056d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context(\"paper\",font_scale=2)\n",
    "corr = df.corr()\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "f, ax = plt.subplots(figsize=(11,9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0,\n",
    "           square=True, linewidths=0.5, cbar_kws={\"shrink\":0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5fc7753c18>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1tQEAampq8Oijj2Lp0qVISEiwQ+lEROQsrAobmUzW5ekzAKitrQUA+Pn5WbzGaDSivr4e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5fb4f1fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1)\n",
    "sns.distplot(df['trip_duration']/3600,ax=ax,bins=100,kde=False,hist_kws={'log':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5fbef3b390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aS2GjVCqh1WoRFhbm1NfS0gKz2YzU1FSH9pSUFDQ3N6OnpwcA0NraiqVLl2LlypXIysoa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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5fbba64978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1)\n",
    "ax.set_xlim(0,30)\n",
    "sns.distplot(df['trip_duration']/3600,ax=ax,bins=1000,kde=False,hist_kws={'log':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "0096fc06-fdc4-4e69-800b-ef8612d3a87a",
    "_execution_state": "idle",
    "_uuid": "24be42e88b116df8b0ccdc9d5275b33aafd52934",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def haversine(lon1, lat1, lon2, lat2):\n",
    "\n",
    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n",
    "\n",
    "    c = 2 * np.arcsin(np.sqrt(a))\n",
    "    km = 6367 * c\n",
    "    miles = km *  0.621371\n",
    "    return miles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "e328c381-57ff-4eb1-a422-24c7c746de60",
    "_execution_state": "idle",
    "_uuid": "37c0d27a3f54f11cb4505b31dab8211d9f50b5d5",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['distance'] = haversine(df.pickup_longitude, df.pickup_latitude,\n",
    "                                           df.dropoff_longitude, df.dropoff_latitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "bc07643c-8d28-4bc6-adf6-c42a16951ad0",
    "_execution_state": "idle",
    "_uuid": "c58addc802a84283bdb6995f87c916f091469cff",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wdf = pd.read_csv('input/weather_data_nyc_centralpark_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "5460a272-365c-4cf3-b762-73fc2355581b",
    "_execution_state": "idle",
    "_uuid": "c899eabb0c5c723112f1c67c7b9d472d3a733872",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wdf['date']=pd.to_datetime(wdf.date,format='%d-%m-%Y')\n",
    "wdf['yday'] = wdf.date.dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "502ddf18-340f-4d4b-a8bd-c0ca8cd3da2e",
    "_execution_state": "idle",
    "_uuid": "d7cb7e9d02379be0d4b36ddfeea80fb82a24c615",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temerature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>yday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  maximum temerature  minimum temperature  average temperature  \\\n",
       "0 2016-01-01                  42                   34                 38.0   \n",
       "1 2016-01-02                  40                   32                 36.0   \n",
       "2 2016-01-03                  45                   35                 40.0   \n",
       "3 2016-01-04                  36                   14                 25.0   \n",
       "4 2016-01-05                  29                   11                 20.0   \n",
       "\n",
       "  precipitation snow fall snow depth  yday  \n",
       "0          0.00       0.0          0     1  \n",
       "1          0.00       0.0          0     2  \n",
       "2          0.00       0.0          0     3  \n",
       "3          0.00       0.0          0     4  \n",
       "4          0.00       0.0          0     5  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wdf['snowfall'] = wdf['snow fall'].replace(['T'],0.05).astype(np.float32)\n",
    "wdf['precipitation'] = wdf['precipitation'].replace(['T'],0.05).astype(np.float32)\n",
    "wdf['snowdepth'] = wdf['snow depth'].replace(['T'],0.05).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "f589e6d7-659b-411d-a755-778b6aaf1e3f",
    "_execution_state": "idle",
    "_uuid": "10924046e6f8106f4fdeba52b1f4225e5a824ee5",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df,wdf,on='yday')\n",
    "df = df.drop(['date','maximum temerature','minimum temperature'],axis=1)\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b478029a748>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mdfr378/27duJiIjgyJEj/Prrr4SFhdGrVy/Onz//UM/x//7v/7C3t2fkyJH89ttv7Ny5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\nhBBCCJGz/wWU+BkjT4/msAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b478c441278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context(\"paper\",font_scale=2)\n",
    "corr = df.corr()\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "f, ax = plt.subplots(figsize=(11,9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=0.3, center=0,\n",
    "           square=True, linewidths=0.5, cbar_kws={\"shrink\":0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>pu_hour</th>\n",
       "      <th>yday</th>\n",
       "      <th>wday</th>\n",
       "      <th>month</th>\n",
       "      <th>distance</th>\n",
       "      <th>average temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vendor_id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.287415</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.004496</td>\n",
       "      <td>0.020304</td>\n",
       "      <td>0.009299</td>\n",
       "      <td>-0.006025</td>\n",
       "      <td>0.001311</td>\n",
       "      <td>-0.006221</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>-0.005760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>0.287415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>-0.005125</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>-0.002762</td>\n",
       "      <td>0.008471</td>\n",
       "      <td>0.009101</td>\n",
       "      <td>-0.001784</td>\n",
       "      <td>0.025191</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>-0.001203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022568</td>\n",
       "      <td>0.783582</td>\n",
       "      <td>0.100190</td>\n",
       "      <td>0.026542</td>\n",
       "      <td>0.010150</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.259780</td>\n",
       "      <td>0.005547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>0.001742</td>\n",
       "      <td>-0.005125</td>\n",
       "      <td>0.022568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114884</td>\n",
       "      <td>0.494038</td>\n",
       "      <td>-0.029204</td>\n",
       "      <td>0.010603</td>\n",
       "      <td>-0.002516</td>\n",
       "      <td>-0.028976</td>\n",
       "      <td>-0.001439</td>\n",
       "      <td>-0.210354</td>\n",
       "      <td>-0.002011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>0.001528</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>0.783582</td>\n",
       "      <td>0.114884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124873</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>-0.022455</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>-0.001130</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>0.134033</td>\n",
       "      <td>0.005502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>0.004496</td>\n",
       "      <td>-0.002762</td>\n",
       "      <td>0.100190</td>\n",
       "      <td>0.494038</td>\n",
       "      <td>0.124873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020677</td>\n",
       "      <td>0.013612</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>-0.021762</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>-0.142128</td>\n",
       "      <td>-0.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_duration</th>\n",
       "      <td>0.020304</td>\n",
       "      <td>0.008471</td>\n",
       "      <td>0.026542</td>\n",
       "      <td>-0.029204</td>\n",
       "      <td>0.014678</td>\n",
       "      <td>-0.020677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>0.006607</td>\n",
       "      <td>0.094777</td>\n",
       "      <td>0.004574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pu_hour</th>\n",
       "      <td>0.009299</td>\n",
       "      <td>0.009101</td>\n",
       "      <td>0.010150</td>\n",
       "      <td>0.010603</td>\n",
       "      <td>-0.022455</td>\n",
       "      <td>0.013612</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003340</td>\n",
       "      <td>-0.084255</td>\n",
       "      <td>-0.003438</td>\n",
       "      <td>-0.015813</td>\n",
       "      <td>-0.004948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yday</th>\n",
       "      <td>-0.006025</td>\n",
       "      <td>-0.001784</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>-0.002516</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>-0.003340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013615</td>\n",
       "      <td>0.985657</td>\n",
       "      <td>0.016443</td>\n",
       "      <td>0.837212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wday</th>\n",
       "      <td>0.001311</td>\n",
       "      <td>0.025191</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>-0.028976</td>\n",
       "      <td>-0.001130</td>\n",
       "      <td>-0.021762</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.084255</td>\n",
       "      <td>-0.013615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014544</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>-0.019799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.006221</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>-0.001439</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.006607</td>\n",
       "      <td>-0.003438</td>\n",
       "      <td>0.985657</td>\n",
       "      <td>-0.014544</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015668</td>\n",
       "      <td>0.814295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0.008109</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>0.259780</td>\n",
       "      <td>-0.210354</td>\n",
       "      <td>0.134033</td>\n",
       "      <td>-0.142128</td>\n",
       "      <td>0.094777</td>\n",
       "      <td>-0.015813</td>\n",
       "      <td>0.016443</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>0.015668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average temperature</th>\n",
       "      <td>-0.005760</td>\n",
       "      <td>-0.001203</td>\n",
       "      <td>0.005547</td>\n",
       "      <td>-0.002011</td>\n",
       "      <td>0.005502</td>\n",
       "      <td>-0.000997</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>-0.004948</td>\n",
       "      <td>0.837212</td>\n",
       "      <td>-0.019799</td>\n",
       "      <td>0.814295</td>\n",
       "      <td>0.018631</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     vendor_id  passenger_count  pickup_longitude  \\\n",
       "vendor_id             1.000000         0.287415          0.007820   \n",
       "passenger_count       0.287415         1.000000          0.002169   \n",
       "pickup_longitude      0.007820         0.002169          1.000000   \n",
       "pickup_latitude       0.001742        -0.005125          0.022568   \n",
       "dropoff_longitude     0.001528        -0.000343          0.783582   \n",
       "dropoff_latitude      0.004496        -0.002762          0.100190   \n",
       "trip_duration         0.020304         0.008471          0.026542   \n",
       "pu_hour               0.009299         0.009101          0.010150   \n",
       "yday                 -0.006025        -0.001784          0.004263   \n",
       "wday                  0.001311         0.025191         -0.016356   \n",
       "month                -0.006221        -0.002143          0.004474   \n",
       "distance              0.008109         0.010306          0.259780   \n",
       "average temperature  -0.005760        -0.001203          0.005547   \n",
       "\n",
       "                     pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "vendor_id                   0.001742           0.001528          0.004496   \n",
       "passenger_count            -0.005125          -0.000343         -0.002762   \n",
       "pickup_longitude            0.022568           0.783582          0.100190   \n",
       "pickup_latitude             1.000000           0.114884          0.494038   \n",
       "dropoff_longitude           0.114884           1.000000          0.124873   \n",
       "dropoff_latitude            0.494038           0.124873          1.000000   \n",
       "trip_duration              -0.029204           0.014678         -0.020677   \n",
       "pu_hour                     0.010603          -0.022455          0.013612   \n",
       "yday                       -0.002516           0.004612         -0.001554   \n",
       "wday                       -0.028976          -0.001130         -0.021762   \n",
       "month                      -0.001439           0.004758         -0.000607   \n",
       "distance                   -0.210354           0.134033         -0.142128   \n",
       "average temperature        -0.002011           0.005502         -0.000997   \n",
       "\n",
       "                     trip_duration   pu_hour      yday      wday     month  \\\n",
       "vendor_id                 0.020304  0.009299 -0.006025  0.001311 -0.006221   \n",
       "passenger_count           0.008471  0.009101 -0.001784  0.025191 -0.002143   \n",
       "pickup_longitude          0.026542  0.010150  0.004263 -0.016356  0.004474   \n",
       "pickup_latitude          -0.029204  0.010603 -0.002516 -0.028976 -0.001439   \n",
       "dropoff_longitude         0.014678 -0.022455  0.004612 -0.001130  0.004758   \n",
       "dropoff_latitude         -0.020677  0.013612 -0.001554 -0.021762 -0.000607   \n",
       "trip_duration             1.000000  0.003690  0.006625 -0.000708  0.006607   \n",
       "pu_hour                   0.003690  1.000000 -0.003340 -0.084255 -0.003438   \n",
       "yday                      0.006625 -0.003340  1.000000 -0.013615  0.985657   \n",
       "wday                     -0.000708 -0.084255 -0.013615  1.000000 -0.014544   \n",
       "month                     0.006607 -0.003438  0.985657 -0.014544  1.000000   \n",
       "distance                  0.094777 -0.015813  0.016443  0.007599  0.015668   \n",
       "average temperature       0.004574 -0.004948  0.837212 -0.019799  0.814295   \n",
       "\n",
       "                     distance  average temperature  \n",
       "vendor_id            0.008109            -0.005760  \n",
       "passenger_count      0.010306            -0.001203  \n",
       "pickup_longitude     0.259780             0.005547  \n",
       "pickup_latitude     -0.210354            -0.002011  \n",
       "dropoff_longitude    0.134033             0.005502  \n",
       "dropoff_latitude    -0.142128            -0.000997  \n",
       "trip_duration        0.094777             0.004574  \n",
       "pu_hour             -0.015813            -0.004948  \n",
       "yday                 0.016443             0.837212  \n",
       "wday                 0.007599            -0.019799  \n",
       "month                0.015668             0.814295  \n",
       "distance             1.000000             0.018631  \n",
       "average temperature  0.018631             1.000000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>2513.2</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>1779.4</td>\n",
       "      <td>235.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>1614.9</td>\n",
       "      <td>140.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0801584</td>\n",
       "      <td>1393.5</td>\n",
       "      <td>189.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  total_distance  total_travel_time\n",
       "0  id2875421          2009.1              164.9\n",
       "1  id2377394          2513.2              332.0\n",
       "2  id3504673          1779.4              235.8\n",
       "3  id2181028          1614.9              140.1\n",
       "4  id0801584          1393.5              189.4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastest1 = pd.read_csv('input/fastest_routes_train_part_1.csv')\n",
    "fastest2 = pd.read_csv('input/fastest_routes_train_part_2.csv')\n",
    "fastest = pd.concat([fastest1,fastest2],ignore_index=True)\n",
    "fastest = fastest.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street','end_street'],axis=1)\n",
    "fastest.head() #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>month</th>\n",
       "      <th>distance</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.930648</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.224526</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2782.9</td>\n",
       "      <td>244.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.563173</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3124.4</td>\n",
       "      <td>419.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.803944</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3327.8</td>\n",
       "      <td>280.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.382826</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3354.8</td>\n",
       "      <td>270.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag        ...         month  distance  \\\n",
       "0         40.765602                  N        ...             3  0.930648   \n",
       "1         40.765068                  N        ...             3  1.224526   \n",
       "2         40.722710                  N        ...             3  1.563173   \n",
       "3         40.709492                  N        ...             3  1.803944   \n",
       "4         40.761921                  N        ...             3  1.382826   \n",
       "\n",
       "   average temperature  precipitation  snow fall  snow depth  snowfall  \\\n",
       "0                 45.5           0.29        0.0           0       0.0   \n",
       "1                 45.5           0.29        0.0           0       0.0   \n",
       "2                 45.5           0.29        0.0           0       0.0   \n",
       "3                 45.5           0.29        0.0           0       0.0   \n",
       "4                 45.5           0.29        0.0           0       0.0   \n",
       "\n",
       "   snowdepth total_distance total_travel_time  \n",
       "0        0.0         2009.1             164.9  \n",
       "1        0.0         2782.9             244.6  \n",
       "2        0.0         3124.4             419.9  \n",
       "3        0.0         3327.8             280.8  \n",
       "4        0.0         3354.8             270.3  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df,fastest,on='id',how='outer')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask = ((df.trip_duration > 60) & (df.distance < 0.05))\n",
    "df = df[~mask]\n",
    "mask = (df.trip_duration < 60) \n",
    "df = df[~mask]\n",
    "mask =  df.trip_duration > 79200\n",
    "df = df[~mask]\n",
    "mask = df.distance/(df.trip_duration/3600) > 60\n",
    "df = df[~mask]\n",
    "df.trip_duration = df.trip_duration.astype(np.uint16)\n",
    "df = df[df.passenger_count > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = df.groupby(['wday','vendor_id'])[['trip_duration']].apply(np.median)\n",
    "m.name = 'trip_duration_median'\n",
    "df = df.join(m, on=['wday','vendor_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b5fd5c12588>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W2LVrl6SdeXl51erg0dHRWLt2LVJTU2FqagpfX1+MGTNG2f/DDz9g69atuHnz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\niDk/+lVlFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5fbf37a400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y='trip_duration_median', x='wday',data=df, fit_reg=False, hue='vendor_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = df.groupby(['pu_hour','vendor_id'])[['trip_duration']].apply(np.median)\n",
    "m.name ='trip_duration_median_hour'\n",
    "df = df.join(m, on=['pu_hour','vendor_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b9d5c30c630>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FPixdumSut81pcnJ5z8eo60VvvKxt+5pzN3LFO45d0J/Vz/NcTJ7naBn184yIG4D/lZl3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\ntv8PZPs9W1bNQmEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b9d5c30cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(y='trip_duration_median_hour', x='pu_hour',data=df, fit_reg=False, hue='vendor_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jfk_lon = -73.778889\n",
    "jfk_lat = 40.639722\n",
    "lga_lon = -73.872611\n",
    "lga_lat = 40.77725"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['jfk_pickup_dist'] = df.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "df['lga_pickup_dist'] = df.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "df['jfk_dropoff_dist'] = df.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n",
    "df['lga_dropoff_dist'] = df.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b48ab563da0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Teh3Ae0wMC2NBs6qKL8aWblFrRGJnZ6cycADg5s2bAABbW1ulbaRSKXJyclBW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\niEiU/wNdEPHuEEiyqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b48aa771ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=2, sharex=True)\n",
    "ax[0,0].set_xlim(0,50)\n",
    "\n",
    "sns.distplot(df['jfk_pickup_dist'],ax=ax[0,0],bins=100,kde=False,hist_kws={'log':True})\n",
    "sns.distplot(df['jfk_dropoff_dist'],ax=ax[0,1],bins=100,kde=False,hist_kws={'log':True})\n",
    "sns.distplot(df['lga_pickup_dist'],ax=ax[1,0],bins=100,kde=False,hist_kws={'log':True})\n",
    "sns.distplot(df['lga_dropoff_dist'],ax=ax[1,1],bins=100,kde=False,hist_kws={'log':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>trip_duration_median</th>\n",
       "      <th>trip_duration_median_hour</th>\n",
       "      <th>jfk</th>\n",
       "      <th>lga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "      <td>634.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2782.9</td>\n",
       "      <td>244.6</td>\n",
       "      <td>633.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3124.4</td>\n",
       "      <td>419.9</td>\n",
       "      <td>633.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3327.8</td>\n",
       "      <td>280.8</td>\n",
       "      <td>634.0</td>\n",
       "      <td>633.5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3354.8</td>\n",
       "      <td>270.3</td>\n",
       "      <td>634.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  ...    snow fall  snow depth  \\\n",
       "0         40.765602                  N  ...          0.0           0   \n",
       "1         40.765068                  N  ...          0.0           0   \n",
       "2         40.722710                  N  ...          0.0           0   \n",
       "3         40.709492                  N  ...          0.0           0   \n",
       "4         40.761921                  N  ...          0.0           0   \n",
       "\n",
       "   snowfall  snowdepth  total_distance  total_travel_time  \\\n",
       "0       0.0        0.0          2009.1              164.9   \n",
       "1       0.0        0.0          2782.9              244.6   \n",
       "2       0.0        0.0          3124.4              419.9   \n",
       "3       0.0        0.0          3327.8              280.8   \n",
       "4       0.0        0.0          3354.8              270.3   \n",
       "\n",
       "   trip_duration_median  trip_duration_median_hour    jfk    lga  \n",
       "0                 634.0                      709.0  False  False  \n",
       "1                 633.0                      727.0  False  False  \n",
       "2                 633.0                      724.0  False  False  \n",
       "3                 634.0                      633.5  False  False  \n",
       "4                 634.0                      735.0  False  False  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['jfk'] = ((df['jfk_pickup_dist'] < 2) | (df['jfk_dropoff_dist'] < 2))\n",
    "df['lga'] = ((df['lga_pickup_dist'] < 2) | (df['lga_dropoff_dist'] < 2))\n",
    "df = df.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>...</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>total_distance</th>\n",
       "      <th>total_travel_time</th>\n",
       "      <th>trip_duration_median</th>\n",
       "      <th>trip_duration_median_hour</th>\n",
       "      <th>jfk</th>\n",
       "      <th>lga</th>\n",
       "      <th>workday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.1</td>\n",
       "      <td>164.9</td>\n",
       "      <td>634.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2782.9</td>\n",
       "      <td>244.6</td>\n",
       "      <td>633.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3124.4</td>\n",
       "      <td>419.9</td>\n",
       "      <td>633.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3327.8</td>\n",
       "      <td>280.8</td>\n",
       "      <td>634.0</td>\n",
       "      <td>633.5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3354.8</td>\n",
       "      <td>270.3</td>\n",
       "      <td>634.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime    dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55 2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39 2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38 2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36 2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56 2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag   ...    snow depth  snowfall  \\\n",
       "0         40.765602                  N   ...             0       0.0   \n",
       "1         40.765068                  N   ...             0       0.0   \n",
       "2         40.722710                  N   ...             0       0.0   \n",
       "3         40.709492                  N   ...             0       0.0   \n",
       "4         40.761921                  N   ...             0       0.0   \n",
       "\n",
       "   snowdepth  total_distance  total_travel_time  trip_duration_median  \\\n",
       "0        0.0          2009.1              164.9                 634.0   \n",
       "1        0.0          2782.9              244.6                 633.0   \n",
       "2        0.0          3124.4              419.9                 633.0   \n",
       "3        0.0          3327.8              280.8                 634.0   \n",
       "4        0.0          3354.8              270.3                 634.0   \n",
       "\n",
       "   trip_duration_median_hour    jfk    lga workday  \n",
       "0                      709.0  False  False    True  \n",
       "1                      727.0  False  False    True  \n",
       "2                      724.0  False  False    True  \n",
       "3                      633.5  False  False   False  \n",
       "4                      735.0  False  False    True  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workday'] = ((df['pu_hour'] > 8) & (df['pu_hour'] < 18))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f396143a-380f-4d90-8a25-99b5a2d91454",
    "_execution_state": "idle",
    "_uuid": "53eb8baab05d86229fbaa11c5947f6ec085f9726",
    "collapsed": false
   },
   "source": [
    "## Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "6067f4ea-649d-438b-8de3-deab1a8438c1",
    "_execution_state": "idle",
    "_uuid": "f700181673094d1b8e3cbce2f285ccc2c5000be7",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ab7ade02cc0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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S29+n0RpwwdzsUSC0nzHv7ILLzqp0e7NCq+0+SI7vCRF1FQZ5IgpIUgjVGy04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6AAAg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\niUAo5AkhhBBCCIlA/j+Z8m9UjdDzhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ab7b49c6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1, nrows=1,figsize=(12,10))\n",
    "plt.ylim(40.6, 40.9)\n",
    "plt.xlim(-74.1,-73.7)\n",
    "ax.scatter(df['pickup_longitude'],df['pickup_latitude'], s=0.01, alpha=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMSLE: Evaluation Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def rmsle(evaluator,X,real):\n",
    "    sum = 0.0\n",
    "    predicted = evaluator.predict(X)\n",
    "    print(\"Number predicted less than 0: {}\".format(np.where(predicted < 0)[0].shape))\n",
    "\n",
    "    predicted[predicted < 0] = 0\n",
    "    for x in range(len(predicted)):\n",
    "        p = np.log(predicted[x]+1)\n",
    "        r = np.log(real[x]+1)\n",
    "        sum = sum + (p-r)**2\n",
    "    return (sum/len(predicted))**0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "f36d65a0-9179-4e0e-b893-fad641510ca5",
    "_execution_state": "idle",
    "_uuid": "ff6a80de90c26ecb63b02f197f72aed5f1fc4414",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tdf = pd.read_csv('input/test.csv')\n",
    "tdf.pickup_datetime=pd.to_datetime(tdf.pickup_datetime)\n",
    "#tdf.dropoff_datetime=pd.to_datetime(tdf.dropoff_datetime)\n",
    "tdf['pu_hour'] = tdf.pickup_datetime.dt.hour\n",
    "tdf['yday'] = tdf.pickup_datetime.dt.dayofyear\n",
    "tdf['wday'] = tdf.pickup_datetime.dt.dayofweek\n",
    "tdf['month'] = tdf.pickup_datetime.dt.month\n",
    "tdf['distance'] = haversine(tdf.pickup_longitude, tdf.pickup_latitude,\n",
    "                                           tdf.dropoff_longitude, tdf.dropoff_latitude)\n",
    "fastest_test = pd.read_csv('input/fastest_routes_test.csv')\n",
    "tdf = pd.merge(tdf,fastest_test,on='id',how='outer')\n",
    "tdf = tdf.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street','end_street'],axis=1)\n",
    "tdf = pd.merge(tdf,wdf,on='yday')\n",
    "tdf = tdf.drop(['date','maximum temerature','minimum temperature'],axis=1)\n",
    "tdf['jfk_pickup_dist'] = tdf.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "tdf['lga_pickup_dist'] = tdf.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n",
    "tdf['jfk_dropoff_dist'] = tdf.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n",
    "tdf['lga_dropoff_dist'] = tdf.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n",
    "tdf['jfk'] = ((tdf['jfk_pickup_dist'] < 2) | (tdf['jfk_dropoff_dist'] < 2))\n",
    "tdf['lga'] = ((tdf['lga_pickup_dist'] < 2) | (tdf['lga_dropoff_dist'] < 2))\n",
    "tdf = tdf.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n",
    "tdf['workday'] = ((tdf['pu_hour'] > 8) & (tdf['pu_hour'] < 18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tdf['snowfall'] = tdf['snow fall'].replace(['T'],0.05)\n",
    "tdf['precipitation'] = tdf['precipitation'].replace(['T'],0.05)\n",
    "tdf['snowdepth'] = tdf['snow depth'].replace(['T'],0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>pu_hour</th>\n",
       "      <th>...</th>\n",
       "      <th>step_location_list</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "      <th>snowfall</th>\n",
       "      <th>snowdepth</th>\n",
       "      <th>jfk</th>\n",
       "      <th>lga</th>\n",
       "      <th>workday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id3004672</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:58</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.988129</td>\n",
       "      <td>40.732029</td>\n",
       "      <td>-73.990173</td>\n",
       "      <td>40.756680</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.988109,40.732021|-73.976697,40.747615|-73....</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3505355</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:53</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.964203</td>\n",
       "      <td>40.679993</td>\n",
       "      <td>-73.959808</td>\n",
       "      <td>40.655403</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.96419,40.679994|-73.960956,40.663285|-73.9...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id1217141</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:47</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.997437</td>\n",
       "      <td>40.737583</td>\n",
       "      <td>-73.986160</td>\n",
       "      <td>40.729523</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.997421,40.737604|-73.990752,40.734816|-73....</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id2150126</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-30 23:59:41</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.956070</td>\n",
       "      <td>40.771900</td>\n",
       "      <td>-73.986427</td>\n",
       "      <td>40.730469</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.956096,40.771911</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id1598245</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-30 23:59:33</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.970215</td>\n",
       "      <td>40.761475</td>\n",
       "      <td>-73.961510</td>\n",
       "      <td>40.755890</td>\n",
       "      <td>N</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.970262,40.761412|-73.960671,40.757364|-73....</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime  passenger_count  \\\n",
       "0  id3004672          1  2016-06-30 23:59:58                1   \n",
       "1  id3505355          1  2016-06-30 23:59:53                1   \n",
       "2  id1217141          1  2016-06-30 23:59:47                1   \n",
       "3  id2150126          2  2016-06-30 23:59:41                1   \n",
       "4  id1598245          1  2016-06-30 23:59:33                1   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.988129        40.732029         -73.990173         40.756680   \n",
       "1        -73.964203        40.679993         -73.959808         40.655403   \n",
       "2        -73.997437        40.737583         -73.986160         40.729523   \n",
       "3        -73.956070        40.771900         -73.986427         40.730469   \n",
       "4        -73.970215        40.761475         -73.961510         40.755890   \n",
       "\n",
       "  store_and_fwd_flag  pu_hour   ...    \\\n",
       "0                  N       23   ...     \n",
       "1                  N       23   ...     \n",
       "2                  N       23   ...     \n",
       "3                  N       23   ...     \n",
       "4                  N       23   ...     \n",
       "\n",
       "                                  step_location_list  average temperature  \\\n",
       "0  -73.988109,40.732021|-73.976697,40.747615|-73....                 76.0   \n",
       "1  -73.96419,40.679994|-73.960956,40.663285|-73.9...                 76.0   \n",
       "2  -73.997421,40.737604|-73.990752,40.734816|-73....                 76.0   \n",
       "3                               -73.956096,40.771911                 76.0   \n",
       "4  -73.970262,40.761412|-73.960671,40.757364|-73....                 76.0   \n",
       "\n",
       "   precipitation  snow fall snow depth snowfall  snowdepth    jfk    lga  \\\n",
       "0            0.0          0          0        0          0  False  False   \n",
       "1            0.0          0          0        0          0  False  False   \n",
       "2            0.0          0          0        0          0  False  False   \n",
       "3            0.0          0          0        0          0  False  False   \n",
       "4            0.0          0          0        0          0  False  False   \n",
       "\n",
       "  workday  \n",
       "0   False  \n",
       "1   False  \n",
       "2   False  \n",
       "3   False  \n",
       "4   False  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_csv('train_data.csv',index=False)\n",
    "tdf.to_csv('test_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home12/rebecca.krall/.conda/envs/py3xg/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/n/home12/rebecca.krall/.conda/envs/py3xg/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (27,28) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('train_data.csv')\n",
    "tdf = pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>id</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id3004672</td>\n",
       "      <td>424.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id3505355</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id1217141</td>\n",
       "      <td>193.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id1598245</td>\n",
       "      <td>103.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id0898117</td>\n",
       "      <td>489.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  trip_duration\n",
       "0  id3004672          424.6\n",
       "1  id3505355          200.0\n",
       "2  id1217141          193.2\n",
       "3  id1598245          103.2\n",
       "4  id0898117          489.2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark = fastest_test[['id','total_travel_time']]\n",
    "benchmark = benchmark.rename(index=str, columns={\"total_travel_time\": \"trip_duration\"})\n",
    "benchmark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "benchmark['trip_duration'].isnull().values.any()\n",
    "benchmark.to_csv('benchmark.csv',index=False)\n",
    "#RMSLE=0.990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "da4346dd-73a7-4412-8222-3fc122e4020c",
    "_execution_state": "idle",
    "_uuid": "39ebf018303a0b31cea169a5bc184d9290ea249a",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = df[['vendor_id','passenger_count','pickup_latitude','pickup_longitude','dropoff_latitude','dropoff_longitude','pu_hour','wday','month','workday','precipitation','snowfall','snowdepth','total_distance','total_travel_time','jfk','lga']]\n",
    "target = df['trip_duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "58acd8d6-8b32-43ba-bf48-7e16d5c52244",
    "_execution_state": "idle",
    "_uuid": "1035773aa16ec621683056b10197c4ce36bf500a",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tfeatures = tdf[['vendor_id','passenger_count','pickup_latitude','pickup_longitude','dropoff_latitude','dropoff_longitude','pu_hour','wday','month','workday','precipitation','snowfall','snowdepth','total_distance','total_travel_time','jfk','lga']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b0bbeea7-80a5-450b-864e-6f540293b8e2",
    "_execution_state": "idle",
    "_uuid": "790a8b8ce345eb618bbef88fe8fec940abf8c75c",
    "collapsed": false
   },
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_cell_guid": "3efc1ba1-55cb-4039-b8d3-991e43f6c22e",
    "_execution_state": "idle",
    "_uuid": "1785c513750bbef82f98065c4dc9b4deeee2dd5f",
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (14,)\n",
      "Number predicted less than 0: (9,)\n",
      "Number predicted less than 0: (17,)\n",
      "Number predicted less than 0: (12,)\n",
      "[ 0.43999617  0.44022755  0.43897449  0.44073678]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "reg = linear_model.LinearRegression()\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(reg, features, np.ravel(target), cv=cv, scoring=rmsle))\n",
    "reg.fit(features, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43998374750000002"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.43999617,  0.44022755 , 0.43897449,0.44073678])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(625134, 17)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfeatures.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(490,)\n"
     ]
    }
   ],
   "source": [
    "pred = reg.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)\n",
    "pred[pred < 0]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "1baff0f1-e3cf-40b4-af2d-2a3856dfbc81",
    "_execution_state": "idle",
    "_uuid": "70381e4a5b6a713d2dc671ce72686149210d790f",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "0721f21f-2888-4ab3-b3bd-f54968156bdb",
    "_execution_state": "idle",
    "_uuid": "74f10d64578769e0eddbe133fb3ca32f6f26e28b",
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out['trip_duration'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "31941b96-6946-47d6-8b72-28842f417e30",
    "_execution_state": "idle",
    "_uuid": "3ca4d926dc29d9767614e8e05dd313379ae5bdb5",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "out.to_csv('pred_linear.csv',index=False)\n",
    "#RMSLE=0.535"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-nearest Neighbors Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "55f8b4ca-315d-402d-a85b-f18d1f521c54",
    "_execution_state": "idle",
    "_uuid": "6db76ebe986844f2484ab081ece300d4b0f8fa8f",
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "[ 0.42010954  0.41940803  0.41947931  0.41968818]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "neigh = KNeighborsRegressor(n_neighbors=10)\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(neigh, features, np.ravel(target), cv=cv,scoring=rmsle))\n",
    "neigh.fit(features,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41967126500000002"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.42010954, 0.41940803, 0.41947931, 0.41968818])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,)\n"
     ]
    }
   ],
   "source": [
    "pred = neigh.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "04e4bf99-8113-42f5-ba0d-a419e2842afe",
    "_execution_state": "idle",
    "_uuid": "849b36bc0db626910c1ad2b4657e6cb944ddc748",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]\n",
    "out.to_csv('pred_knn.csv',index=False)\n",
    "#RMSLE=0.505"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "[ 0.35085909  0.35181223  0.34976928  0.35057147]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(rf, features, np.ravel(target), cv=cv,scoring=rmsle))\n",
    "rf = rf.fit(features,np.ravel(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35075301749999999"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([0.35085909, 0.35181223, 0.34976928, 0.35057147])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,)\n"
     ]
    }
   ],
   "source": [
    "pred = rf.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]\n",
    "out.to_csv('pred_rf.csv',index=False)\n",
    "#RMSLE=0.473"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "6e34d8d0-402d-4115-b00a-4d56414d2085",
    "_execution_state": "idle",
    "_uuid": "12c7db13fe177d3573b0b3ec0c260738ecc56f4c",
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home12/rebecca.krall/.conda/envs/py3xg/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number predicted less than 0: (1,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "Number predicted less than 0: (0,)\n",
      "[ 0.34347654  0.34348951  0.34196095  0.34286041]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,\n",
       "       learning_rate=0.08, max_delta_step=0, max_depth=7,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='reg:linear', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=0.75)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = xgboost.XGBRegressor(n_estimators=100, learning_rate=0.08, gamma=0, subsample=0.75,\n",
    "                           colsample_bytree=1, max_depth=7)\n",
    "\n",
    "cv = ShuffleSplit(n_splits=4, test_size=0.1, random_state=0)\n",
    "print(cross_val_score(reg, features, np.ravel(target), cv=cv,scoring=rmsle))\n",
    "reg.fit(features,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1,)\n"
     ]
    }
   ],
   "source": [
    "pred = reg.predict(tfeatures)\n",
    "print(np.where(pred < 0)[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "ac0340eb-fdfd-4998-a698-a8f440b1e632",
    "_execution_state": "idle",
    "_uuid": "723ceeca2a5f05925711eda23e672d38a01b1d8f",
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pred[pred < 0] = 0\n",
    "tdf['trip_duration']=pred.astype(int)\n",
    "out = tdf[['id','trip_duration']]\n",
    "out['trip_duration'].isnull().values.any()\n",
    "out.to_csv('pred_xgboost.csv',index=False)\n",
    "#RMSLE=0.463"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost import plot_tree\n",
    "plot_tree(reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(reg, open('xgb_model.sav','wb'),protocol=2)"
   ]
  }
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