ETAModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "import datetime\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "import seaborn as sns\n",
    "from pykalman import KalmanFilter\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import plotly \n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "plotly.tools.set_credentials_file(username='mainakchain', api_key='rBcOCFilf51mhco71qaQ')\n",
    "# from math import pi\n",
    "# from bokeh.plotting import figure, show, output_notebook\n",
    "# from nsepy.archives import get_price_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('SUPER_FINAL_DF.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toll_both</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>loading_out_time</th>\n",
       "      <th>ist_timestamp</th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>time_of_travel</th>\n",
       "      <th>day_of_travel</th>\n",
       "      <th>month_of_travel</th>\n",
       "      <th>days_from_now</th>\n",
       "      <th>delivery</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>delay</th>\n",
       "      <th>time_btn_tolls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sergrarh</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-09 13:57:40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>85.678333</td>\n",
       "      <td>49.392083</td>\n",
       "      <td>85.678333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Manguli</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-09 20:35:40</td>\n",
       "      <td>146.47</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>92.311667</td>\n",
       "      <td>49.692778</td>\n",
       "      <td>6.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gudipada (Old Gangapada)</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-09 22:47:12</td>\n",
       "      <td>55.83</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>94.503889</td>\n",
       "      <td>50.178056</td>\n",
       "      <td>2.192222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bellupada</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-10 06:52:59</td>\n",
       "      <td>171.77</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>102.600278</td>\n",
       "      <td>47.963889</td>\n",
       "      <td>8.096389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madapam</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-10 13:30:33</td>\n",
       "      <td>114.17</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>109.226389</td>\n",
       "      <td>50.314722</td>\n",
       "      <td>6.626111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  toll_both  vehicle_no  loading_out_time  \\\n",
       "0                  Sergrarh  JH01AE6027                 0   \n",
       "1                   Manguli  JH01AE6027                 0   \n",
       "2  Gudipada (Old Gangapada)  JH01AE6027                 0   \n",
       "3                 Bellupada  JH01AE6027                 0   \n",
       "4                   Madapam  JH01AE6027                 0   \n",
       "\n",
       "         ist_timestamp  distance_travelled  time_of_travel  day_of_travel  \\\n",
       "0  2017-11-09 13:57:40                0.00              13              2   \n",
       "1  2017-11-09 20:35:40              146.47              20              2   \n",
       "2  2017-11-09 22:47:12               55.83              22              2   \n",
       "3  2017-11-10 06:52:59              171.77               6              2   \n",
       "4  2017-11-10 13:30:33              114.17              13              2   \n",
       "\n",
       "   month_of_travel  days_from_now  delivery  time_taken      delay  \\\n",
       "0               11             42         1   85.678333  49.392083   \n",
       "1               11             42         1   92.311667  49.692778   \n",
       "2               11             42         1   94.503889  50.178056   \n",
       "3               11             42         1  102.600278  47.963889   \n",
       "4               11             42         1  109.226389  50.314722   \n",
       "\n",
       "   time_btn_tolls  \n",
       "0       85.678333  \n",
       "1        6.633333  \n",
       "2        2.192222  \n",
       "3        8.096389  \n",
       "4        6.626111  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_toll_num(df):\n",
    "    toll_dict = {}\n",
    "    new_toll_dict = {}\n",
    "    x = {}\n",
    "    num = 1\n",
    "    for toll in df.toll_both.unique().tolist():\n",
    "        t = df['time_taken'][df.toll_both == toll].median()\n",
    "        toll_dict[toll] = t\n",
    "    \n",
    "    for key, value in sorted(toll_dict.iteritems(), key=lambda (k,v): (v,k)):\n",
    "        x[key] = value\n",
    "        new_toll_dict[key] = num\n",
    "        num+=1\n",
    "        \n",
    "    return new_toll_dict, x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_dict,x = find_toll_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bellupada': 6,\n",
       " 'Bolapalli': 16,\n",
       " 'Budanam': 19,\n",
       " 'Chilakapalem': 8,\n",
       " 'Gaddurur': 21,\n",
       " 'Gudipada (Old Gangapada)': 4,\n",
       " 'Gurapalli': 5,\n",
       " 'Kalaparru': 14,\n",
       " 'Kaza': 15,\n",
       " 'Krishnavaram': 12,\n",
       " 'Laxmipuram': 9,\n",
       " 'Madapam': 7,\n",
       " 'Manguli': 3,\n",
       " 'Mulbagal': 20,\n",
       " 'Nathavalasa/ Vizianagaram': 10,\n",
       " 'Panikholi': 2,\n",
       " 'Pottipadu': 13,\n",
       " 'Sergrarh': 1,\n",
       " 'Sunambatti (Musunur)': 18,\n",
       " 'Tangutur': 17,\n",
       " 'Vempadu': 11}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_toll_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['toll_both'] = df['toll_both'].replace(new_toll_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "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>toll_both</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>loading_out_time</th>\n",
       "      <th>ist_timestamp</th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>time_of_travel</th>\n",
       "      <th>day_of_travel</th>\n",
       "      <th>month_of_travel</th>\n",
       "      <th>days_from_now</th>\n",
       "      <th>delivery</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>delay</th>\n",
       "      <th>time_btn_tolls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-09 13:57:40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>85.678333</td>\n",
       "      <td>49.392083</td>\n",
       "      <td>85.678333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-09 20:35:40</td>\n",
       "      <td>146.47</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>92.311667</td>\n",
       "      <td>49.692778</td>\n",
       "      <td>6.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-09 22:47:12</td>\n",
       "      <td>55.83</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>94.503889</td>\n",
       "      <td>50.178056</td>\n",
       "      <td>2.192222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-10 06:52:59</td>\n",
       "      <td>171.77</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>102.600278</td>\n",
       "      <td>47.963889</td>\n",
       "      <td>8.096389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-10 13:30:33</td>\n",
       "      <td>114.17</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>109.226389</td>\n",
       "      <td>50.314722</td>\n",
       "      <td>6.626111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-10 14:09:56</td>\n",
       "      <td>27.46</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>109.882778</td>\n",
       "      <td>49.641944</td>\n",
       "      <td>0.656389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-10 19:49:56</td>\n",
       "      <td>38.72</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>115.549444</td>\n",
       "      <td>53.543611</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-11 11:01:14</td>\n",
       "      <td>67.28</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>130.737778</td>\n",
       "      <td>61.925556</td>\n",
       "      <td>12.783056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-11 16:18:20</td>\n",
       "      <td>69.42</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>136.022778</td>\n",
       "      <td>63.340278</td>\n",
       "      <td>5.285000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-11 23:39:01</td>\n",
       "      <td>209.57</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>143.367500</td>\n",
       "      <td>62.708889</td>\n",
       "      <td>7.344722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-12 01:59:18</td>\n",
       "      <td>82.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>145.705556</td>\n",
       "      <td>60.510000</td>\n",
       "      <td>2.338056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-12 03:44:21</td>\n",
       "      <td>63.96</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>147.456389</td>\n",
       "      <td>59.966250</td>\n",
       "      <td>1.750833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-12 10:09:19</td>\n",
       "      <td>62.31</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>153.872500</td>\n",
       "      <td>64.157639</td>\n",
       "      <td>6.416111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-12 12:21:02</td>\n",
       "      <td>97.26</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>156.067778</td>\n",
       "      <td>61.315972</td>\n",
       "      <td>2.195278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-12 21:35:57</td>\n",
       "      <td>215.25</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>165.316389</td>\n",
       "      <td>53.598889</td>\n",
       "      <td>9.248611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>0</td>\n",
       "      <td>2017-11-12 22:34:09</td>\n",
       "      <td>30.89</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>166.286389</td>\n",
       "      <td>54.965000</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-30 18:33:56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>41.146389</td>\n",
       "      <td>4.860139</td>\n",
       "      <td>41.146389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-30 21:00:06</td>\n",
       "      <td>92.99</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>43.582500</td>\n",
       "      <td>2.538333</td>\n",
       "      <td>2.436111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-11-30 22:18:16</td>\n",
       "      <td>54.27</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>44.885278</td>\n",
       "      <td>2.266389</td>\n",
       "      <td>1.302778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-01 03:06:21</td>\n",
       "      <td>77.86</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>49.686667</td>\n",
       "      <td>1.934444</td>\n",
       "      <td>4.801389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-01 06:22:44</td>\n",
       "      <td>84.43</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>52.959722</td>\n",
       "      <td>-1.676667</td>\n",
       "      <td>3.273056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-02 01:01:57</td>\n",
       "      <td>144.56</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>71.613333</td>\n",
       "      <td>-7.751528</td>\n",
       "      <td>18.653611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-02 01:35:57</td>\n",
       "      <td>21.60</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>72.180000</td>\n",
       "      <td>-6.196111</td>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-02 02:47:07</td>\n",
       "      <td>45.56</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>73.366111</td>\n",
       "      <td>-7.292500</td>\n",
       "      <td>1.186111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-10-12 18:24:07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>8.726111</td>\n",
       "      <td>-27.560139</td>\n",
       "      <td>8.726111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-10-12 21:09:18</td>\n",
       "      <td>93.80</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>11.479167</td>\n",
       "      <td>-29.565000</td>\n",
       "      <td>2.753056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-10-12 23:03:29</td>\n",
       "      <td>53.36</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>13.382222</td>\n",
       "      <td>-29.236667</td>\n",
       "      <td>1.903056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-10-13 00:56:40</td>\n",
       "      <td>56.74</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>15.268611</td>\n",
       "      <td>-29.057222</td>\n",
       "      <td>1.886389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-10-13 03:35:01</td>\n",
       "      <td>87.69</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>17.907778</td>\n",
       "      <td>-29.844444</td>\n",
       "      <td>2.639167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6</td>\n",
       "      <td>JH01AE6027</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-10-13 08:19:08</td>\n",
       "      <td>83.42</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>22.643056</td>\n",
       "      <td>-31.993333</td>\n",
       "      <td>4.735278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>8</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-04 18:15:44</td>\n",
       "      <td>27.06</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>62.001389</td>\n",
       "      <td>1.760556</td>\n",
       "      <td>0.789444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>9</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-04 19:16:21</td>\n",
       "      <td>39.70</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>63.011667</td>\n",
       "      <td>1.399722</td>\n",
       "      <td>1.010278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>14</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-05 12:13:08</td>\n",
       "      <td>170.34</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>79.958056</td>\n",
       "      <td>0.593194</td>\n",
       "      <td>16.946389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>13</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-05 13:11:08</td>\n",
       "      <td>21.93</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>80.924722</td>\n",
       "      <td>2.548611</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3360</th>\n",
       "      <td>15</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-05 14:21:34</td>\n",
       "      <td>44.66</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>82.098611</td>\n",
       "      <td>1.440000</td>\n",
       "      <td>1.173889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>16</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-05 17:34:53</td>\n",
       "      <td>82.67</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>85.320556</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>3.221944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3362</th>\n",
       "      <td>17</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-05 19:13:13</td>\n",
       "      <td>63.96</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>86.959444</td>\n",
       "      <td>-0.530694</td>\n",
       "      <td>1.638889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3363</th>\n",
       "      <td>19</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-06 07:14:35</td>\n",
       "      <td>110.44</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>98.982222</td>\n",
       "      <td>4.230417</td>\n",
       "      <td>12.022778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3364</th>\n",
       "      <td>21</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-06 17:46:41</td>\n",
       "      <td>219.14</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>109.517222</td>\n",
       "      <td>-2.200278</td>\n",
       "      <td>10.535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3365</th>\n",
       "      <td>20</td>\n",
       "      <td>CG04DM2391</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-11-06 18:51:23</td>\n",
       "      <td>30.14</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>110.595556</td>\n",
       "      <td>-0.725833</td>\n",
       "      <td>1.078333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3366</th>\n",
       "      <td>1</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-23 20:16:35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>18.782222</td>\n",
       "      <td>-17.504028</td>\n",
       "      <td>18.782222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3367</th>\n",
       "      <td>2</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-23 22:25:45</td>\n",
       "      <td>93.46</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>20.935000</td>\n",
       "      <td>-20.109167</td>\n",
       "      <td>2.152778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3368</th>\n",
       "      <td>3</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-24 00:24:35</td>\n",
       "      <td>48.22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>22.915556</td>\n",
       "      <td>-19.703333</td>\n",
       "      <td>1.980556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3369</th>\n",
       "      <td>6</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-24 06:29:23</td>\n",
       "      <td>81.15</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>28.995556</td>\n",
       "      <td>-25.640833</td>\n",
       "      <td>6.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3370</th>\n",
       "      <td>8</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-24 11:07:47</td>\n",
       "      <td>36.62</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>33.635556</td>\n",
       "      <td>-26.605278</td>\n",
       "      <td>4.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3371</th>\n",
       "      <td>9</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-24 12:28:38</td>\n",
       "      <td>39.06</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>34.983056</td>\n",
       "      <td>-26.628889</td>\n",
       "      <td>1.347500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3372</th>\n",
       "      <td>15</td>\n",
       "      <td>AP07TU1248</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-10-26 13:21:54</td>\n",
       "      <td>107.17</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>83.870833</td>\n",
       "      <td>3.212222</td>\n",
       "      <td>48.887778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3373</th>\n",
       "      <td>1</td>\n",
       "      <td>OR05AJ1911</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-10-05 20:12:21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>86.326667</td>\n",
       "      <td>50.040417</td>\n",
       "      <td>86.326667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3374</th>\n",
       "      <td>6</td>\n",
       "      <td>OR05AJ1911</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-10-06 18:18:54</td>\n",
       "      <td>160.22</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>108.435833</td>\n",
       "      <td>53.799444</td>\n",
       "      <td>22.109167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3375</th>\n",
       "      <td>7</td>\n",
       "      <td>OR05AJ1911</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-10-06 23:00:25</td>\n",
       "      <td>99.24</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>113.127778</td>\n",
       "      <td>54.216111</td>\n",
       "      <td>4.691944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>8</td>\n",
       "      <td>OR05AJ1911</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-10-06 23:40:51</td>\n",
       "      <td>10.07</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>113.801667</td>\n",
       "      <td>53.560833</td>\n",
       "      <td>0.673889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>9</td>\n",
       "      <td>OR05AJ1911</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-10-07 02:36:16</td>\n",
       "      <td>6.11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>116.725278</td>\n",
       "      <td>55.113333</td>\n",
       "      <td>2.923611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3378</th>\n",
       "      <td>6</td>\n",
       "      <td>JH05W7748</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-12-06 13:52:30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>82.114722</td>\n",
       "      <td>27.478333</td>\n",
       "      <td>82.114722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3379</th>\n",
       "      <td>12</td>\n",
       "      <td>JH05W7748</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-12-07 13:29:33</td>\n",
       "      <td>11.60</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>105.732222</td>\n",
       "      <td>33.049722</td>\n",
       "      <td>23.617500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3380</th>\n",
       "      <td>21</td>\n",
       "      <td>JH05W7748</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-12-08 14:41:08</td>\n",
       "      <td>41.43</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>130.925278</td>\n",
       "      <td>19.207778</td>\n",
       "      <td>25.193056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>20</td>\n",
       "      <td>JH05W7748</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-12-08 15:29:24</td>\n",
       "      <td>30.78</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>131.729722</td>\n",
       "      <td>20.408333</td>\n",
       "      <td>0.804444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>11</td>\n",
       "      <td>AP07TB1989</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-11-24 15:41:33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>63.783056</td>\n",
       "      <td>-5.029167</td>\n",
       "      <td>63.783056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>12</td>\n",
       "      <td>AP07TB1989</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-11-24 17:13:44</td>\n",
       "      <td>70.03</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>65.319444</td>\n",
       "      <td>-7.363056</td>\n",
       "      <td>1.536389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>14</td>\n",
       "      <td>AP07TB1989</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-11-25 00:21:29</td>\n",
       "      <td>146.31</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>72.448611</td>\n",
       "      <td>-6.916250</td>\n",
       "      <td>7.129167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>13</td>\n",
       "      <td>AP07TB1989</td>\n",
       "      <td>23</td>\n",
       "      <td>2017-11-25 00:50:29</td>\n",
       "      <td>21.61</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>72.931944</td>\n",
       "      <td>-5.444167</td>\n",
       "      <td>0.483333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3386 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      toll_both  vehicle_no  loading_out_time        ist_timestamp  \\\n",
       "0             1  JH01AE6027                 0  2017-11-09 13:57:40   \n",
       "1             3  JH01AE6027                 0  2017-11-09 20:35:40   \n",
       "2             4  JH01AE6027                 0  2017-11-09 22:47:12   \n",
       "3             6  JH01AE6027                 0  2017-11-10 06:52:59   \n",
       "4             7  JH01AE6027                 0  2017-11-10 13:30:33   \n",
       "5             8  JH01AE6027                 0  2017-11-10 14:09:56   \n",
       "6            10  JH01AE6027                 0  2017-11-10 19:49:56   \n",
       "7            11  JH01AE6027                 0  2017-11-11 11:01:14   \n",
       "8            12  JH01AE6027                 0  2017-11-11 16:18:20   \n",
       "9            15  JH01AE6027                 0  2017-11-11 23:39:01   \n",
       "10           16  JH01AE6027                 0  2017-11-12 01:59:18   \n",
       "11           17  JH01AE6027                 0  2017-11-12 03:44:21   \n",
       "12           18  JH01AE6027                 0  2017-11-12 10:09:19   \n",
       "13           19  JH01AE6027                 0  2017-11-12 12:21:02   \n",
       "14           21  JH01AE6027                 0  2017-11-12 21:35:57   \n",
       "15           20  JH01AE6027                 0  2017-11-12 22:34:09   \n",
       "16            1  JH01AE6027                 1  2017-11-30 18:33:56   \n",
       "17            2  JH01AE6027                 1  2017-11-30 21:00:06   \n",
       "18            3  JH01AE6027                 1  2017-11-30 22:18:16   \n",
       "19            5  JH01AE6027                 1  2017-12-01 03:06:21   \n",
       "20            6  JH01AE6027                 1  2017-12-01 06:22:44   \n",
       "21           14  JH01AE6027                 1  2017-12-02 01:01:57   \n",
       "22           13  JH01AE6027                 1  2017-12-02 01:35:57   \n",
       "23           15  JH01AE6027                 1  2017-12-02 02:47:07   \n",
       "24            1  JH01AE6027                 9  2017-10-12 18:24:07   \n",
       "25            2  JH01AE6027                 9  2017-10-12 21:09:18   \n",
       "26            3  JH01AE6027                 9  2017-10-12 23:03:29   \n",
       "27            4  JH01AE6027                 9  2017-10-13 00:56:40   \n",
       "28            5  JH01AE6027                 9  2017-10-13 03:35:01   \n",
       "29            6  JH01AE6027                 9  2017-10-13 08:19:08   \n",
       "...         ...         ...               ...                  ...   \n",
       "3356          8  CG04DM2391                 4  2017-11-04 18:15:44   \n",
       "3357          9  CG04DM2391                 4  2017-11-04 19:16:21   \n",
       "3358         14  CG04DM2391                 4  2017-11-05 12:13:08   \n",
       "3359         13  CG04DM2391                 4  2017-11-05 13:11:08   \n",
       "3360         15  CG04DM2391                 4  2017-11-05 14:21:34   \n",
       "3361         16  CG04DM2391                 4  2017-11-05 17:34:53   \n",
       "3362         17  CG04DM2391                 4  2017-11-05 19:13:13   \n",
       "3363         19  CG04DM2391                 4  2017-11-06 07:14:35   \n",
       "3364         21  CG04DM2391                 4  2017-11-06 17:46:41   \n",
       "3365         20  CG04DM2391                 4  2017-11-06 18:51:23   \n",
       "3366          1  AP07TU1248                 1  2017-10-23 20:16:35   \n",
       "3367          2  AP07TU1248                 1  2017-10-23 22:25:45   \n",
       "3368          3  AP07TU1248                 1  2017-10-24 00:24:35   \n",
       "3369          6  AP07TU1248                 1  2017-10-24 06:29:23   \n",
       "3370          8  AP07TU1248                 1  2017-10-24 11:07:47   \n",
       "3371          9  AP07TU1248                 1  2017-10-24 12:28:38   \n",
       "3372         15  AP07TU1248                 1  2017-10-26 13:21:54   \n",
       "3373          1  OR05AJ1911                 5  2017-10-05 20:12:21   \n",
       "3374          6  OR05AJ1911                 5  2017-10-06 18:18:54   \n",
       "3375          7  OR05AJ1911                 5  2017-10-06 23:00:25   \n",
       "3376          8  OR05AJ1911                 5  2017-10-06 23:40:51   \n",
       "3377          9  OR05AJ1911                 5  2017-10-07 02:36:16   \n",
       "3378          6   JH05W7748                 3  2017-12-06 13:52:30   \n",
       "3379         12   JH05W7748                 3  2017-12-07 13:29:33   \n",
       "3380         21   JH05W7748                 3  2017-12-08 14:41:08   \n",
       "3381         20   JH05W7748                 3  2017-12-08 15:29:24   \n",
       "3382         11  AP07TB1989                23  2017-11-24 15:41:33   \n",
       "3383         12  AP07TB1989                23  2017-11-24 17:13:44   \n",
       "3384         14  AP07TB1989                23  2017-11-25 00:21:29   \n",
       "3385         13  AP07TB1989                23  2017-11-25 00:50:29   \n",
       "\n",
       "      distance_travelled  time_of_travel  day_of_travel  month_of_travel  \\\n",
       "0                   0.00              13              2               11   \n",
       "1                 146.47              20              2               11   \n",
       "2                  55.83              22              2               11   \n",
       "3                 171.77               6              2               11   \n",
       "4                 114.17              13              2               11   \n",
       "5                  27.46              14              2               11   \n",
       "6                  38.72              19              2               11   \n",
       "7                  67.28              11              2               11   \n",
       "8                  69.42              16              2               11   \n",
       "9                 209.57              23              2               11   \n",
       "10                 82.18               1              2               11   \n",
       "11                 63.96               3              2               11   \n",
       "12                 62.31              10              2               11   \n",
       "13                 97.26              12              2               11   \n",
       "14                215.25              21              2               11   \n",
       "15                 30.89              22              2               11   \n",
       "16                  0.00              18              4               12   \n",
       "17                 92.99              21              4               12   \n",
       "18                 54.27              22              4               12   \n",
       "19                 77.86               3              4               12   \n",
       "20                 84.43               6              4               12   \n",
       "21                144.56               1              4               12   \n",
       "22                 21.60               1              4               12   \n",
       "23                 45.56               2              4               12   \n",
       "24                  0.00              18              2               10   \n",
       "25                 93.80              21              2               10   \n",
       "26                 53.36              23              2               10   \n",
       "27                 56.74               0              2               10   \n",
       "28                 87.69               3              2               10   \n",
       "29                 83.42               8              2               10   \n",
       "...                  ...             ...            ...              ...   \n",
       "3356               27.06              18              5               11   \n",
       "3357               39.70              19              5               11   \n",
       "3358              170.34              12              5               11   \n",
       "3359               21.93              13              5               11   \n",
       "3360               44.66              14              5               11   \n",
       "3361               82.67              17              5               11   \n",
       "3362               63.96              19              5               11   \n",
       "3363              110.44               7              5               11   \n",
       "3364              219.14              17              5               11   \n",
       "3365               30.14              18              5               11   \n",
       "3366                0.00              20              1               10   \n",
       "3367               93.46              22              1               10   \n",
       "3368               48.22               0              1               10   \n",
       "3369               81.15               6              1               10   \n",
       "3370               36.62              11              1               10   \n",
       "3371               39.06              12              1               10   \n",
       "3372              107.17              13              1               10   \n",
       "3373                0.00              20              4               10   \n",
       "3374              160.22              18              4               10   \n",
       "3375               99.24              23              4               10   \n",
       "3376               10.07              23              4               10   \n",
       "3377                6.11               2              4               10   \n",
       "3378                0.00              13              1               12   \n",
       "3379               11.60              13              1               12   \n",
       "3380               41.43              14              1               12   \n",
       "3381               30.78              15              1               12   \n",
       "3382                0.00              15              3               11   \n",
       "3383               70.03              17              3               11   \n",
       "3384              146.31               0              3               11   \n",
       "3385               21.61               0              3               11   \n",
       "\n",
       "      days_from_now  delivery  time_taken      delay  time_btn_tolls  \n",
       "0                42         1   85.678333  49.392083       85.678333  \n",
       "1                42         1   92.311667  49.692778        6.633333  \n",
       "2                42         1   94.503889  50.178056        2.192222  \n",
       "3                42         1  102.600278  47.963889        8.096389  \n",
       "4                42         1  109.226389  50.314722        6.626111  \n",
       "5                42         1  109.882778  49.641944        0.656389  \n",
       "6                42         1  115.549444  53.543611        5.666667  \n",
       "7                42         1  130.737778  61.925556       12.783056  \n",
       "8                42         1  136.022778  63.340278        5.285000  \n",
       "9                42         1  143.367500  62.708889        7.344722  \n",
       "10               42         1  145.705556  60.510000        2.338056  \n",
       "11               42         1  147.456389  59.966250        1.750833  \n",
       "12               42         1  153.872500  64.157639        6.416111  \n",
       "13               42         1  156.067778  61.315972        2.195278  \n",
       "14               42         1  165.316389  53.598889        9.248611  \n",
       "15               42         1  166.286389  54.965000        0.970000  \n",
       "16               18         1   41.146389   4.860139       41.146389  \n",
       "17               18         1   43.582500   2.538333        2.436111  \n",
       "18               18         1   44.885278   2.266389        1.302778  \n",
       "19               18         1   49.686667   1.934444        4.801389  \n",
       "20               18         1   52.959722  -1.676667        3.273056  \n",
       "21               18         1   71.613333  -7.751528       18.653611  \n",
       "22               18         1   72.180000  -6.196111        0.566667  \n",
       "23               18         1   73.366111  -7.292500        1.186111  \n",
       "24               69         1    8.726111 -27.560139        8.726111  \n",
       "25               69         1   11.479167 -29.565000        2.753056  \n",
       "26               69         1   13.382222 -29.236667        1.903056  \n",
       "27               69         1   15.268611 -29.057222        1.886389  \n",
       "28               69         1   17.907778 -29.844444        2.639167  \n",
       "29               69         1   22.643056 -31.993333        4.735278  \n",
       "...             ...       ...         ...        ...             ...  \n",
       "3356             45         1   62.001389   1.760556        0.789444  \n",
       "3357             45         1   63.011667   1.399722        1.010278  \n",
       "3358             45         1   79.958056   0.593194       16.946389  \n",
       "3359             45         1   80.924722   2.548611        0.966667  \n",
       "3360             45         1   82.098611   1.440000        1.173889  \n",
       "3361             45         1   85.320556   0.125000        3.221944  \n",
       "3362             45         1   86.959444  -0.530694        1.638889  \n",
       "3363             45         1   98.982222   4.230417       12.022778  \n",
       "3364             45         1  109.517222  -2.200278       10.535000  \n",
       "3365             45         1  110.595556  -0.725833        1.078333  \n",
       "3366             55         1   18.782222 -17.504028       18.782222  \n",
       "3367             55         1   20.935000 -20.109167        2.152778  \n",
       "3368             55         1   22.915556 -19.703333        1.980556  \n",
       "3369             55         1   28.995556 -25.640833        6.080000  \n",
       "3370             55         1   33.635556 -26.605278        4.640000  \n",
       "3371             55         1   34.983056 -26.628889        1.347500  \n",
       "3372             55         1   83.870833   3.212222       48.887778  \n",
       "3373             78         2   86.326667  50.040417       86.326667  \n",
       "3374             78         2  108.435833  53.799444       22.109167  \n",
       "3375             78         2  113.127778  54.216111        4.691944  \n",
       "3376             78         2  113.801667  53.560833        0.673889  \n",
       "3377             78         2  116.725278  55.113333        2.923611  \n",
       "3378             14         1   82.114722  27.478333       82.114722  \n",
       "3379             14         1  105.732222  33.049722       23.617500  \n",
       "3380             14         1  130.925278  19.207778       25.193056  \n",
       "3381             14         1  131.729722  20.408333        0.804444  \n",
       "3382             27         1   63.783056  -5.029167       63.783056  \n",
       "3383             27         1   65.319444  -7.363056        1.536389  \n",
       "3384             27         1   72.448611  -6.916250        7.129167  \n",
       "3385             27         1   72.931944  -5.444167        0.483333  \n",
       "\n",
       "[3386 rows x 13 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = df[(df['vehicle_no'].isin(['JH02M1289']) == False) | (df['loading_out_time'] != 2 )]\n",
    "test_df = df[(df['vehicle_no'].isin(['JH02M1289'])) & (df['loading_out_time'] == 2) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3386, 13), (3368, 13), (18, 13))"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape, train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_vehicle_no_to_dummy(df):\n",
    "    num = 1\n",
    "    dict_vehicle_no = {}\n",
    "    for truck in df['vehicle_no'].unique().tolist():\n",
    "        df['vehicle_no'] = df['vehicle_no'].replace({truck: num})\n",
    "        dict_vehicle_no[num] = truck\n",
    "        num += 1\n",
    "    return dict_vehicle_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "dict_vehicle_no_train_df = change_vehicle_no_to_dummy(train_df)\n",
    "rev_dict_vehicle_no_test_df = dict((v,k) for k,v in dict_vehicle_no_train_df.iteritems())\n",
    "test_df.vehicle_no = test_df.vehicle_no.replace(rev_dict_vehicle_no_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toll_both</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>loading_out_time</th>\n",
       "      <th>ist_timestamp</th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>time_of_travel</th>\n",
       "      <th>day_of_travel</th>\n",
       "      <th>month_of_travel</th>\n",
       "      <th>days_from_now</th>\n",
       "      <th>delivery</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>delay</th>\n",
       "      <th>time_btn_tolls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 14:23:21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>107.976667</td>\n",
       "      <td>71.690417</td>\n",
       "      <td>107.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 20:28:22</td>\n",
       "      <td>93.41</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>114.060278</td>\n",
       "      <td>73.016111</td>\n",
       "      <td>6.083611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 21:52:24</td>\n",
       "      <td>53.63</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>115.460833</td>\n",
       "      <td>72.841944</td>\n",
       "      <td>1.400556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 23:50:35</td>\n",
       "      <td>56.21</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>117.430556</td>\n",
       "      <td>73.104722</td>\n",
       "      <td>1.969722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-30 02:59:55</td>\n",
       "      <td>88.06</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>120.586111</td>\n",
       "      <td>72.833889</td>\n",
       "      <td>3.155556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-30 12:44:14</td>\n",
       "      <td>83.77</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>130.324722</td>\n",
       "      <td>75.688333</td>\n",
       "      <td>9.738611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 00:40:37</td>\n",
       "      <td>105.57</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>142.264444</td>\n",
       "      <td>80.652500</td>\n",
       "      <td>11.939722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 15:34:12</td>\n",
       "      <td>117.16</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>157.157500</td>\n",
       "      <td>88.345278</td>\n",
       "      <td>14.893056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 19:23:22</td>\n",
       "      <td>69.99</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>160.976944</td>\n",
       "      <td>88.294444</td>\n",
       "      <td>3.819444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 11:29:53</td>\n",
       "      <td>144.93</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>177.085556</td>\n",
       "      <td>97.720694</td>\n",
       "      <td>16.108611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 12:11:53</td>\n",
       "      <td>21.71</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>177.785556</td>\n",
       "      <td>99.409444</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 14:29:06</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>180.072500</td>\n",
       "      <td>99.413889</td>\n",
       "      <td>2.286944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 21:15:50</td>\n",
       "      <td>82.28</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>186.851389</td>\n",
       "      <td>101.655833</td>\n",
       "      <td>6.778889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 23:06:01</td>\n",
       "      <td>63.82</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>188.687778</td>\n",
       "      <td>101.197639</td>\n",
       "      <td>1.836389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 06:05:50</td>\n",
       "      <td>64.18</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>195.684722</td>\n",
       "      <td>105.969861</td>\n",
       "      <td>6.996944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 08:59:40</td>\n",
       "      <td>98.19</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>198.581944</td>\n",
       "      <td>103.830139</td>\n",
       "      <td>2.897222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 18:29:24</td>\n",
       "      <td>218.68</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>208.077500</td>\n",
       "      <td>96.360000</td>\n",
       "      <td>9.495556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 19:40:35</td>\n",
       "      <td>31.05</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>209.263889</td>\n",
       "      <td>97.942500</td>\n",
       "      <td>1.186389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      toll_both  vehicle_no  loading_out_time        ist_timestamp  \\\n",
       "1345          1          50                 2  2017-10-29 14:23:21   \n",
       "1346          2          50                 2  2017-10-29 20:28:22   \n",
       "1347          3          50                 2  2017-10-29 21:52:24   \n",
       "1348          4          50                 2  2017-10-29 23:50:35   \n",
       "1349          5          50                 2  2017-10-30 02:59:55   \n",
       "1350          6          50                 2  2017-10-30 12:44:14   \n",
       "1351          9          50                 2  2017-10-31 00:40:37   \n",
       "1352         11          50                 2  2017-10-31 15:34:12   \n",
       "1353         12          50                 2  2017-10-31 19:23:22   \n",
       "1354         14          50                 2  2017-11-01 11:29:53   \n",
       "1355         13          50                 2  2017-11-01 12:11:53   \n",
       "1356         15          50                 2  2017-11-01 14:29:06   \n",
       "1357         16          50                 2  2017-11-01 21:15:50   \n",
       "1358         17          50                 2  2017-11-01 23:06:01   \n",
       "1359         18          50                 2  2017-11-02 06:05:50   \n",
       "1360         19          50                 2  2017-11-02 08:59:40   \n",
       "1361         21          50                 2  2017-11-02 18:29:24   \n",
       "1362         20          50                 2  2017-11-02 19:40:35   \n",
       "\n",
       "      distance_travelled  time_of_travel  day_of_travel  month_of_travel  \\\n",
       "1345                0.00              14              5               10   \n",
       "1346               93.41              20              5               10   \n",
       "1347               53.63              21              5               10   \n",
       "1348               56.21              23              5               10   \n",
       "1349               88.06               2              5               10   \n",
       "1350               83.77              12              5               10   \n",
       "1351              105.57               0              5               10   \n",
       "1352              117.16              15              5               10   \n",
       "1353               69.99              19              5               10   \n",
       "1354              144.93              11              5               10   \n",
       "1355               21.71              12              5               10   \n",
       "1356               45.88              14              5               10   \n",
       "1357               82.28              21              5               10   \n",
       "1358               63.82              23              5               10   \n",
       "1359               64.18               6              5               10   \n",
       "1360               98.19               8              5               10   \n",
       "1361              218.68              18              5               10   \n",
       "1362               31.05              19              5               10   \n",
       "\n",
       "      days_from_now  delivery  time_taken       delay  time_btn_tolls  \n",
       "1345             53         1  107.976667   71.690417      107.976667  \n",
       "1346             53         1  114.060278   73.016111        6.083611  \n",
       "1347             53         1  115.460833   72.841944        1.400556  \n",
       "1348             53         1  117.430556   73.104722        1.969722  \n",
       "1349             53         1  120.586111   72.833889        3.155556  \n",
       "1350             53         1  130.324722   75.688333        9.738611  \n",
       "1351             53         1  142.264444   80.652500       11.939722  \n",
       "1352             53         1  157.157500   88.345278       14.893056  \n",
       "1353             53         1  160.976944   88.294444        3.819444  \n",
       "1354             53         1  177.085556   97.720694       16.108611  \n",
       "1355             53         1  177.785556   99.409444        0.700000  \n",
       "1356             53         1  180.072500   99.413889        2.286944  \n",
       "1357             53         1  186.851389  101.655833        6.778889  \n",
       "1358             53         1  188.687778  101.197639        1.836389  \n",
       "1359             53         1  195.684722  105.969861        6.996944  \n",
       "1360             53         1  198.581944  103.830139        2.897222  \n",
       "1361             53         1  208.077500   96.360000        9.495556  \n",
       "1362             53         1  209.263889   97.942500        1.186389  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_df['vehicle_no'] = test_df['vehicle_no'].replace({'JH02M1289':130})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toll_both</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>loading_out_time</th>\n",
       "      <th>ist_timestamp</th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>time_of_travel</th>\n",
       "      <th>day_of_travel</th>\n",
       "      <th>month_of_travel</th>\n",
       "      <th>days_from_now</th>\n",
       "      <th>delivery</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>delay</th>\n",
       "      <th>time_btn_tolls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 14:23:21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>107.976667</td>\n",
       "      <td>71.690417</td>\n",
       "      <td>107.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 20:28:22</td>\n",
       "      <td>93.41</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>114.060278</td>\n",
       "      <td>73.016111</td>\n",
       "      <td>6.083611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 21:52:24</td>\n",
       "      <td>53.63</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>115.460833</td>\n",
       "      <td>72.841944</td>\n",
       "      <td>1.400556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 23:50:35</td>\n",
       "      <td>56.21</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>117.430556</td>\n",
       "      <td>73.104722</td>\n",
       "      <td>1.969722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-30 02:59:55</td>\n",
       "      <td>88.06</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>120.586111</td>\n",
       "      <td>72.833889</td>\n",
       "      <td>3.155556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-30 12:44:14</td>\n",
       "      <td>83.77</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>130.324722</td>\n",
       "      <td>75.688333</td>\n",
       "      <td>9.738611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 00:40:37</td>\n",
       "      <td>105.57</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>142.264444</td>\n",
       "      <td>80.652500</td>\n",
       "      <td>11.939722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 15:34:12</td>\n",
       "      <td>117.16</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>157.157500</td>\n",
       "      <td>88.345278</td>\n",
       "      <td>14.893056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 19:23:22</td>\n",
       "      <td>69.99</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>160.976944</td>\n",
       "      <td>88.294444</td>\n",
       "      <td>3.819444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 11:29:53</td>\n",
       "      <td>144.93</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>177.085556</td>\n",
       "      <td>97.720694</td>\n",
       "      <td>16.108611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 12:11:53</td>\n",
       "      <td>21.71</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>177.785556</td>\n",
       "      <td>99.409444</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 14:29:06</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>180.072500</td>\n",
       "      <td>99.413889</td>\n",
       "      <td>2.286944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 21:15:50</td>\n",
       "      <td>82.28</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>186.851389</td>\n",
       "      <td>101.655833</td>\n",
       "      <td>6.778889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 23:06:01</td>\n",
       "      <td>63.82</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>188.687778</td>\n",
       "      <td>101.197639</td>\n",
       "      <td>1.836389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 06:05:50</td>\n",
       "      <td>64.18</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>195.684722</td>\n",
       "      <td>105.969861</td>\n",
       "      <td>6.996944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 08:59:40</td>\n",
       "      <td>98.19</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>198.581944</td>\n",
       "      <td>103.830139</td>\n",
       "      <td>2.897222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 18:29:24</td>\n",
       "      <td>218.68</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>208.077500</td>\n",
       "      <td>96.360000</td>\n",
       "      <td>9.495556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 19:40:35</td>\n",
       "      <td>31.05</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>209.263889</td>\n",
       "      <td>97.942500</td>\n",
       "      <td>1.186389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      toll_both  vehicle_no  loading_out_time        ist_timestamp  \\\n",
       "1345          1          50                 2  2017-10-29 14:23:21   \n",
       "1346          2          50                 2  2017-10-29 20:28:22   \n",
       "1347          3          50                 2  2017-10-29 21:52:24   \n",
       "1348          4          50                 2  2017-10-29 23:50:35   \n",
       "1349          5          50                 2  2017-10-30 02:59:55   \n",
       "1350          6          50                 2  2017-10-30 12:44:14   \n",
       "1351          9          50                 2  2017-10-31 00:40:37   \n",
       "1352         11          50                 2  2017-10-31 15:34:12   \n",
       "1353         12          50                 2  2017-10-31 19:23:22   \n",
       "1354         14          50                 2  2017-11-01 11:29:53   \n",
       "1355         13          50                 2  2017-11-01 12:11:53   \n",
       "1356         15          50                 2  2017-11-01 14:29:06   \n",
       "1357         16          50                 2  2017-11-01 21:15:50   \n",
       "1358         17          50                 2  2017-11-01 23:06:01   \n",
       "1359         18          50                 2  2017-11-02 06:05:50   \n",
       "1360         19          50                 2  2017-11-02 08:59:40   \n",
       "1361         21          50                 2  2017-11-02 18:29:24   \n",
       "1362         20          50                 2  2017-11-02 19:40:35   \n",
       "\n",
       "      distance_travelled  time_of_travel  day_of_travel  month_of_travel  \\\n",
       "1345                0.00              14              5               10   \n",
       "1346               93.41              20              5               10   \n",
       "1347               53.63              21              5               10   \n",
       "1348               56.21              23              5               10   \n",
       "1349               88.06               2              5               10   \n",
       "1350               83.77              12              5               10   \n",
       "1351              105.57               0              5               10   \n",
       "1352              117.16              15              5               10   \n",
       "1353               69.99              19              5               10   \n",
       "1354              144.93              11              5               10   \n",
       "1355               21.71              12              5               10   \n",
       "1356               45.88              14              5               10   \n",
       "1357               82.28              21              5               10   \n",
       "1358               63.82              23              5               10   \n",
       "1359               64.18               6              5               10   \n",
       "1360               98.19               8              5               10   \n",
       "1361              218.68              18              5               10   \n",
       "1362               31.05              19              5               10   \n",
       "\n",
       "      days_from_now  delivery  time_taken       delay  time_btn_tolls  \n",
       "1345             53         1  107.976667   71.690417      107.976667  \n",
       "1346             53         1  114.060278   73.016111        6.083611  \n",
       "1347             53         1  115.460833   72.841944        1.400556  \n",
       "1348             53         1  117.430556   73.104722        1.969722  \n",
       "1349             53         1  120.586111   72.833889        3.155556  \n",
       "1350             53         1  130.324722   75.688333        9.738611  \n",
       "1351             53         1  142.264444   80.652500       11.939722  \n",
       "1352             53         1  157.157500   88.345278       14.893056  \n",
       "1353             53         1  160.976944   88.294444        3.819444  \n",
       "1354             53         1  177.085556   97.720694       16.108611  \n",
       "1355             53         1  177.785556   99.409444        0.700000  \n",
       "1356             53         1  180.072500   99.413889        2.286944  \n",
       "1357             53         1  186.851389  101.655833        6.778889  \n",
       "1358             53         1  188.687778  101.197639        1.836389  \n",
       "1359             53         1  195.684722  105.969861        6.996944  \n",
       "1360             53         1  198.581944  103.830139        2.897222  \n",
       "1361             53         1  208.077500   96.360000        9.495556  \n",
       "1362             53         1  209.263889   97.942500        1.186389  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_train_df = train_df[['toll_both','vehicle_no','distance_travelled','time_btn_tolls']]\n",
    "final_test_df = test_df[['toll_both','vehicle_no','distance_travelled','time_btn_tolls']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAIyCAYAAADv6U8XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f838d7b7890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn heatmap documentation\n",
    "# https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html\n",
    "\n",
    "# cmap choices: http://matplotlib.org/users/colormaps.html\n",
    "plt.figure(figsize=(9,9))\n",
    "# pivot_table = phase_1_2.pivot('helix1 phase', 'helix 2 phase','Energy')\n",
    "plt.xlabel('helix 2 phase', size = 15)\n",
    "plt.ylabel('helix1 phase', size = 15)\n",
    "plt.title('Energy from Helix Phase Angles', size = 15)\n",
    "sns.heatmap(train_df.corr(), annot=True, fmt=\".1f\", linewidths=.5, square = True, cmap = 'Blues_r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = final_train_df.drop(['time_btn_tolls'], axis=1)\n",
    "train_y = final_train_df.time_btn_tolls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale= MinMaxScaler()\n",
    "train_X_scaled = scale.fit_transform(train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train_X_scaled,train_y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_mlp = MLPRegressor(activation='logistic', alpha=5, solver='lbfgs', random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folds: 5, mean squared error: 63.03 std: 44.85\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(reg_mlp, train_X_scaled, train_y,\n",
    " scoring='neg_mean_squared_error', cv=5,\n",
    " n_jobs=-1)\n",
    "print 'Folds: %i, mean squared error: %.2f std: %.2f' %(len(scores),np.mean(np.abs(scores)),np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.52 (+/- 0.14)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(reg_mlp, train_X_scaled, train_y, scoring='r2', n_jobs=-1)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: -66.24 (+/- 57.29)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(reg_mlp, train_X_scaled, train_y, scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59133015507508757"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_mlp.fit(X_train, y_train)\n",
    "reg_mlp.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = MinMaxScaler()\n",
    "test_X = final_test_df.drop(['time_btn_tolls'], axis=1)\n",
    "X_predict_scaled = scale.fit_transform(test_X)\n",
    "predictions = reg_mlp.predict(X_predict_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 34.16033799,   8.32825411,   3.58603082,   4.57669946,\n",
       "         9.5918364 ,   9.72407861,  12.17253985,  12.8693258 ,\n",
       "        10.07852735,  13.48989563,   1.82312786,   5.96648253,\n",
       "        11.22385084,   9.02284876,   8.92455333,  11.57800397,\n",
       "        13.81348087,   2.75651629])"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[  3.52847986e-02,  -6.97595383e-02,   6.98608948e-01,\n",
       "           4.73835393e-01,   6.16241515e-03,  -9.58084368e-01,\n",
       "          -1.69831965e-01,  -1.44300500e-01,   3.25734743e-01,\n",
       "           7.51496889e-02,   6.90315790e-01,  -3.51585019e-01,\n",
       "           2.37501512e-01,  -1.62572923e-01,  -1.80798955e-01,\n",
       "          -2.04687414e-01,   7.46203110e-01,   4.41763145e-01,\n",
       "          -2.57146903e-01,  -2.42494919e-01,  -6.99746963e-01,\n",
       "          -2.31348992e+00,   3.97649023e-01,   3.43207545e-01,\n",
       "           6.38388577e-02,   2.19710256e-01,  -2.24325599e-01,\n",
       "          -2.96684142e-01,   6.42411219e-01,  -5.92062259e+00,\n",
       "           4.27730994e-02,  -1.44162942e-01,  -2.22622930e-01,\n",
       "          -4.22463133e-01,  -1.13426832e+00,   6.80124530e-01,\n",
       "           6.25778713e-01,  -6.13734601e+00,  -4.16790733e-02,\n",
       "           5.32578274e-01,  -4.26723890e-01,  -3.76812716e-01,\n",
       "          -4.37258600e-01,   7.61511374e-01,  -1.16246271e-01,\n",
       "           5.58570101e-01,   6.74377337e-01,   2.74530913e-01,\n",
       "           6.55466632e-01,   5.89835144e-01,   3.90844925e-01,\n",
       "           9.07057201e-03,   5.59658676e-01,   7.69285658e-02,\n",
       "           7.61168267e-02,  -5.82052983e+00,   9.29247871e-01,\n",
       "          -1.88011409e-01,   5.33894372e-01,   5.07032064e-01,\n",
       "           5.20386571e-02,  -2.42401319e-01,   3.26722764e-01,\n",
       "          -2.39888651e-01,  -2.50665441e-01,   4.67190890e-01,\n",
       "           9.83680808e-01,   2.44282240e-01,   2.89057964e-01,\n",
       "          -1.60470213e-01,  -1.11836183e-01,   5.10304074e-03,\n",
       "           4.50502016e+00,   2.16343328e-01,   6.50163635e-01,\n",
       "          -8.43880190e-01,  -4.71847423e-01,  -2.15126802e-01,\n",
       "           7.90951270e-01,  -2.01060205e-01,  -9.63634934e-02,\n",
       "          -3.60922176e-02,  -2.12028169e-01,  -6.11882315e-01,\n",
       "          -2.31688267e-01,   1.19907049e+00,  -3.97466841e-02,\n",
       "           3.73064885e-01,  -2.67733943e-01,  -3.51351275e-01,\n",
       "          -1.86937945e-01,   4.46232072e-01,   5.12073935e-01,\n",
       "           3.56461926e-01,   7.36100086e-01,   5.87819497e-01,\n",
       "          -4.17217218e-01,   4.94444254e-01,  -7.36682518e+00,\n",
       "          -8.13292445e+00],\n",
       "        [  9.79131820e-02,  -2.39533526e-02,  -4.80766401e-01,\n",
       "          -1.01809495e+00,  -3.72033873e-01,  -6.29458908e-01,\n",
       "          -1.14524972e-01,   2.61343282e-01,   1.03630407e-02,\n",
       "          -1.98495846e+00,   8.96990528e-01,  -4.83496178e-02,\n",
       "           1.06242189e-02,  -2.81696597e-01,  -3.93641846e-01,\n",
       "          -2.45743717e-01,  -1.33965312e-01,  -1.35692591e-02,\n",
       "          -9.17131596e-02,  -2.89481845e-01,  -7.36093231e-01,\n",
       "          -4.74920138e-01,   1.57832815e-01,  -1.01844490e-01,\n",
       "           1.49575440e-01,  -5.22374888e-01,   1.42301127e-01,\n",
       "          -2.71589739e-01,  -1.07046778e-01,  -5.84561284e-01,\n",
       "           1.21140535e-01,  -1.86339409e-01,  -3.04247171e-01,\n",
       "          -4.98806817e-01,  -1.08199346e+00,  -5.00320498e-01,\n",
       "           3.51085474e-01,   4.94652478e-01,  -7.85371145e-01,\n",
       "           2.37308703e-01,   7.72865118e-01,   3.49328182e-01,\n",
       "          -5.90398592e-01,  -1.05335795e-01,  -2.98275581e-01,\n",
       "           4.35505540e-02,  -1.17661482e+00,  -1.23100096e-01,\n",
       "          -1.78685369e-01,   5.63213745e-01,  -7.44674433e-01,\n",
       "          -6.79263537e-01,  -1.18069029e+00,   6.25381943e-02,\n",
       "           4.06405029e-02,  -1.27376146e-01,   7.78861288e-01,\n",
       "          -1.04831150e-01,   7.95702685e-01,   5.94752294e-01,\n",
       "           5.52165332e-02,  -1.94935823e-01,  -4.24470673e-01,\n",
       "           8.20375898e-02,  -2.86155648e-01,  -1.10037232e-01,\n",
       "           6.42899059e-01,  -8.12461276e-02,  -3.17756708e-01,\n",
       "          -5.40683442e-02,   1.65526433e-01,  -5.89191464e-01,\n",
       "          -6.72759413e-01,  -8.17462481e-02,  -2.87603945e-01,\n",
       "          -7.23858195e-01,  -2.26967045e-03,  -2.98730787e-01,\n",
       "          -1.08483553e+00,   2.70461367e-02,  -1.06513621e+00,\n",
       "          -6.89656908e-01,   5.87917940e-02,  -9.26179253e-01,\n",
       "           8.96802547e-02,   2.27153844e-01,   1.79624877e-01,\n",
       "           6.91963958e-03,  -1.84583957e-01,   2.64070003e-01,\n",
       "          -6.55098958e-02,  -4.70655325e-01,  -8.21315795e-02,\n",
       "          -1.59002378e-02,  -3.39733548e-01,  -2.65860681e-01,\n",
       "          -7.87360572e-01,   1.99467359e-01,   1.14940559e+00,\n",
       "           5.03265695e-01],\n",
       "        [  7.79089539e-02,   1.72353043e-01,   1.26694932e-01,\n",
       "          -2.85828057e-02,  -7.57856110e-01,  -2.92408631e+00,\n",
       "           1.15041613e-01,   8.53743840e-02,   3.54758519e-01,\n",
       "           5.37999727e-02,  -8.16761949e-02,  -5.01724978e-01,\n",
       "           3.46428648e-01,   9.06929844e-02,   9.35317881e-02,\n",
       "          -5.41712104e-02,  -6.16602305e-02,   2.31002051e-01,\n",
       "           7.83870044e-02,   5.34398592e-02,   2.35565481e-01,\n",
       "          -5.20939945e+00,  -2.54642794e-01,   3.75240019e-01,\n",
       "           1.16974733e-01,  -2.83503691e-01,   2.04703383e-01,\n",
       "           1.67897053e-01,   1.79782193e-01,   2.84173826e+00,\n",
       "           1.53658236e-01,   9.29224824e-01,  -8.70976856e-02,\n",
       "          -1.38896656e+01,  -1.72350620e+00,  -2.74066242e-02,\n",
       "          -2.39248665e-01,  -1.13895094e+01,  -2.96228052e-02,\n",
       "          -2.72891629e-01,  -1.58875384e-01,   1.57313082e-01,\n",
       "          -2.29550897e-01,   1.52393756e-01,   1.07356937e+00,\n",
       "           5.93272606e-01,   5.12051957e-01,   6.49270578e-01,\n",
       "           1.56834495e-01,  -2.98776392e-01,   2.17419558e-02,\n",
       "          -6.21066293e-01,  -1.94600830e-01,   1.56463786e-01,\n",
       "           1.43101897e-01,  -1.93614748e+00,   1.79199234e+00,\n",
       "           9.61951411e-02,  -5.05123792e-01,  -2.85288416e-01,\n",
       "           1.14480159e-01,   5.85529550e-03,   6.74953563e-02,\n",
       "           1.03611692e-01,   5.04405473e-01,   2.84641919e-01,\n",
       "           1.22863715e-03,   1.29766381e-01,  -5.97521958e-01,\n",
       "          -1.61679106e-01,   4.28499395e-02,   1.28808934e+00,\n",
       "           4.94273646e+00,   4.48589250e-02,   1.96420024e-01,\n",
       "           5.38982225e-02,   1.26724542e-01,  -1.02224608e-01,\n",
       "           1.00804507e+00,   1.43416165e-01,  -3.03933188e-01,\n",
       "          -8.98949961e-02,   1.53233104e-01,  -2.40825636e+00,\n",
       "           7.12296480e-02,   2.78113997e+00,   8.36823794e-02,\n",
       "           2.71194083e-01,   9.00124452e-02,   1.30899909e-01,\n",
       "           8.75509612e-02,   1.46255491e-01,   2.71779156e-01,\n",
       "           3.02498425e-01,   9.15087651e-02,   2.72764607e-01,\n",
       "          -1.55215262e-01,  -2.63422537e-01,  -2.57797353e+01,\n",
       "          -2.39593729e+01]]), array([[  3.38232976e-01],\n",
       "        [  5.19699226e-01],\n",
       "        [ -1.49315398e-01],\n",
       "        [  3.02390476e-01],\n",
       "        [ -4.19067554e-01],\n",
       "        [  2.06670368e+00],\n",
       "        [  1.76007841e-01],\n",
       "        [  3.65259108e-01],\n",
       "        [  1.01219534e+00],\n",
       "        [ -4.22681422e-01],\n",
       "        [  9.09261870e-01],\n",
       "        [ -9.01752602e-01],\n",
       "        [  6.80685261e-01],\n",
       "        [  4.54266072e-01],\n",
       "        [  2.65381300e-01],\n",
       "        [  1.87753852e-01],\n",
       "        [ -1.65421241e-01],\n",
       "        [  1.80084993e-01],\n",
       "        [  1.10588957e-01],\n",
       "        [  2.94994140e-01],\n",
       "        [ -4.32623033e-02],\n",
       "        [  3.69463766e+00],\n",
       "        [  1.33128909e-01],\n",
       "        [  5.07652169e-01],\n",
       "        [  3.98643618e-01],\n",
       "        [ -3.14413692e-01],\n",
       "        [  2.32560649e-01],\n",
       "        [  1.34314827e-01],\n",
       "        [  4.77027871e-02],\n",
       "        [  3.98882442e+00],\n",
       "        [  4.20869972e-01],\n",
       "        [  5.06539082e-01],\n",
       "        [  3.30873020e-01],\n",
       "        [ -1.10312059e+01],\n",
       "        [ -1.15204580e+00],\n",
       "        [ -1.36537930e-01],\n",
       "        [  3.78087494e-01],\n",
       "        [  9.77785023e+00],\n",
       "        [ -3.69817035e-01],\n",
       "        [  2.89058490e-01],\n",
       "        [ -5.05185561e-01],\n",
       "        [  5.61328751e-02],\n",
       "        [ -2.57609349e-01],\n",
       "        [  6.33947150e-03],\n",
       "        [  5.89395225e-01],\n",
       "        [  6.32874333e-01],\n",
       "        [ -3.88962797e-01],\n",
       "        [  5.58022294e-01],\n",
       "        [  1.50201872e-03],\n",
       "        [  6.69357137e-01],\n",
       "        [ -1.61363352e-01],\n",
       "        [ -4.90412275e-01],\n",
       "        [ -5.14754079e-01],\n",
       "        [  3.51954184e-01],\n",
       "        [  3.13651967e-01],\n",
       "        [ -4.18616774e+00],\n",
       "        [  2.10730113e+00],\n",
       "        [  1.51521903e-01],\n",
       "        [  2.45251839e-01],\n",
       "        [  7.06915183e-01],\n",
       "        [  2.92989441e-01],\n",
       "        [  2.72285593e-01],\n",
       "        [  1.04705549e-01],\n",
       "        [  1.42162768e-01],\n",
       "        [  1.55214531e-01],\n",
       "        [  3.80426292e-01],\n",
       "        [  3.25491693e-01],\n",
       "        [  4.36136539e-01],\n",
       "        [ -2.76194302e-01],\n",
       "        [  1.79438723e-01],\n",
       "        [  2.92603236e-01],\n",
       "        [  5.80689753e-01],\n",
       "        [  4.90916193e+00],\n",
       "        [  3.18753752e-01],\n",
       "        [  1.62868945e-01],\n",
       "        [ -1.83319088e-01],\n",
       "        [  3.87346981e-01],\n",
       "        [  2.54080566e-01],\n",
       "        [ -7.63607339e-01],\n",
       "        [  1.19386709e-01],\n",
       "        [ -8.13269450e-01],\n",
       "        [ -2.50418015e-01],\n",
       "        [  2.08862178e-01],\n",
       "        [  1.15873876e+00],\n",
       "        [  2.70570285e-01],\n",
       "        [ -1.50873200e+00],\n",
       "        [  3.37212224e-01],\n",
       "        [  2.39594234e-01],\n",
       "        [  9.29282466e-02],\n",
       "        [  3.50359813e-02],\n",
       "        [  1.59161794e-01],\n",
       "        [  4.36611111e-01],\n",
       "        [  1.41876733e-01],\n",
       "        [  2.00722144e-01],\n",
       "        [ -1.18961027e-01],\n",
       "        [  1.61340709e-01],\n",
       "        [ -4.99100461e-01],\n",
       "        [  1.89777705e-01],\n",
       "        [  2.57373852e+01],\n",
       "        [  2.38695852e+01]])]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_mlp.coefs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df = test_df.copy()\n",
    "show_df['predicted'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['total_time'] = show_df['time_btn_tolls'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['total_predicted'] = show_df['predicted'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toll_both</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>loading_out_time</th>\n",
       "      <th>ist_timestamp</th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>time_of_travel</th>\n",
       "      <th>day_of_travel</th>\n",
       "      <th>month_of_travel</th>\n",
       "      <th>days_from_now</th>\n",
       "      <th>delivery</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>delay</th>\n",
       "      <th>time_btn_tolls</th>\n",
       "      <th>predicted</th>\n",
       "      <th>total_time</th>\n",
       "      <th>total_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 14:23:21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>107.976667</td>\n",
       "      <td>71.690417</td>\n",
       "      <td>107.976667</td>\n",
       "      <td>34.160338</td>\n",
       "      <td>107.976667</td>\n",
       "      <td>34.160338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 20:28:22</td>\n",
       "      <td>93.41</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>114.060278</td>\n",
       "      <td>73.016111</td>\n",
       "      <td>6.083611</td>\n",
       "      <td>8.328254</td>\n",
       "      <td>114.060278</td>\n",
       "      <td>42.488592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 21:52:24</td>\n",
       "      <td>53.63</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>115.460833</td>\n",
       "      <td>72.841944</td>\n",
       "      <td>1.400556</td>\n",
       "      <td>3.586031</td>\n",
       "      <td>115.460833</td>\n",
       "      <td>46.074623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-29 23:50:35</td>\n",
       "      <td>56.21</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>117.430556</td>\n",
       "      <td>73.104722</td>\n",
       "      <td>1.969722</td>\n",
       "      <td>4.576699</td>\n",
       "      <td>117.430556</td>\n",
       "      <td>50.651322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-30 02:59:55</td>\n",
       "      <td>88.06</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>120.586111</td>\n",
       "      <td>72.833889</td>\n",
       "      <td>3.155556</td>\n",
       "      <td>9.591836</td>\n",
       "      <td>120.586111</td>\n",
       "      <td>60.243159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-30 12:44:14</td>\n",
       "      <td>83.77</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>130.324722</td>\n",
       "      <td>75.688333</td>\n",
       "      <td>9.738611</td>\n",
       "      <td>9.724079</td>\n",
       "      <td>130.324722</td>\n",
       "      <td>69.967237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 00:40:37</td>\n",
       "      <td>105.57</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>142.264444</td>\n",
       "      <td>80.652500</td>\n",
       "      <td>11.939722</td>\n",
       "      <td>12.172540</td>\n",
       "      <td>142.264444</td>\n",
       "      <td>82.139777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 15:34:12</td>\n",
       "      <td>117.16</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>157.157500</td>\n",
       "      <td>88.345278</td>\n",
       "      <td>14.893056</td>\n",
       "      <td>12.869326</td>\n",
       "      <td>157.157500</td>\n",
       "      <td>95.009103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-10-31 19:23:22</td>\n",
       "      <td>69.99</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>160.976944</td>\n",
       "      <td>88.294444</td>\n",
       "      <td>3.819444</td>\n",
       "      <td>10.078527</td>\n",
       "      <td>160.976944</td>\n",
       "      <td>105.087630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 11:29:53</td>\n",
       "      <td>144.93</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>177.085556</td>\n",
       "      <td>97.720694</td>\n",
       "      <td>16.108611</td>\n",
       "      <td>13.489896</td>\n",
       "      <td>177.085556</td>\n",
       "      <td>118.577526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 12:11:53</td>\n",
       "      <td>21.71</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>177.785556</td>\n",
       "      <td>99.409444</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.823128</td>\n",
       "      <td>177.785556</td>\n",
       "      <td>120.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 14:29:06</td>\n",
       "      <td>45.88</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>180.072500</td>\n",
       "      <td>99.413889</td>\n",
       "      <td>2.286944</td>\n",
       "      <td>5.966483</td>\n",
       "      <td>180.072500</td>\n",
       "      <td>126.367136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 21:15:50</td>\n",
       "      <td>82.28</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>186.851389</td>\n",
       "      <td>101.655833</td>\n",
       "      <td>6.778889</td>\n",
       "      <td>11.223851</td>\n",
       "      <td>186.851389</td>\n",
       "      <td>137.590987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-01 23:06:01</td>\n",
       "      <td>63.82</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>188.687778</td>\n",
       "      <td>101.197639</td>\n",
       "      <td>1.836389</td>\n",
       "      <td>9.022849</td>\n",
       "      <td>188.687778</td>\n",
       "      <td>146.613836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 06:05:50</td>\n",
       "      <td>64.18</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>195.684722</td>\n",
       "      <td>105.969861</td>\n",
       "      <td>6.996944</td>\n",
       "      <td>8.924553</td>\n",
       "      <td>195.684722</td>\n",
       "      <td>155.538389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 08:59:40</td>\n",
       "      <td>98.19</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>198.581944</td>\n",
       "      <td>103.830139</td>\n",
       "      <td>2.897222</td>\n",
       "      <td>11.578004</td>\n",
       "      <td>198.581944</td>\n",
       "      <td>167.116393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 18:29:24</td>\n",
       "      <td>218.68</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>208.077500</td>\n",
       "      <td>96.360000</td>\n",
       "      <td>9.495556</td>\n",
       "      <td>13.813481</td>\n",
       "      <td>208.077500</td>\n",
       "      <td>180.929874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-11-02 19:40:35</td>\n",
       "      <td>31.05</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>209.263889</td>\n",
       "      <td>97.942500</td>\n",
       "      <td>1.186389</td>\n",
       "      <td>2.756516</td>\n",
       "      <td>209.263889</td>\n",
       "      <td>183.686390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      toll_both  vehicle_no  loading_out_time        ist_timestamp  \\\n",
       "1345          1          50                 2  2017-10-29 14:23:21   \n",
       "1346          2          50                 2  2017-10-29 20:28:22   \n",
       "1347          3          50                 2  2017-10-29 21:52:24   \n",
       "1348          4          50                 2  2017-10-29 23:50:35   \n",
       "1349          5          50                 2  2017-10-30 02:59:55   \n",
       "1350          6          50                 2  2017-10-30 12:44:14   \n",
       "1351          9          50                 2  2017-10-31 00:40:37   \n",
       "1352         11          50                 2  2017-10-31 15:34:12   \n",
       "1353         12          50                 2  2017-10-31 19:23:22   \n",
       "1354         14          50                 2  2017-11-01 11:29:53   \n",
       "1355         13          50                 2  2017-11-01 12:11:53   \n",
       "1356         15          50                 2  2017-11-01 14:29:06   \n",
       "1357         16          50                 2  2017-11-01 21:15:50   \n",
       "1358         17          50                 2  2017-11-01 23:06:01   \n",
       "1359         18          50                 2  2017-11-02 06:05:50   \n",
       "1360         19          50                 2  2017-11-02 08:59:40   \n",
       "1361         21          50                 2  2017-11-02 18:29:24   \n",
       "1362         20          50                 2  2017-11-02 19:40:35   \n",
       "\n",
       "      distance_travelled  time_of_travel  day_of_travel  month_of_travel  \\\n",
       "1345                0.00              14              5               10   \n",
       "1346               93.41              20              5               10   \n",
       "1347               53.63              21              5               10   \n",
       "1348               56.21              23              5               10   \n",
       "1349               88.06               2              5               10   \n",
       "1350               83.77              12              5               10   \n",
       "1351              105.57               0              5               10   \n",
       "1352              117.16              15              5               10   \n",
       "1353               69.99              19              5               10   \n",
       "1354              144.93              11              5               10   \n",
       "1355               21.71              12              5               10   \n",
       "1356               45.88              14              5               10   \n",
       "1357               82.28              21              5               10   \n",
       "1358               63.82              23              5               10   \n",
       "1359               64.18               6              5               10   \n",
       "1360               98.19               8              5               10   \n",
       "1361              218.68              18              5               10   \n",
       "1362               31.05              19              5               10   \n",
       "\n",
       "      days_from_now  delivery  time_taken       delay  time_btn_tolls  \\\n",
       "1345             53         1  107.976667   71.690417      107.976667   \n",
       "1346             53         1  114.060278   73.016111        6.083611   \n",
       "1347             53         1  115.460833   72.841944        1.400556   \n",
       "1348             53         1  117.430556   73.104722        1.969722   \n",
       "1349             53         1  120.586111   72.833889        3.155556   \n",
       "1350             53         1  130.324722   75.688333        9.738611   \n",
       "1351             53         1  142.264444   80.652500       11.939722   \n",
       "1352             53         1  157.157500   88.345278       14.893056   \n",
       "1353             53         1  160.976944   88.294444        3.819444   \n",
       "1354             53         1  177.085556   97.720694       16.108611   \n",
       "1355             53         1  177.785556   99.409444        0.700000   \n",
       "1356             53         1  180.072500   99.413889        2.286944   \n",
       "1357             53         1  186.851389  101.655833        6.778889   \n",
       "1358             53         1  188.687778  101.197639        1.836389   \n",
       "1359             53         1  195.684722  105.969861        6.996944   \n",
       "1360             53         1  198.581944  103.830139        2.897222   \n",
       "1361             53         1  208.077500   96.360000        9.495556   \n",
       "1362             53         1  209.263889   97.942500        1.186389   \n",
       "\n",
       "      predicted  total_time  total_predicted  \n",
       "1345  34.160338  107.976667        34.160338  \n",
       "1346   8.328254  114.060278        42.488592  \n",
       "1347   3.586031  115.460833        46.074623  \n",
       "1348   4.576699  117.430556        50.651322  \n",
       "1349   9.591836  120.586111        60.243159  \n",
       "1350   9.724079  130.324722        69.967237  \n",
       "1351  12.172540  142.264444        82.139777  \n",
       "1352  12.869326  157.157500        95.009103  \n",
       "1353  10.078527  160.976944       105.087630  \n",
       "1354  13.489896  177.085556       118.577526  \n",
       "1355   1.823128  177.785556       120.400654  \n",
       "1356   5.966483  180.072500       126.367136  \n",
       "1357  11.223851  186.851389       137.590987  \n",
       "1358   9.022849  188.687778       146.613836  \n",
       "1359   8.924553  195.684722       155.538389  \n",
       "1360  11.578004  198.581944       167.116393  \n",
       "1361  13.813481  208.077500       180.929874  \n",
       "1362   2.756516  209.263889       183.686390  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "kalman_df = show_df[['distance_travelled','predicted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>0.00</td>\n",
       "      <td>34.160338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>93.41</td>\n",
       "      <td>8.328254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>53.63</td>\n",
       "      <td>3.586031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>56.21</td>\n",
       "      <td>4.576699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>88.06</td>\n",
       "      <td>9.591836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>83.77</td>\n",
       "      <td>9.724079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>105.57</td>\n",
       "      <td>12.172540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>117.16</td>\n",
       "      <td>12.869326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>69.99</td>\n",
       "      <td>10.078527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>144.93</td>\n",
       "      <td>13.489896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>21.71</td>\n",
       "      <td>1.823128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>45.88</td>\n",
       "      <td>5.966483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>82.28</td>\n",
       "      <td>11.223851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>63.82</td>\n",
       "      <td>9.022849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>64.18</td>\n",
       "      <td>8.924553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>98.19</td>\n",
       "      <td>11.578004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>218.68</td>\n",
       "      <td>13.813481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>31.05</td>\n",
       "      <td>2.756516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      distance_travelled  predicted\n",
       "1345                0.00  34.160338\n",
       "1346               93.41   8.328254\n",
       "1347               53.63   3.586031\n",
       "1348               56.21   4.576699\n",
       "1349               88.06   9.591836\n",
       "1350               83.77   9.724079\n",
       "1351              105.57  12.172540\n",
       "1352              117.16  12.869326\n",
       "1353               69.99  10.078527\n",
       "1354              144.93  13.489896\n",
       "1355               21.71   1.823128\n",
       "1356               45.88   5.966483\n",
       "1357               82.28  11.223851\n",
       "1358               63.82   9.022849\n",
       "1359               64.18   8.924553\n",
       "1360               98.19  11.578004\n",
       "1361              218.68  13.813481\n",
       "1362               31.05   2.756516"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kalman_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 34.16033799,   8.32825411,   3.58603082,   4.57669946,\n",
       "         9.5918364 ,   9.72407861,  12.17253985,  12.8693258 ,\n",
       "        10.07852735,  13.48989563,   1.82312786,   5.96648253,\n",
       "        11.22385084,   9.02284876,   8.92455333,  11.57800397,\n",
       "        13.81348087,   2.75651629])"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurements = np.asarray(predictions[3:,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4.57669946,   9.5918364 ,   9.72407861,  12.17253985,\n",
       "        12.8693258 ,  10.07852735,  13.48989563,   1.82312786,\n",
       "         5.96648253,  11.22385084,   9.02284876,   8.92455333,\n",
       "        11.57800397,  13.81348087,   2.75651629])"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_state_mean = np.array(test_df.iloc[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 56.21      ,   4.57669946])"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_state_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toll_both</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>distance_travelled</th>\n",
       "      <th>time_btn_tolls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.976667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>93.41</td>\n",
       "      <td>6.083611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>53.63</td>\n",
       "      <td>1.400556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>56.21</td>\n",
       "      <td>1.969722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      toll_both  vehicle_no  distance_travelled  time_btn_tolls\n",
       "1345          1          50                0.00      107.976667\n",
       "1346          2          50               93.41        6.083611\n",
       "1347          3          50               53.63        1.400556\n",
       "1348          4          50               56.21        1.969722"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test_df.iloc[[0,1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "transition_matrix = reg_mlp.coefs_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_matrix = np.asarray(final_train_df.iloc[[0,1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 56.21      ,   4.57669946])"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_state_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1.        ,    1.        ,    0.        ,   85.67833333],\n",
       "       [   3.        ,    1.        ,  146.47      ,    6.63333333],\n",
       "       [   4.        ,    1.        ,   55.83      ,    2.19222222]])"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observation_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shapes (3,4) and (2,1) not aligned: 4 (dim 1) != 2 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-359-971672ea195f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m                                                                       \u001b[0mobservation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtransition_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                                                                       \u001b[0mobservation_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobservation_matrix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                                                                       observation_covariance = np.asarray([[.1622,0,0],[0,.1622,0],[0,0,.1622]]))\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pykalman/standard.pyc\u001b[0m in \u001b[0;36mfilter_update\u001b[0;34m(self, filtered_state_mean, filtered_state_covariance, observation, transition_matrix, transition_offset, transition_covariance, observation_matrix, observation_offset, observation_covariance)\u001b[0m\n\u001b[1;32m   1282\u001b[0m                 \u001b[0mobservation_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobservation_covariance\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1283\u001b[0m                 \u001b[0mobservation_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_state_mean\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1284\u001b[0;31m                 \u001b[0mpredicted_state_covariance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobservation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1285\u001b[0m             )\n\u001b[1;32m   1286\u001b[0m         )\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pykalman/standard.pyc\u001b[0m in \u001b[0;36m_filter_correct\u001b[0;34m(observation_matrix, observation_covariance, observation_offset, predicted_state_mean, predicted_state_covariance, observation)\u001b[0m\n\u001b[1;32m    258\u001b[0m         predicted_observation_mean = (\n\u001b[1;32m    259\u001b[0m             np.dot(observation_matrix,\n\u001b[0;32m--> 260\u001b[0;31m                    predicted_state_mean)\n\u001b[0m\u001b[1;32m    261\u001b[0m             \u001b[0;34m+\u001b[0m \u001b[0mobservation_offset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: shapes (3,4) and (2,1) not aligned: 4 (dim 1) != 2 (dim 0)"
     ]
    }
   ],
   "source": [
    "kf = KalmanFilter()\n",
    "\n",
    "(filtered_state_means, filtered_state_covariances) = kf.filter_update(filtered_state_mean = [[0],[0]],\n",
    "                                                                      filtered_state_covariance = [[90000,0],[0,90000]],\n",
    "                                                                      observation=np.asarray([[7],[2],[14]]),\n",
    "                                                                      transition_matrix = np.asarray([[1,0],[0,1]]),\n",
    "                                                                      observation_matrix = np.asarray([[2,1],[0,1],[5,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data =\n",
       " [[ 2.39473719]\n",
       " [ 2.0789453 ]],\n",
       "             mask =\n",
       " False,\n",
       "       fill_value = 1e+20)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_state_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'filter_update' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-347-dd2905559592>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m filter_update(filtered_state_mean, filtered_state_covariance, observation=None, transition_matrix=None, \n\u001b[0m\u001b[1;32m      2\u001b[0m               \u001b[0mtransition_offset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransition_covariance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobservation_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m               observation_offset=None, observation_covariance=None)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'filter_update' is not defined"
     ]
    }
   ],
   "source": [
    "filter_update(filtered_state_mean, filtered_state_covariance, observation=None, transition_matrix=None, \n",
    "              transition_offset=None, transition_covariance=None, observation_matrix=None, \n",
    "              observation_offset=None, observation_covariance=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The shape of all parameters is not consistent.  Please re-check their values.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-344-dd0f00efb247>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m kf1 = KalmanFilter(transition_matrices = transition_matrix,\n\u001b[0;32m----> 2\u001b[0;31m                   initial_state_mean = initial_state_mean)\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pykalman/standard.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, transition_matrices, observation_matrices, transition_covariance, observation_covariance, transition_offsets, observation_offsets, initial_state_mean, initial_state_covariance, random_state, em_vars, n_dim_state, n_dim_obs)\u001b[0m\n\u001b[1;32m   1025\u001b[0m              \u001b[0;34m(\u001b[0m\u001b[0minitial_state_covariance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m              (observation_matrices, array2d, -1)],\n\u001b[0;32m-> 1027\u001b[0;31m             \u001b[0mn_dim_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m         )\n\u001b[1;32m   1029\u001b[0m         n_dim_obs = _determine_dimensionality(\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pykalman/standard.pyc\u001b[0m in \u001b[0;36m_determine_dimensionality\u001b[0;34m(variables, default)\u001b[0m\n\u001b[1;32m     76\u001b[0m                 \u001b[0;34m\"The shape of all \"\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m                 \u001b[0;34m\"parameters is not consistent.  \"\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m                 \u001b[0;34m\"Please re-check their values.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m             )\n\u001b[1;32m     80\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcandidates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The shape of all parameters is not consistent.  Please re-check their values."
     ]
    }
   ],
   "source": [
    "kf1 = KalmanFilter(transition_matrices = transition_matrix,\n",
    "                  initial_state_mean = initial_state_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'kf1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-268-9f16bbdc932f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeasurements\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msmoothed_state_means\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmoothed_state_covariances\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeasurements\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'kf1' is not defined"
     ]
    }
   ],
   "source": [
    "kf1 = kf1.em(measurements, n_iter=5)\n",
    "(smoothed_state_means, smoothed_state_covariances) = kf1.smooth(measurements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~mainakchain/39.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace = go.Scatter(\n",
    "    x = train_df['distance_travelled'].tolist(),\n",
    "    y = train_df['time_btn_tolls'].apply(lambda x:x*60),\n",
    "    mode = 'markers'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "\n",
    "# Plot and embed in ipython notebook!\n",
    "py.iplot(data, filename='basic-scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data\n",
    "secs = ['EWA', 'EWC']\n",
    "data = data.DataReader(secs, 'yahoo', '2010-1-1', '2014-8-1')['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "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>EWA</th>\n",
       "      <th>EWC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-01</th>\n",
       "      <td>22.840326</td>\n",
       "      <td>29.960068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>22.987291</td>\n",
       "      <td>30.251669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-30</th>\n",
       "      <td>23.281225</td>\n",
       "      <td>30.674969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>23.263933</td>\n",
       "      <td>30.580898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td>23.298517</td>\n",
       "      <td>30.740816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>23.212065</td>\n",
       "      <td>30.712595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>23.359030</td>\n",
       "      <td>30.778442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-23</th>\n",
       "      <td>23.428192</td>\n",
       "      <td>30.797255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-22</th>\n",
       "      <td>23.177485</td>\n",
       "      <td>30.646749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-21</th>\n",
       "      <td>23.013229</td>\n",
       "      <td>30.496241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-18</th>\n",
       "      <td>23.108320</td>\n",
       "      <td>30.524462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-17</th>\n",
       "      <td>22.848972</td>\n",
       "      <td>30.298704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-16</th>\n",
       "      <td>23.013229</td>\n",
       "      <td>30.373955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-15</th>\n",
       "      <td>22.944065</td>\n",
       "      <td>30.101164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-14</th>\n",
       "      <td>23.056454</td>\n",
       "      <td>30.308109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-11</th>\n",
       "      <td>22.952711</td>\n",
       "      <td>30.167011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-10</th>\n",
       "      <td>22.710649</td>\n",
       "      <td>30.373955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-09</th>\n",
       "      <td>22.840326</td>\n",
       "      <td>30.543278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-08</th>\n",
       "      <td>22.857615</td>\n",
       "      <td>30.364552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td>22.944065</td>\n",
       "      <td>30.364552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-03</th>\n",
       "      <td>22.944065</td>\n",
       "      <td>30.609121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-02</th>\n",
       "      <td>22.909487</td>\n",
       "      <td>30.468023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>22.693359</td>\n",
       "      <td>30.449209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>22.589617</td>\n",
       "      <td>30.298704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-27</th>\n",
       "      <td>22.848972</td>\n",
       "      <td>30.214045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-26</th>\n",
       "      <td>22.848972</td>\n",
       "      <td>29.969473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-25</th>\n",
       "      <td>22.537746</td>\n",
       "      <td>29.753120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-24</th>\n",
       "      <td>22.448706</td>\n",
       "      <td>29.682570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-23</th>\n",
       "      <td>22.763800</td>\n",
       "      <td>29.990694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-20</th>\n",
       "      <td>22.644577</td>\n",
       "      <td>29.915997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-12</th>\n",
       "      <td>15.013734</td>\n",
       "      <td>21.875607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-11</th>\n",
       "      <td>15.281835</td>\n",
       "      <td>21.867027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-10</th>\n",
       "      <td>14.766797</td>\n",
       "      <td>21.378044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-09</th>\n",
       "      <td>14.858518</td>\n",
       "      <td>21.215048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-08</th>\n",
       "      <td>14.435200</td>\n",
       "      <td>20.803274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-05</th>\n",
       "      <td>14.526918</td>\n",
       "      <td>21.026320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-04</th>\n",
       "      <td>14.442253</td>\n",
       "      <td>20.871902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-03</th>\n",
       "      <td>15.161897</td>\n",
       "      <td>21.523882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-02</th>\n",
       "      <td>15.352391</td>\n",
       "      <td>21.618248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>15.246559</td>\n",
       "      <td>21.360886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>14.886736</td>\n",
       "      <td>20.966270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-28</th>\n",
       "      <td>15.295946</td>\n",
       "      <td>21.180733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-27</th>\n",
       "      <td>15.437052</td>\n",
       "      <td>21.403778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-26</th>\n",
       "      <td>15.507608</td>\n",
       "      <td>21.506725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-25</th>\n",
       "      <td>15.789816</td>\n",
       "      <td>21.575354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-22</th>\n",
       "      <td>15.549937</td>\n",
       "      <td>21.515303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21</th>\n",
       "      <td>15.874482</td>\n",
       "      <td>21.961393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-20</th>\n",
       "      <td>16.424799</td>\n",
       "      <td>22.424641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-19</th>\n",
       "      <td>16.925726</td>\n",
       "      <td>22.947943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>16.678791</td>\n",
       "      <td>22.922205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>17.038614</td>\n",
       "      <td>23.265348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>16.826952</td>\n",
       "      <td>23.196724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>16.572958</td>\n",
       "      <td>22.965097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>17.137388</td>\n",
       "      <td>23.376875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>16.968060</td>\n",
       "      <td>23.445501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>16.770510</td>\n",
       "      <td>23.325405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>16.848124</td>\n",
       "      <td>23.402611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>16.784624</td>\n",
       "      <td>23.222456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>16.735235</td>\n",
       "      <td>23.093777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>16.114365</td>\n",
       "      <td>22.587635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1154 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  EWA        EWC\n",
       "Date                            \n",
       "2014-08-01  22.840326  29.960068\n",
       "2014-07-31  22.987291  30.251669\n",
       "2014-07-30  23.281225  30.674969\n",
       "2014-07-29  23.263933  30.580898\n",
       "2014-07-28  23.298517  30.740816\n",
       "2014-07-25  23.212065  30.712595\n",
       "2014-07-24  23.359030  30.778442\n",
       "2014-07-23  23.428192  30.797255\n",
       "2014-07-22  23.177485  30.646749\n",
       "2014-07-21  23.013229  30.496241\n",
       "2014-07-18  23.108320  30.524462\n",
       "2014-07-17  22.848972  30.298704\n",
       "2014-07-16  23.013229  30.373955\n",
       "2014-07-15  22.944065  30.101164\n",
       "2014-07-14  23.056454  30.308109\n",
       "2014-07-11  22.952711  30.167011\n",
       "2014-07-10  22.710649  30.373955\n",
       "2014-07-09  22.840326  30.543278\n",
       "2014-07-08  22.857615  30.364552\n",
       "2014-07-07  22.944065  30.364552\n",
       "2014-07-03  22.944065  30.609121\n",
       "2014-07-02  22.909487  30.468023\n",
       "2014-07-01  22.693359  30.449209\n",
       "2014-06-30  22.589617  30.298704\n",
       "2014-06-27  22.848972  30.214045\n",
       "2014-06-26  22.848972  29.969473\n",
       "2014-06-25  22.537746  29.753120\n",
       "2014-06-24  22.448706  29.682570\n",
       "2014-06-23  22.763800  29.990694\n",
       "2014-06-20  22.644577  29.915997\n",
       "...               ...        ...\n",
       "2010-02-12  15.013734  21.875607\n",
       "2010-02-11  15.281835  21.867027\n",
       "2010-02-10  14.766797  21.378044\n",
       "2010-02-09  14.858518  21.215048\n",
       "2010-02-08  14.435200  20.803274\n",
       "2010-02-05  14.526918  21.026320\n",
       "2010-02-04  14.442253  20.871902\n",
       "2010-02-03  15.161897  21.523882\n",
       "2010-02-02  15.352391  21.618248\n",
       "2010-02-01  15.246559  21.360886\n",
       "2010-01-29  14.886736  20.966270\n",
       "2010-01-28  15.295946  21.180733\n",
       "2010-01-27  15.437052  21.403778\n",
       "2010-01-26  15.507608  21.506725\n",
       "2010-01-25  15.789816  21.575354\n",
       "2010-01-22  15.549937  21.515303\n",
       "2010-01-21  15.874482  21.961393\n",
       "2010-01-20  16.424799  22.424641\n",
       "2010-01-19  16.925726  22.947943\n",
       "2010-01-15  16.678791  22.922205\n",
       "2010-01-14  17.038614  23.265348\n",
       "2010-01-13  16.826952  23.196724\n",
       "2010-01-12  16.572958  22.965097\n",
       "2010-01-11  17.137388  23.376875\n",
       "2010-01-08  16.968060  23.445501\n",
       "2010-01-07  16.770510  23.325405\n",
       "2010-01-06  16.848124  23.402611\n",
       "2010-01-05  16.784624  23.222456\n",
       "2010-01-04  16.735235  23.093777\n",
       "2009-12-31  16.114365  22.587635\n",
       "\n",
       "[1154 rows x 2 columns]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers, not str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-319-53c7cc403477>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobs_mat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EWA'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EWA'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers, not str"
     ]
    }
   ],
   "source": [
    "obs_mat = np.vstack([data['EWA'], np.ones(data['EWA'].shape)]).T[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}