ML for Parking Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Travelling Behavior Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, i tried to predict people's travelling behavior by some features. It was a part of my projects for BIOS Hackathon 2019 (by Universitas Multimedia Nusantara, Indonesia: https://www.umn.ac.id/bios-hackathon-2019-buat-prototype-aplikasi-selama-24-jam/) and Jakbee Hackathon 2019 (by DKI Jakarta Governance and BAZNAS DKI Jakarta, Indonesia: https://timur.jakarta.go.id/v19/news/Pemerintahan/5043/Gubernur-DKI-Hadiri-Jakbee-Hackathon-Dan-Masa-Depan-Jakarta?utm_source=WebTimurWidget&utm_medium=box&utm_campaign=BeritaTerpopuler). \n",
    "\n",
    "In these two hackathons, my team created AI-powered Apps for reserving parking areas. The model that i built here was used to recommend costumers' most likely visited parking areas by their behaviors. People can also rent their home's carport, park, or some of their legal lot as a car or motorbike parking area in this apps. We believe that this apps, with sharing economy concept, can stop illegal parking growth in Indonesia. We were awarded as first winner in both competitions. Take a look for more descriptions and more visual docummentations in my LinkedIn profile:\n",
    "www.linkedin.com/in/banggarakana\n",
    "\n",
    "Thanks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, i use uber rides dataset from https://www.kaggle.com/ because it provides everything that i need to make the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_completed_at</th>\n",
       "      <th>trip_status</th>\n",
       "      <th>ride_hailing_app</th>\n",
       "      <th>trip_uid</th>\n",
       "      <th>driver_uid</th>\n",
       "      <th>rider_uid</th>\n",
       "      <th>customer</th>\n",
       "      <th>trip_start_time</th>\n",
       "      <th>dayornight</th>\n",
       "      <th>trip_end_time</th>\n",
       "      <th>...</th>\n",
       "      <th>distance_kms</th>\n",
       "      <th>temperature_time</th>\n",
       "      <th>temperature_value</th>\n",
       "      <th>feels_like</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_desc</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>cloudness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May 11, 2015 at 6:55PM</td>\n",
       "      <td>Completed</td>\n",
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       "      <td>stantyan</td>\n",
       "      <td>5:26 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:55 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.29</td>\n",
       "      <td>2015-05-11T17:26:00</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.81</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May 11, 2015 at 8:12PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>518be51d403944a03c47e8d1f2c87311</td>\n",
       "      <td>4a4e248742f9d5ff517c5bbbb48d0e54</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>6:46 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>7:12 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.93</td>\n",
       "      <td>2015-05-11T18:46:00</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.70</td>\n",
       "      <td>6.53</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May 13, 2015 at 11:38AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>6e460cc8a12c3c6568d0d4a67ac58393</td>\n",
       "      <td>cb249a2bd807ca78697b4ed0348c37da</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:15 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:38 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>18.01</td>\n",
       "      <td>2015-05-13T09:15:00</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.25</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May 16, 2015 at 1:44AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>49613a86a04e6c15d72b51d1a2935d81</td>\n",
       "      <td>d3f73f8151c2e8c34b541f961db7f5fa</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>12:24 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>12:44 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.10</td>\n",
       "      <td>2015-05-16T00:24:00</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.87</td>\n",
       "      <td>partly-cloudy-night</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May 16, 2015 at 3:18AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>9896148fdecdb4c5d977a8691510bdb6</td>\n",
       "      <td>1287d21e6455ee40d4861f6b91c680f4</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>1:29 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>2:18 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>21.92</td>\n",
       "      <td>2015-05-16T01:29:00</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.61</td>\n",
       "      <td>partly-cloudy-night</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May 18, 2015 at 11:06AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>5c0312a92ff104197d799c42ae67542f</td>\n",
       "      <td>fc6b1516376f15c97e508d904505d27a</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:32 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:06 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>4.88</td>\n",
       "      <td>2015-05-18T09:32:00</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.83</td>\n",
       "      <td>2.73</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May 18, 2015 at 11:08PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>4ad2e954813b53afeb73ce659ac3376c</td>\n",
       "      <td>1b926e88a8477f7b5d1fad298e00fb11</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:24 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>10:08 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>18.65</td>\n",
       "      <td>2015-05-18T21:24:00</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>4.08</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May 19, 2015 at 9:10AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1e3935b05addc654d65e72b8da96fd43</td>\n",
       "      <td>439ae2cf8ae38bc24b2f8dbc3f0b987d</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>7:12 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>8:10 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>12.62</td>\n",
       "      <td>2015-05-19T07:12:00</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.96</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May 19, 2015 at 12:37PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>0eb9a9f7a3fd598c885c67af75645c06</td>\n",
       "      <td>75a4c47c323bcc96ac5849052b19ed5f</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:22 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11:37 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.94</td>\n",
       "      <td>2015-05-19T11:22:00</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.88</td>\n",
       "      <td>4.64</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>May 19, 2015 at 10:33PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>b56495d149fea002e04438a3369ab532</td>\n",
       "      <td>176f50c4249ddc7b086d8997349d9ae5</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:00 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>9:33 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>17.38</td>\n",
       "      <td>2015-05-19T21:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>May 20, 2015 at 11:08AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>613f3deb51643339560bc9041184e810</td>\n",
       "      <td>e516233997d15dedf0f12878fd231e23</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:42 AM</td>\n",
       "      <td>3</td>\n",
       "      <td>10:08 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.66</td>\n",
       "      <td>2015-05-20T09:42:00</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0.67</td>\n",
       "      <td>4.15</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>May 31, 2015 at 2:27PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>0d486aced52a47da5cf9e09757a964e7</td>\n",
       "      <td>a81952458c5e4c7b62a0bacf591dd690</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>1:27 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>14.30</td>\n",
       "      <td>2015-05-31T13:00:00</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.18</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>June 1, 2015 at 11:45PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>d12da2c7ae96bab237bba8823c3f9c74</td>\n",
       "      <td>f065223fa83baa7b2e0037f61b8ebac7</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>10:17 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>10:45 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>13.33</td>\n",
       "      <td>2015-06-01T22:17:00</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.24</td>\n",
       "      <td>partly-cloudy-night</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>June 3, 2015 at 4:14PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>36695e9088a840d3f7476e86294aa846</td>\n",
       "      <td>b897afbe685f70aa5114bf4f6a19392b</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>2:10 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>3:14 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.41</td>\n",
       "      <td>2015-06-03T14:10:00</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>0.63</td>\n",
       "      <td>5.11</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>June 3, 2015 at 5:20PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>30bd4a26ca276b8f04a01d87ea3777fc</td>\n",
       "      <td>ef33153fbce5b15f93d9319528dc598f</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>3:43 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>4:20 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2015-06-03T15:43:00</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>0.61</td>\n",
       "      <td>4.90</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>June 4, 2015 at 4:34AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>3e955966177bc5b2d97fc9a239c561ef</td>\n",
       "      <td>b6584dd094679d58c938511d478169d6</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>3:00 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>3:34 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.40</td>\n",
       "      <td>2015-06-04T03:00:00</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>clear-night</td>\n",
       "      <td>Clear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>June 5, 2015 at 4:31PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>4669e3d96401bf985ecf6ee9fd78a816</td>\n",
       "      <td>c85086781a9a41cb76345374fb11360a</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>3:06 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>3:31 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2015-06-05T15:06:00</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.23</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>June 6, 2015 at 2:59PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>a7e321acb3162ea301b42939c7ca7394</td>\n",
       "      <td>d505ef8cb3f776ca6acccd10b3fdbf78</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>1:29 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>1:59 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>4.75</td>\n",
       "      <td>2015-06-06T13:29:00</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.77</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>June 7, 2015 at 9:48PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>8e9c9603d9abce84a08458da0c718698</td>\n",
       "      <td>97e28e41c95e7cbf1e36d6737d0eeefe</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>8:21 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>8:48 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>14.61</td>\n",
       "      <td>2015-06-07T20:21:00</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.32</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>June 12, 2015 at 5:25PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>e01e7067e5cbbda3a9eb621a1268e8d0</td>\n",
       "      <td>767a9c87d435285ab26f73a14abbc8d2</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>4:11 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>4:28 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.05</td>\n",
       "      <td>2015-06-12T16:11:00</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.62</td>\n",
       "      <td>3.90</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>June 12, 2015 at 6:01PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Gett</td>\n",
       "      <td>c9df3a9edb2c7b37c80f9ffa5e1b8c36</td>\n",
       "      <td>151944a8f9967b3edad02deb712d61f2</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>4:25 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:01 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>12.81</td>\n",
       "      <td>2015-06-12T16:25:00</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.62</td>\n",
       "      <td>3.51</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>June 14, 2015 at 7:44AM</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Uber</td>\n",
       "      <td>b970e99cf7d4e163c9808ed945f9e5be</td>\n",
       "      <td>a89bc69e8452fcec3f650b688f6e3473</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>5:58 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>6:44 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2015-06-14T05:58:00</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.88</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>June 14, 2015 at 11:18AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>945269c9502d99bc6065d9c8166760b1</td>\n",
       "      <td>2f5f40db716f7d90f664a61e42a57c46</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:24 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:18 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>29.08</td>\n",
       "      <td>2015-06-14T09:24:00</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.19</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>June 14, 2015 at 1:39PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>04f14fe72ad64ecd1f62d8bfb5fb2ba1</td>\n",
       "      <td>6b3642ae75aaf7e19a5b525ffeeeaa69</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:59 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>12:39 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2015-06-14T11:59:00</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.11</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>June 14, 2015 at 8:31PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>fdbd229175bc4d808d52df176debf210</td>\n",
       "      <td>69f4e9a8758cf75e5dbc8fb9b80a5bd2</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>7:01 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>7:31 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>19.10</td>\n",
       "      <td>2015-06-14T19:01:00</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.01</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>June 14, 2015 at 10:41PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>d4984dfe402ec968a2365649c478de41</td>\n",
       "      <td>e1992ce90ac08ca0d5afa13da214e187</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:04 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>9:41 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>21.18</td>\n",
       "      <td>2015-06-14T21:04:00</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.41</td>\n",
       "      <td>cloudy</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>0</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>June 14, 2015 at 11:19PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>57bd0828f1a6d5e812b0ca38cab4231b</td>\n",
       "      <td>b2cadbd6ea7ae0f6b30e2968652830d7</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>10:00 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>10:19 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2015-06-14T22:00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.79</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>June 14, 2015 at 12:46AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>268145a88a53bbb369a1e812be234908</td>\n",
       "      <td>a45d6a74641c9079c0a0f91d34446aa1</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:17 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>11:46 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.85</td>\n",
       "      <td>2015-06-14T23:17:00</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.73</td>\n",
       "      <td>partly-cloudy-night</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>June 15, 2015 at 10:48AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>9622fd4e1819f8656c418ffdfcd359a8</td>\n",
       "      <td>78aab4ce1d6f0c1e0acf0a1b0b933ce6</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:01 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>9:58 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>17.79</td>\n",
       "      <td>2015-06-15T09:01:00</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0.74</td>\n",
       "      <td>4.12</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>June 15, 2015 at 11:10AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>72801dd197d168a38ebaa88875492d60</td>\n",
       "      <td>3613586d80fac02ff4bbbb4ce24fed87</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>8:42 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:10 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>21.22</td>\n",
       "      <td>2015-06-15T08:42:00</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.66</td>\n",
       "      <td>5.75</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.62</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",
       "      <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>648</th>\n",
       "      <td>March 16, 2018 at 09:50PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>4680ab0d1315efda3127590babd645f4</td>\n",
       "      <td>4b878a752734d04f0d7166cf771785ad</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:14 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>9:50 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>11.59</td>\n",
       "      <td>2018-03-16T21:14:00</td>\n",
       "      <td>-8</td>\n",
       "      <td>-14</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.98</td>\n",
       "      <td>partly-cloudy-night</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>March 17, 2018 at 02:59PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>ed8e683821e2e1f040a580f06815e0da</td>\n",
       "      <td>db8834cee846c8b35a81fec692037f5a</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>2:21 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>2:59 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.29</td>\n",
       "      <td>2018-03-17T14:21:00</td>\n",
       "      <td>-6</td>\n",
       "      <td>-12</td>\n",
       "      <td>0.64</td>\n",
       "      <td>4.80</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>March 19, 2018 at 10:08AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>c4e175f18fc08f7422f961caa8390867</td>\n",
       "      <td>fd4d4b6d729b41a493754e834e4a55e6</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:43 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:08 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.09</td>\n",
       "      <td>2018-03-19T09:43:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5.62</td>\n",
       "      <td>snow</td>\n",
       "      <td>Flurries</td>\n",
       "      <td>0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>March 19, 2018 at 01:36PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>d7778e57d2e045d7eeda2431c5476efa</td>\n",
       "      <td>5c814a116a76cd8be152270d43f16438</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>1:12 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>1:36 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.46</td>\n",
       "      <td>2018-03-19T13:12:00</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>0.82</td>\n",
       "      <td>6.16</td>\n",
       "      <td>snow</td>\n",
       "      <td>Flurries</td>\n",
       "      <td>0</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>March 19, 2018 at 09:16PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>489a00849b68640b22860f2e5eda3fc6</td>\n",
       "      <td>6cf501815d18b3b9fcd5cba781119092</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>8:40 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>9:16 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>7.92</td>\n",
       "      <td>2018-03-19T20:40:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>4.84</td>\n",
       "      <td>rain</td>\n",
       "      <td>Drizzle</td>\n",
       "      <td>0</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>March 20, 2018 at 07:38PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>169cc0cc774e69bdc6c5daa6437acbc5</td>\n",
       "      <td>e4a509e2aca78d76cd59f9e2d659207e</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>7:19 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>7:38 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>4.91</td>\n",
       "      <td>2018-03-20T19:19:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-4</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.65</td>\n",
       "      <td>snow</td>\n",
       "      <td>Flurries</td>\n",
       "      <td>0</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>March 21, 2018 at 04:08PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>b180bb9c1399be40e529352b6cef844a</td>\n",
       "      <td>7dbfd5a19f39f8f10dc0bf9466858ca3</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>3:41 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>4:08 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>6.61</td>\n",
       "      <td>2018-03-21T15:41:00</td>\n",
       "      <td>-5</td>\n",
       "      <td>-9</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.09</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>March 21, 2018 at 05:47PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>009d39f3d6fb390197a2ddb8ff3a3961</td>\n",
       "      <td>0e692e7bbab235172f69fd2e505b7556</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>5:11 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:45 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.19</td>\n",
       "      <td>2018-03-21T17:11:00</td>\n",
       "      <td>-5</td>\n",
       "      <td>-8</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.83</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>March 26, 2018 at 05:46AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>e577ffebf76c3e4474405710c5bac6fd</td>\n",
       "      <td>d3bf1f1ba25b57ae936c69c66c0f8eb8</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>5:23 AM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:46 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.93</td>\n",
       "      <td>2018-03-26T05:23:00</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3.99</td>\n",
       "      <td>fog</td>\n",
       "      <td>Foggy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>March 26, 2018 at 11:50PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>a6a7fa0860dd3dedfbf5f2b3f0108b8e</td>\n",
       "      <td>38bf923e6a1d2daec6a18afea6244a56</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:25 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>11:50 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.04</td>\n",
       "      <td>2018-03-26T23:25:00</td>\n",
       "      <td>-8</td>\n",
       "      <td>-8</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.17</td>\n",
       "      <td>clear-night</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>March 30, 2018 at 01:13PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>cf6dde37f034d88626c59baf96160e22</td>\n",
       "      <td>0a314fbc4e88d9401a2c6f9ce6431d1b</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>12:40 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>1:13 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.69</td>\n",
       "      <td>2018-03-30T12:40:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.56</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>March 30, 2018 at 03:43PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>e824dfc7aee2954a48ff8c544bc356f1</td>\n",
       "      <td>e16c5bac50f877af7321ce4b8dce8f6e</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>3:07 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>3:43 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.96</td>\n",
       "      <td>2018-03-30T15:07:00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.47</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>April 2, 2018 at 05:43PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>8a0169c4bcdd439e53e6d9171163e67e</td>\n",
       "      <td>50f7345a0dee40b3ccef7561bb0abc7a</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>5:02 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:43 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>6.18</td>\n",
       "      <td>2018-04-02T17:02:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>4.77</td>\n",
       "      <td>rain</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>April 4, 2018 at 09:37AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>6c3d3dad4e9374baa1eaa0221f2de832</td>\n",
       "      <td>d8639f27ca78ea0193b1f6d251697ec4</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>9:15 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>9:37 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>7.08</td>\n",
       "      <td>2018-04-04T09:15:00</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.63</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>April 4, 2018 at 11:40AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>307c5fef399666fdc58e5fdcb1b485ac</td>\n",
       "      <td>275b800368042b0e500997b6e6410ffb</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11:40 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>8.43</td>\n",
       "      <td>2018-04-04T11:17:00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.81</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>April 4, 2018 at 07:54PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>cccd84c9c90738540dc47cc6ef13a8de</td>\n",
       "      <td>815e8aa01a2e697ead24b293ce405ef2</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>7:05 PM</td>\n",
       "      <td>3</td>\n",
       "      <td>7:54 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.01</td>\n",
       "      <td>2018-04-04T19:05:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.62</td>\n",
       "      <td>4.56</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>April 5, 2018 at 11:36AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>97bdc85e1fbc97d9a5343529d777d4f3</td>\n",
       "      <td>fca722bbe330fb7d7830bda983717109</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:11 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11:36 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>12.81</td>\n",
       "      <td>2018-04-05T11:11:00</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.78</td>\n",
       "      <td>3.56</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>April 5, 2018 at 05:10PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>44710d54cab0a131b943cb4b9524b576</td>\n",
       "      <td>83fc88c85060a8d7e8c96258d8002016</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>4:43 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:10 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>14.08</td>\n",
       "      <td>2018-04-05T16:43:00</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5.15</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>April 9, 2018 at 12:20PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>80ae86cf699105b63ff04da16b0e9023</td>\n",
       "      <td>d88bc99cc640aa60799fc4ccfe3231ea</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:34 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>12:20 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>34.18</td>\n",
       "      <td>2018-04-09T11:34:00</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3.76</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>April 12, 2018 at 01:26PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>b4490a4fd184cc2f12569750d50ad67c</td>\n",
       "      <td>c4f1f21ac4cd136a89d7756609e8940e</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>12:54 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>1:26 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.99</td>\n",
       "      <td>2018-04-12T12:54:00</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.22</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>April 12, 2018 at 06:05PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>a7f621ef27212fa1e54572cad4edde58</td>\n",
       "      <td>adee3059ee7ddf290a7a04d0b9fad61f</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>5:28 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>6:05 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>10.73</td>\n",
       "      <td>2018-04-12T17:28:00</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0.28</td>\n",
       "      <td>3.69</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>April 13, 2018 at 06:13PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>ff2fe36701f7e1a7d60898972df30e56</td>\n",
       "      <td>76412a4ca12da91ef882041c08746f25</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>5:36 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>6:13 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.89</td>\n",
       "      <td>2018-04-13T17:36:00</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.34</td>\n",
       "      <td>3.11</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>April 19, 2018 at 10:54AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>8a531b2bebaa4cb72915b43914bbd97c</td>\n",
       "      <td>0a43af18ab6928e72eaaac52ee58c5ce</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>10:33 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:54 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>7.10</td>\n",
       "      <td>2018-04-19T10:33:00</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.96</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>April 19, 2018 at 11:55AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>6995ab85de12672bf28ba46c713ead7d</td>\n",
       "      <td>88496b5bd9a82fc882ff2a22cc744d2c</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:19 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>11:55 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>7.97</td>\n",
       "      <td>2018-04-19T11:19:00</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>0.69</td>\n",
       "      <td>3.83</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>April 23, 2018 at 10:59AM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>8ad3710eadbf7225ed43ee277b13ab90</td>\n",
       "      <td>6b81d4b07871146faffcf00502faa876</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>10:44 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>10:59 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>4.26</td>\n",
       "      <td>2018-04-23T10:44:00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.84</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>April 23, 2018 at 12:11PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>ce5bdea8e9c366b2f9f9cdf1bb879965</td>\n",
       "      <td>e96ad9d6f2f0b2c55b2fa2c284507543</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>11:59 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>12:11 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>5.79</td>\n",
       "      <td>2018-04-23T11:59:00</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.57</td>\n",
       "      <td>clear-day</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>April 24, 2018 at 02:58PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>3a1df88ce67d8f749ac5f09da8b4dadf</td>\n",
       "      <td>e63e621e61b493e119239f5ff635810d</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>2:21 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>2:58 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>11.46</td>\n",
       "      <td>2018-04-24T14:21:00</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.62</td>\n",
       "      <td>5.37</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>April 26, 2018 at 03:57PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>1879ce44367d4cc555479253323ec36d</td>\n",
       "      <td>b65196da38435a1dbe8e9009b514a7b8</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>3:22 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>3:57 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>6.68</td>\n",
       "      <td>2018-04-26T15:22:00</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.76</td>\n",
       "      <td>4.02</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>April 26, 2018 at 05:00PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>7a237cb245b6ca854498ecb7c842beb3</td>\n",
       "      <td>2af94300321de81edc1eeef8c041fd9a</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>4:37 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>5:00 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>7.48</td>\n",
       "      <td>2018-04-26T16:37:00</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0.68</td>\n",
       "      <td>4.28</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>April 27, 2018 at 03:05PM</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Uber</td>\n",
       "      <td>f794daa1e63029828ec58248549815d2</td>\n",
       "      <td>ab02b9b447412505a2f42cefab54520b</td>\n",
       "      <td>3ffa4a71a5aa791a8bc3409f5b15b936</td>\n",
       "      <td>stantyan</td>\n",
       "      <td>2:15 PM</td>\n",
       "      <td>2</td>\n",
       "      <td>3:05 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>9.03</td>\n",
       "      <td>2018-04-27T14:15:00</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.56</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>678 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             trip_completed_at trip_status ride_hailing_app  \\\n",
       "0       May 11, 2015 at 6:55PM   Completed             Uber   \n",
       "1       May 11, 2015 at 8:12PM   Completed             Uber   \n",
       "2      May 13, 2015 at 11:38AM   Completed             Uber   \n",
       "3       May 16, 2015 at 1:44AM   Completed             Uber   \n",
       "4       May 16, 2015 at 3:18AM   Completed             Uber   \n",
       "5      May 18, 2015 at 11:06AM   Completed             Uber   \n",
       "6      May 18, 2015 at 11:08PM   Completed             Uber   \n",
       "7       May 19, 2015 at 9:10AM   Completed             Uber   \n",
       "8      May 19, 2015 at 12:37PM   Completed             Uber   \n",
       "9      May 19, 2015 at 10:33PM   Completed             Uber   \n",
       "10     May 20, 2015 at 11:08AM   Completed             Uber   \n",
       "11      May 31, 2015 at 2:27PM   Completed             Uber   \n",
       "12     June 1, 2015 at 11:45PM   Completed             Uber   \n",
       "13      June 3, 2015 at 4:14PM   Completed             Uber   \n",
       "14      June 3, 2015 at 5:20PM   Completed             Uber   \n",
       "15      June 4, 2015 at 4:34AM   Completed             Uber   \n",
       "16      June 5, 2015 at 4:31PM   Completed             Uber   \n",
       "17      June 6, 2015 at 2:59PM   Completed             Uber   \n",
       "18      June 7, 2015 at 9:48PM   Completed             Uber   \n",
       "19     June 12, 2015 at 5:25PM   Completed             Uber   \n",
       "20     June 12, 2015 at 6:01PM   Completed             Gett   \n",
       "21     June 14, 2015 at 7:44AM   Cancelled             Uber   \n",
       "22    June 14, 2015 at 11:18AM   Completed             Uber   \n",
       "23     June 14, 2015 at 1:39PM   Completed             Uber   \n",
       "24     June 14, 2015 at 8:31PM   Completed             Uber   \n",
       "25    June 14, 2015 at 10:41PM   Completed             Uber   \n",
       "26    June 14, 2015 at 11:19PM   Completed             Uber   \n",
       "27    June 14, 2015 at 12:46AM   Completed             Uber   \n",
       "28    June 15, 2015 at 10:48AM   Completed             Uber   \n",
       "29    June 15, 2015 at 11:10AM   Completed             Uber   \n",
       "..                         ...         ...              ...   \n",
       "648  March 16, 2018 at 09:50PM   Completed             Uber   \n",
       "649  March 17, 2018 at 02:59PM   Completed             Uber   \n",
       "650  March 19, 2018 at 10:08AM   Completed             Uber   \n",
       "651  March 19, 2018 at 01:36PM   Completed             Uber   \n",
       "652  March 19, 2018 at 09:16PM   Completed             Uber   \n",
       "653  March 20, 2018 at 07:38PM   Completed             Uber   \n",
       "654  March 21, 2018 at 04:08PM   Completed             Uber   \n",
       "655  March 21, 2018 at 05:47PM   Completed             Uber   \n",
       "656  March 26, 2018 at 05:46AM   Completed             Uber   \n",
       "657  March 26, 2018 at 11:50PM   Completed             Uber   \n",
       "658  March 30, 2018 at 01:13PM   Completed             Uber   \n",
       "659  March 30, 2018 at 03:43PM   Completed             Uber   \n",
       "660   April 2, 2018 at 05:43PM   Completed             Uber   \n",
       "661   April 4, 2018 at 09:37AM   Completed             Uber   \n",
       "662   April 4, 2018 at 11:40AM   Completed             Uber   \n",
       "663   April 4, 2018 at 07:54PM   Completed             Uber   \n",
       "664   April 5, 2018 at 11:36AM   Completed             Uber   \n",
       "665   April 5, 2018 at 05:10PM   Completed             Uber   \n",
       "666   April 9, 2018 at 12:20PM   Completed             Uber   \n",
       "667  April 12, 2018 at 01:26PM   Completed             Uber   \n",
       "668  April 12, 2018 at 06:05PM   Completed             Uber   \n",
       "669  April 13, 2018 at 06:13PM   Completed             Uber   \n",
       "670  April 19, 2018 at 10:54AM   Completed             Uber   \n",
       "671  April 19, 2018 at 11:55AM   Completed             Uber   \n",
       "672  April 23, 2018 at 10:59AM   Completed             Uber   \n",
       "673  April 23, 2018 at 12:11PM   Completed             Uber   \n",
       "674  April 24, 2018 at 02:58PM   Completed             Uber   \n",
       "675  April 26, 2018 at 03:57PM   Completed             Uber   \n",
       "676  April 26, 2018 at 05:00PM   Completed             Uber   \n",
       "677  April 27, 2018 at 03:05PM   Completed             Uber   \n",
       "\n",
       "                             trip_uid                        driver_uid  \\\n",
       "0    ee89076fd9da9bddf5f096b0ca42f8d5  05cfeb269e606247fe9d2b6082942c59   \n",
       "1    518be51d403944a03c47e8d1f2c87311  4a4e248742f9d5ff517c5bbbb48d0e54   \n",
       "2    6e460cc8a12c3c6568d0d4a67ac58393  cb249a2bd807ca78697b4ed0348c37da   \n",
       "3    49613a86a04e6c15d72b51d1a2935d81  d3f73f8151c2e8c34b541f961db7f5fa   \n",
       "4    9896148fdecdb4c5d977a8691510bdb6  1287d21e6455ee40d4861f6b91c680f4   \n",
       "5    5c0312a92ff104197d799c42ae67542f  fc6b1516376f15c97e508d904505d27a   \n",
       "6    4ad2e954813b53afeb73ce659ac3376c  1b926e88a8477f7b5d1fad298e00fb11   \n",
       "7    1e3935b05addc654d65e72b8da96fd43  439ae2cf8ae38bc24b2f8dbc3f0b987d   \n",
       "8    0eb9a9f7a3fd598c885c67af75645c06  75a4c47c323bcc96ac5849052b19ed5f   \n",
       "9    b56495d149fea002e04438a3369ab532  176f50c4249ddc7b086d8997349d9ae5   \n",
       "10   613f3deb51643339560bc9041184e810  e516233997d15dedf0f12878fd231e23   \n",
       "11   0d486aced52a47da5cf9e09757a964e7  a81952458c5e4c7b62a0bacf591dd690   \n",
       "12   d12da2c7ae96bab237bba8823c3f9c74  f065223fa83baa7b2e0037f61b8ebac7   \n",
       "13   36695e9088a840d3f7476e86294aa846  b897afbe685f70aa5114bf4f6a19392b   \n",
       "14   30bd4a26ca276b8f04a01d87ea3777fc  ef33153fbce5b15f93d9319528dc598f   \n",
       "15   3e955966177bc5b2d97fc9a239c561ef  b6584dd094679d58c938511d478169d6   \n",
       "16   4669e3d96401bf985ecf6ee9fd78a816  c85086781a9a41cb76345374fb11360a   \n",
       "17   a7e321acb3162ea301b42939c7ca7394  d505ef8cb3f776ca6acccd10b3fdbf78   \n",
       "18   8e9c9603d9abce84a08458da0c718698  97e28e41c95e7cbf1e36d6737d0eeefe   \n",
       "19   e01e7067e5cbbda3a9eb621a1268e8d0  767a9c87d435285ab26f73a14abbc8d2   \n",
       "20   c9df3a9edb2c7b37c80f9ffa5e1b8c36  151944a8f9967b3edad02deb712d61f2   \n",
       "21   b970e99cf7d4e163c9808ed945f9e5be  a89bc69e8452fcec3f650b688f6e3473   \n",
       "22   945269c9502d99bc6065d9c8166760b1  2f5f40db716f7d90f664a61e42a57c46   \n",
       "23   04f14fe72ad64ecd1f62d8bfb5fb2ba1  6b3642ae75aaf7e19a5b525ffeeeaa69   \n",
       "24   fdbd229175bc4d808d52df176debf210  69f4e9a8758cf75e5dbc8fb9b80a5bd2   \n",
       "25   d4984dfe402ec968a2365649c478de41  e1992ce90ac08ca0d5afa13da214e187   \n",
       "26   57bd0828f1a6d5e812b0ca38cab4231b  b2cadbd6ea7ae0f6b30e2968652830d7   \n",
       "27   268145a88a53bbb369a1e812be234908  a45d6a74641c9079c0a0f91d34446aa1   \n",
       "28   9622fd4e1819f8656c418ffdfcd359a8  78aab4ce1d6f0c1e0acf0a1b0b933ce6   \n",
       "29   72801dd197d168a38ebaa88875492d60  3613586d80fac02ff4bbbb4ce24fed87   \n",
       "..                                ...                               ...   \n",
       "648  4680ab0d1315efda3127590babd645f4  4b878a752734d04f0d7166cf771785ad   \n",
       "649  ed8e683821e2e1f040a580f06815e0da  db8834cee846c8b35a81fec692037f5a   \n",
       "650  c4e175f18fc08f7422f961caa8390867  fd4d4b6d729b41a493754e834e4a55e6   \n",
       "651  d7778e57d2e045d7eeda2431c5476efa  5c814a116a76cd8be152270d43f16438   \n",
       "652  489a00849b68640b22860f2e5eda3fc6  6cf501815d18b3b9fcd5cba781119092   \n",
       "653  169cc0cc774e69bdc6c5daa6437acbc5  e4a509e2aca78d76cd59f9e2d659207e   \n",
       "654  b180bb9c1399be40e529352b6cef844a  7dbfd5a19f39f8f10dc0bf9466858ca3   \n",
       "655  009d39f3d6fb390197a2ddb8ff3a3961  0e692e7bbab235172f69fd2e505b7556   \n",
       "656  e577ffebf76c3e4474405710c5bac6fd  d3bf1f1ba25b57ae936c69c66c0f8eb8   \n",
       "657  a6a7fa0860dd3dedfbf5f2b3f0108b8e  38bf923e6a1d2daec6a18afea6244a56   \n",
       "658  cf6dde37f034d88626c59baf96160e22  0a314fbc4e88d9401a2c6f9ce6431d1b   \n",
       "659  e824dfc7aee2954a48ff8c544bc356f1  e16c5bac50f877af7321ce4b8dce8f6e   \n",
       "660  8a0169c4bcdd439e53e6d9171163e67e  50f7345a0dee40b3ccef7561bb0abc7a   \n",
       "661  6c3d3dad4e9374baa1eaa0221f2de832  d8639f27ca78ea0193b1f6d251697ec4   \n",
       "662  307c5fef399666fdc58e5fdcb1b485ac  275b800368042b0e500997b6e6410ffb   \n",
       "663  cccd84c9c90738540dc47cc6ef13a8de  815e8aa01a2e697ead24b293ce405ef2   \n",
       "664  97bdc85e1fbc97d9a5343529d777d4f3  fca722bbe330fb7d7830bda983717109   \n",
       "665  44710d54cab0a131b943cb4b9524b576  83fc88c85060a8d7e8c96258d8002016   \n",
       "666  80ae86cf699105b63ff04da16b0e9023  d88bc99cc640aa60799fc4ccfe3231ea   \n",
       "667  b4490a4fd184cc2f12569750d50ad67c  c4f1f21ac4cd136a89d7756609e8940e   \n",
       "668  a7f621ef27212fa1e54572cad4edde58  adee3059ee7ddf290a7a04d0b9fad61f   \n",
       "669  ff2fe36701f7e1a7d60898972df30e56  76412a4ca12da91ef882041c08746f25   \n",
       "670  8a531b2bebaa4cb72915b43914bbd97c  0a43af18ab6928e72eaaac52ee58c5ce   \n",
       "671  6995ab85de12672bf28ba46c713ead7d  88496b5bd9a82fc882ff2a22cc744d2c   \n",
       "672  8ad3710eadbf7225ed43ee277b13ab90  6b81d4b07871146faffcf00502faa876   \n",
       "673  ce5bdea8e9c366b2f9f9cdf1bb879965  e96ad9d6f2f0b2c55b2fa2c284507543   \n",
       "674  3a1df88ce67d8f749ac5f09da8b4dadf  e63e621e61b493e119239f5ff635810d   \n",
       "675  1879ce44367d4cc555479253323ec36d  b65196da38435a1dbe8e9009b514a7b8   \n",
       "676  7a237cb245b6ca854498ecb7c842beb3  2af94300321de81edc1eeef8c041fd9a   \n",
       "677  f794daa1e63029828ec58248549815d2  ab02b9b447412505a2f42cefab54520b   \n",
       "\n",
       "                            rider_uid  customer trip_start_time  dayornight  \\\n",
       "0    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:26 PM           2   \n",
       "1    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         6:46 PM           3   \n",
       "2    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:15 AM           1   \n",
       "3    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        12:24 AM           1   \n",
       "4    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         1:29 AM           1   \n",
       "5    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:32 AM           1   \n",
       "6    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:24 PM           3   \n",
       "7    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         7:12 AM           1   \n",
       "8    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:22 AM           1   \n",
       "9    3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:00 PM           3   \n",
       "10   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:42 AM           3   \n",
       "11   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         1:00 PM           2   \n",
       "12   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        10:17 PM           3   \n",
       "13   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         2:10 PM           2   \n",
       "14   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         3:43 PM           2   \n",
       "15   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         3:00 AM           1   \n",
       "16   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         3:06 PM           2   \n",
       "17   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         1:29 PM           2   \n",
       "18   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         8:21 PM           3   \n",
       "19   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         4:11 PM           2   \n",
       "20   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         4:25 PM           2   \n",
       "21   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:58 AM           1   \n",
       "22   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:24 AM           1   \n",
       "23   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:59 AM           1   \n",
       "24   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         7:01 PM           3   \n",
       "25   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:04 PM           3   \n",
       "26   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        10:00 PM           3   \n",
       "27   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:17 PM           3   \n",
       "28   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:01 AM           1   \n",
       "29   3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         8:42 AM           1   \n",
       "..                                ...       ...             ...         ...   \n",
       "648  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:14 PM           3   \n",
       "649  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         2:21 PM           2   \n",
       "650  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:43 AM           1   \n",
       "651  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         1:12 PM           2   \n",
       "652  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         8:40 PM           3   \n",
       "653  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         7:19 PM           3   \n",
       "654  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         3:41 PM           2   \n",
       "655  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:11 PM           2   \n",
       "656  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:23 AM           2   \n",
       "657  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:25 PM           3   \n",
       "658  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        12:40 PM           2   \n",
       "659  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         3:07 PM           2   \n",
       "660  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:02 PM           2   \n",
       "661  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         9:15 AM           1   \n",
       "662  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:17 AM           1   \n",
       "663  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         7:05 PM           3   \n",
       "664  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:11 AM           1   \n",
       "665  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         4:43 PM           2   \n",
       "666  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:34 AM           1   \n",
       "667  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        12:54 PM           2   \n",
       "668  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:28 PM           2   \n",
       "669  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         5:36 PM           2   \n",
       "670  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        10:33 AM           1   \n",
       "671  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:19 AM           1   \n",
       "672  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        10:44 AM           1   \n",
       "673  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan        11:59 AM           1   \n",
       "674  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         2:21 PM           2   \n",
       "675  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         3:22 PM           2   \n",
       "676  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         4:37 PM           2   \n",
       "677  3ffa4a71a5aa791a8bc3409f5b15b936  stantyan         2:15 PM           2   \n",
       "\n",
       "    trip_end_time    ...    distance_kms     temperature_time  \\\n",
       "0         5:55 PM    ...            9.29  2015-05-11T17:26:00   \n",
       "1         7:12 PM    ...            9.93  2015-05-11T18:46:00   \n",
       "2        10:38 AM    ...           18.01  2015-05-13T09:15:00   \n",
       "3        12:44 AM    ...            5.10  2015-05-16T00:24:00   \n",
       "4         2:18 AM    ...           21.92  2015-05-16T01:29:00   \n",
       "5        10:06 AM    ...            4.88  2015-05-18T09:32:00   \n",
       "6        10:08 PM    ...           18.65  2015-05-18T21:24:00   \n",
       "7         8:10 AM    ...           12.62  2015-05-19T07:12:00   \n",
       "8        11:37 AM    ...            5.94  2015-05-19T11:22:00   \n",
       "9         9:33 PM    ...           17.38  2015-05-19T21:00:00   \n",
       "10       10:08 AM    ...            5.66  2015-05-20T09:42:00   \n",
       "11        1:27 PM    ...           14.30  2015-05-31T13:00:00   \n",
       "12       10:45 PM    ...           13.33  2015-06-01T22:17:00   \n",
       "13        3:14 PM    ...            9.41  2015-06-03T14:10:00   \n",
       "14        4:20 PM    ...            0.29  2015-06-03T15:43:00   \n",
       "15        3:34 AM    ...            8.40  2015-06-04T03:00:00   \n",
       "16        3:31 PM    ...            4.33  2015-06-05T15:06:00   \n",
       "17        1:59 PM    ...            4.75  2015-06-06T13:29:00   \n",
       "18        8:48 PM    ...           14.61  2015-06-07T20:21:00   \n",
       "19        4:28 PM    ...            5.05  2015-06-12T16:11:00   \n",
       "20        5:01 PM    ...           12.81  2015-06-12T16:25:00   \n",
       "21        6:44 AM    ...            0.10  2015-06-14T05:58:00   \n",
       "22       10:18 AM    ...           29.08  2015-06-14T09:24:00   \n",
       "23       12:39 PM    ...           24.00  2015-06-14T11:59:00   \n",
       "24        7:31 PM    ...           19.10  2015-06-14T19:01:00   \n",
       "25        9:41 PM    ...           21.18  2015-06-14T21:04:00   \n",
       "26       10:19 PM    ...            2.27  2015-06-14T22:00:00   \n",
       "27       11:46 PM    ...            8.85  2015-06-14T23:17:00   \n",
       "28        9:58 AM    ...           17.79  2015-06-15T09:01:00   \n",
       "29       10:10 AM    ...           21.22  2015-06-15T08:42:00   \n",
       "..            ...    ...             ...                  ...   \n",
       "648       9:50 PM    ...           11.59  2018-03-16T21:14:00   \n",
       "649       2:59 PM    ...            8.29  2018-03-17T14:21:00   \n",
       "650      10:08 AM    ...            5.09  2018-03-19T09:43:00   \n",
       "651       1:36 PM    ...            5.46  2018-03-19T13:12:00   \n",
       "652       9:16 PM    ...            7.92  2018-03-19T20:40:00   \n",
       "653       7:38 PM    ...            4.91  2018-03-20T19:19:00   \n",
       "654       4:08 PM    ...            6.61  2018-03-21T15:41:00   \n",
       "655       5:45 PM    ...            8.19  2018-03-21T17:11:00   \n",
       "656       5:46 AM    ...            8.93  2018-03-26T05:23:00   \n",
       "657      11:50 PM    ...            9.04  2018-03-26T23:25:00   \n",
       "658       1:13 PM    ...            9.69  2018-03-30T12:40:00   \n",
       "659       3:43 PM    ...            8.96  2018-03-30T15:07:00   \n",
       "660       5:43 PM    ...            6.18  2018-04-02T17:02:00   \n",
       "661       9:37 AM    ...            7.08  2018-04-04T09:15:00   \n",
       "662      11:40 AM    ...            8.43  2018-04-04T11:17:00   \n",
       "663       7:54 PM    ...            9.01  2018-04-04T19:05:00   \n",
       "664      11:36 AM    ...           12.81  2018-04-05T11:11:00   \n",
       "665       5:10 PM    ...           14.08  2018-04-05T16:43:00   \n",
       "666      12:20 PM    ...           34.18  2018-04-09T11:34:00   \n",
       "667       1:26 PM    ...            9.99  2018-04-12T12:54:00   \n",
       "668       6:05 PM    ...           10.73  2018-04-12T17:28:00   \n",
       "669       6:13 PM    ...            5.89  2018-04-13T17:36:00   \n",
       "670      10:54 AM    ...            7.10  2018-04-19T10:33:00   \n",
       "671      11:55 AM    ...            7.97  2018-04-19T11:19:00   \n",
       "672      10:59 AM    ...            4.26  2018-04-23T10:44:00   \n",
       "673      12:11 PM    ...            5.79  2018-04-23T11:59:00   \n",
       "674       2:58 PM    ...           11.46  2018-04-24T14:21:00   \n",
       "675       3:57 PM    ...            6.68  2018-04-26T15:22:00   \n",
       "676       5:00 PM    ...            7.48  2018-04-26T16:37:00   \n",
       "677       3:05 PM    ...            9.03  2018-04-27T14:15:00   \n",
       "\n",
       "    temperature_value feels_like  humidity wind_speed         weather_main  \\\n",
       "0                  12         12      0.69       4.81    partly-cloudy-day   \n",
       "1                  10         10      0.70       6.53    partly-cloudy-day   \n",
       "2                  14         14      0.61       5.25    partly-cloudy-day   \n",
       "3                   3          3      0.84       0.87  partly-cloudy-night   \n",
       "4                   3          1      0.90       1.61  partly-cloudy-night   \n",
       "5                   7          5      0.83       2.73               cloudy   \n",
       "6                   6          3      0.89       4.08               cloudy   \n",
       "7                   6          4      0.96       3.96    partly-cloudy-day   \n",
       "8                   8          5      0.88       4.64    partly-cloudy-day   \n",
       "9                   9          9      0.82       0.86    partly-cloudy-day   \n",
       "10                 14         14      0.67       4.15    partly-cloudy-day   \n",
       "11                 32         32      0.33       3.18            clear-day   \n",
       "12                 19         19      0.82       1.24  partly-cloudy-night   \n",
       "13                 29         31      0.63       5.11    partly-cloudy-day   \n",
       "14                 28         30      0.61       4.90            clear-day   \n",
       "15                 17         18      1.00       0.90          clear-night   \n",
       "16                 25         25      0.63       1.23            clear-day   \n",
       "17                 21         21      0.64       2.77               cloudy   \n",
       "18                 11         11      0.97       5.32               cloudy   \n",
       "19                 19         19      0.62       3.90    partly-cloudy-day   \n",
       "20                 19         19      0.62       3.51    partly-cloudy-day   \n",
       "21                 17         17      0.69       0.88    partly-cloudy-day   \n",
       "22                 20         20      0.59       1.19    partly-cloudy-day   \n",
       "23                 21         21      0.58       1.11    partly-cloudy-day   \n",
       "24                 16         16      0.73       3.01    partly-cloudy-day   \n",
       "25                 16         16      0.78       1.41               cloudy   \n",
       "26                 16         16      0.77       2.79    partly-cloudy-day   \n",
       "27                 15         15      0.83       1.73  partly-cloudy-night   \n",
       "28                 15         15      0.74       4.12    partly-cloudy-day   \n",
       "29                 16         16      0.66       5.75    partly-cloudy-day   \n",
       "..                ...        ...       ...        ...                  ...   \n",
       "648                -8        -14      0.61       2.98  partly-cloudy-night   \n",
       "649                -6        -12      0.64       4.80    partly-cloudy-day   \n",
       "650                 0         -5      0.87       5.62                 snow   \n",
       "651                 1         -4      0.82       6.16                 snow   \n",
       "652                 0         -5      0.95       4.84                 rain   \n",
       "653                 0         -4      0.77       2.65                 snow   \n",
       "654                -5         -9      0.72       2.09    partly-cloudy-day   \n",
       "655                -5         -8      0.64       1.83    partly-cloudy-day   \n",
       "656                -3         -8      0.90       3.99                  fog   \n",
       "657                -8         -8      0.78       1.17          clear-night   \n",
       "658                 2          0      0.42       2.56            clear-day   \n",
       "659                 3          2      0.30       1.47            clear-day   \n",
       "660                 4          0      0.95       4.77                 rain   \n",
       "661                 0         -2      0.65       1.63    partly-cloudy-day   \n",
       "662                 4          1      0.52       2.81    partly-cloudy-day   \n",
       "663                 5          2      0.62       4.56    partly-cloudy-day   \n",
       "664                 8          6      0.78       3.56    partly-cloudy-day   \n",
       "665                11         11      0.64       5.15    partly-cloudy-day   \n",
       "666                15         15      0.60       3.76    partly-cloudy-day   \n",
       "667                 9          8      0.32       2.22            clear-day   \n",
       "668                 9          7      0.28       3.69            clear-day   \n",
       "669                10         10      0.34       3.11            clear-day   \n",
       "670                 9          6      0.75       3.96    partly-cloudy-day   \n",
       "671                10          8      0.69       3.83    partly-cloudy-day   \n",
       "672                 5          2      0.45       2.84            clear-day   \n",
       "673                 6          4      0.39       2.57            clear-day   \n",
       "674                 7          3      0.62       5.37    partly-cloudy-day   \n",
       "675                 8          6      0.76       4.02    partly-cloudy-day   \n",
       "676                 9          7      0.68       4.28    partly-cloudy-day   \n",
       "677                 8          7      0.77       1.56    partly-cloudy-day   \n",
       "\n",
       "      weather_desc precipitation cloudness  \n",
       "0    Mostly Cloudy             0      0.78  \n",
       "1    Mostly Cloudy             0      0.66  \n",
       "2    Mostly Cloudy             0      0.86  \n",
       "3    Partly Cloudy             0      0.56  \n",
       "4    Partly Cloudy             0      0.31  \n",
       "5         Overcast             0      0.99  \n",
       "6         Overcast             0      0.98  \n",
       "7    Mostly Cloudy             0      0.89  \n",
       "8    Mostly Cloudy             0      0.84  \n",
       "9    Mostly Cloudy             0      0.69  \n",
       "10   Mostly Cloudy             0      0.75  \n",
       "11           Clear             1         0  \n",
       "12   Mostly Cloudy             1      0.75  \n",
       "13   Mostly Cloudy             1      0.75  \n",
       "14           Clear             1         0  \n",
       "15           Clear             1         0  \n",
       "16           Clear             1         0  \n",
       "17        Overcast             1         1  \n",
       "18        Overcast             1         1  \n",
       "19   Partly Cloudy             0      0.39  \n",
       "20   Partly Cloudy             0      0.49  \n",
       "21   Partly Cloudy             0      0.32  \n",
       "22   Partly Cloudy             0      0.58  \n",
       "23   Mostly Cloudy             0      0.75  \n",
       "24   Mostly Cloudy             0      0.75  \n",
       "25        Overcast             0      0.94  \n",
       "26   Mostly Cloudy             0      0.75  \n",
       "27   Mostly Cloudy             0      0.78  \n",
       "28   Mostly Cloudy             0      0.75  \n",
       "29   Mostly Cloudy             0      0.62  \n",
       "..             ...           ...       ...  \n",
       "648  Partly Cloudy             0      0.36  \n",
       "649  Partly Cloudy             0      0.43  \n",
       "650       Flurries             0      0.48  \n",
       "651       Flurries             0      0.41  \n",
       "652        Drizzle             0      0.44  \n",
       "653       Flurries             0      0.47  \n",
       "654  Partly Cloudy             0      0.43  \n",
       "655  Partly Cloudy             0      0.58  \n",
       "656          Foggy             0      0.48  \n",
       "657          Clear             0      0.15  \n",
       "658          Clear             0         0  \n",
       "659          Clear             0      0.17  \n",
       "660     Light Rain             0      0.45  \n",
       "661  Partly Cloudy             0      0.36  \n",
       "662  Partly Cloudy             0      0.25  \n",
       "663  Mostly Cloudy             0      0.65  \n",
       "664  Partly Cloudy             0      0.48  \n",
       "665  Partly Cloudy             0      0.32  \n",
       "666  Partly Cloudy             0      0.52  \n",
       "667          Clear             0      0.07  \n",
       "668          Clear             0      0.22  \n",
       "669          Clear             0       0.1  \n",
       "670  Partly Cloudy             0      0.48  \n",
       "671  Mostly Cloudy             0       0.6  \n",
       "672          Clear             0      0.21  \n",
       "673          Clear             0      0.18  \n",
       "674  Partly Cloudy             0      0.48  \n",
       "675  Mostly Cloudy             0       0.9  \n",
       "676  Mostly Cloudy             0      0.79  \n",
       "677  Mostly Cloudy             0      0.75  \n",
       "\n",
       "[678 rows x 46 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = pd.read_csv('C:/Users/Bangga Rakana Adian/Desktop/Kuliah/python/uber-rides-dataset.csv')\n",
    "data_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis & Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trip_completed_at         object\n",
       "trip_status               object\n",
       "ride_hailing_app          object\n",
       "trip_uid                  object\n",
       "driver_uid                object\n",
       "rider_uid                 object\n",
       "customer                  object\n",
       "trip_start_time           object\n",
       "dayornight                 int64\n",
       "trip_end_time             object\n",
       "trip_time                 object\n",
       "total_time                object\n",
       "wait_time                 object\n",
       "trip_type                 object\n",
       "surge_multiplier         float64\n",
       "vehicle_make_model        object\n",
       "vehicle_license_plate     object\n",
       "driver_name_en            object\n",
       "vehicle_make              object\n",
       "vehicle_model             object\n",
       "driver_gender             object\n",
       "driver_photo_url          object\n",
       "driver_phone_number       object\n",
       "pickup_lat               float64\n",
       "pickup_long              float64\n",
       "dropoff_lat              float64\n",
       "dropoff_long             float64\n",
       "trip_map_image_url        object\n",
       "trip_path_image_url       object\n",
       "city                      object\n",
       "country                   object\n",
       "trip_start_address        object\n",
       "trip_end_address          object\n",
       "rub_usd_exchange_rate    float64\n",
       "price_rub                 object\n",
       "price_usd                float64\n",
       "distance_kms             float64\n",
       "temperature_time          object\n",
       "temperature_value          int64\n",
       "feels_like                 int64\n",
       "humidity                 float64\n",
       "wind_speed               float64\n",
       "weather_main              object\n",
       "weather_desc              object\n",
       "precipitation             object\n",
       "cloudness                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dayornight</th>\n",
       "      <th>surge_multiplier</th>\n",
       "      <th>pickup_lat</th>\n",
       "      <th>pickup_long</th>\n",
       "      <th>dropoff_lat</th>\n",
       "      <th>dropoff_long</th>\n",
       "      <th>rub_usd_exchange_rate</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>distance_kms</th>\n",
       "      <th>temperature_value</th>\n",
       "      <th>feels_like</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>678.000000</td>\n",
       "      <td>643.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>678.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.941003</td>\n",
       "      <td>1.011353</td>\n",
       "      <td>59.756784</td>\n",
       "      <td>31.740062</td>\n",
       "      <td>59.758561</td>\n",
       "      <td>31.739366</td>\n",
       "      <td>60.517463</td>\n",
       "      <td>5.061593</td>\n",
       "      <td>10.057788</td>\n",
       "      <td>5.327434</td>\n",
       "      <td>3.002950</td>\n",
       "      <td>0.778083</td>\n",
       "      <td>3.541519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.730876</td>\n",
       "      <td>0.096085</td>\n",
       "      <td>0.800432</td>\n",
       "      <td>6.063872</td>\n",
       "      <td>0.799396</td>\n",
       "      <td>6.062101</td>\n",
       "      <td>4.526606</td>\n",
       "      <td>4.251843</td>\n",
       "      <td>8.735132</td>\n",
       "      <td>9.996551</td>\n",
       "      <td>11.912408</td>\n",
       "      <td>0.164886</td>\n",
       "      <td>1.737701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.605800</td>\n",
       "      <td>30.168447</td>\n",
       "      <td>55.599648</td>\n",
       "      <td>29.947507</td>\n",
       "      <td>49.260000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-22.000000</td>\n",
       "      <td>-29.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.932134</td>\n",
       "      <td>30.364931</td>\n",
       "      <td>59.933576</td>\n",
       "      <td>30.366456</td>\n",
       "      <td>57.230000</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>4.912500</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-5.750000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>2.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.941415</td>\n",
       "      <td>30.366456</td>\n",
       "      <td>59.941415</td>\n",
       "      <td>30.368724</td>\n",
       "      <td>59.010000</td>\n",
       "      <td>3.735000</td>\n",
       "      <td>7.290000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.960492</td>\n",
       "      <td>30.420179</td>\n",
       "      <td>59.961987</td>\n",
       "      <td>30.417860</td>\n",
       "      <td>64.637500</td>\n",
       "      <td>5.670000</td>\n",
       "      <td>11.665000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>4.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>60.126500</td>\n",
       "      <td>60.809913</td>\n",
       "      <td>60.126957</td>\n",
       "      <td>60.809916</td>\n",
       "      <td>79.360000</td>\n",
       "      <td>33.550000</td>\n",
       "      <td>46.740000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dayornight  surge_multiplier  pickup_lat  pickup_long  dropoff_lat  \\\n",
       "count  678.000000        643.000000  678.000000   678.000000   678.000000   \n",
       "mean     1.941003          1.011353   59.756784    31.740062    59.758561   \n",
       "std      0.730876          0.096085    0.800432     6.063872     0.799396   \n",
       "min      1.000000          1.000000   55.605800    30.168447    55.599648   \n",
       "25%      1.000000          1.000000   59.932134    30.364931    59.933576   \n",
       "50%      2.000000          1.000000   59.941415    30.366456    59.941415   \n",
       "75%      2.000000          1.000000   59.960492    30.420179    59.961987   \n",
       "max      3.000000          2.900000   60.126500    60.809913    60.126957   \n",
       "\n",
       "       dropoff_long  rub_usd_exchange_rate   price_usd  distance_kms  \\\n",
       "count    678.000000             678.000000  678.000000    678.000000   \n",
       "mean      31.739366              60.517463    5.061593     10.057788   \n",
       "std        6.062101               4.526606    4.251843      8.735132   \n",
       "min       29.947507              49.260000    0.840000      0.010000   \n",
       "25%       30.366456              57.230000    2.760000      4.912500   \n",
       "50%       30.368724              59.010000    3.735000      7.290000   \n",
       "75%       30.417860              64.637500    5.670000     11.665000   \n",
       "max       60.809916              79.360000   33.550000     46.740000   \n",
       "\n",
       "       temperature_value  feels_like    humidity  wind_speed  \n",
       "count         678.000000  678.000000  678.000000  678.000000  \n",
       "mean            5.327434    3.002950    0.778083    3.541519  \n",
       "std             9.996551   11.912408    0.164886    1.737701  \n",
       "min           -22.000000  -29.000000    0.250000    0.050000  \n",
       "25%            -2.000000   -5.750000    0.690000    2.350000  \n",
       "50%             3.000000    0.000000    0.820000    3.500000  \n",
       "75%            14.000000   14.000000    0.900000    4.660000  \n",
       "max            32.000000   32.000000    1.000000    8.670000  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some null values in 'surge_multiplier', so I need to clean it first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw['surge_multiplier'] = predictor.surge_multiplier.fillna(predictor.surge_multiplier.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = data_raw[['dayornight', 'surge_multiplier', 'pickup_long','rub_usd_exchange_rate', 'price_usd', 'distance_kms', 'temperature_value','feels_like','humidity','wind_speed']]\n",
    "target = data_raw['dropoff_long']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can disregard some unneeded features in the dataset by using f_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. \n",
    "A large p-value (> 0.05) indicates weak evidence against the null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39, 0.61, 0.  , 0.  , 0.12, 0.4 , 0.  , 0.  , 0.45, 0.66])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "p_val = f_regression(predictor, target)[1]\n",
    "p_val.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as we can see above, there are four features that have strong impact to the target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As 'rub_usd_exchange_rate' explains the country's currency exchange rate to USD, we know that it potentially matters to\n",
    "the increase of fuel prices and will affect to people's travelling distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1083dd358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['temperature_value'], 'r')\n",
    "plt.plot(data['feels_like'], 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of 'feels_like' and 'temperature_value' are very similar. In this case, it's really unnecessary to have them all in our dataset. Plus, it will be very difficult to survey how do poeple feel about the temperature using a large scale numbers! So I plan to drop 'feels_like' feature and put some new features intuitively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Saint Petersburg    640\n",
       "Ekaterinburg         28\n",
       "Moscow               10\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw['city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "city = data_raw['city'].replace(('Saint Petersburg', 'Ekaterinburg', 'Moscow'), (1,2,3))\n",
    "data_raw['City'] = city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's really interesting to know what relation between trip status and destinations so I have a plan to put it in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Completed    668\n",
       "Cancelled     10\n",
       "Name: trip_status, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw['trip_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip_status = data_raw['trip_status'].replace(('Completed', 'Cancelled'), (1,0))\n",
    "data_raw['trip_completeness'] = trip_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "partly-cloudy-day      281\n",
       "cloudy                 171\n",
       "partly-cloudy-night    106\n",
       "clear-day               57\n",
       "wind                    15\n",
       "rain                    14\n",
       "clear-night             12\n",
       "fog                     11\n",
       "snow                    11\n",
       "Name: weather_main, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw['weather_main'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_main = data_raw['weather_main'].replace(('partly-cloudy-day','cloudy','partly-cloudy-night','clear-day','wind','rain','clear-night','fog','snow'),(9,8,7,6,5,4,3,2,1))\n",
    "data_raw['weather'] = weather_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a101a43860>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u3jDqOntJcmqS7UkeTPJAkleOw1yMVaAzwgdSD9hi4wB4T1Vt7n5mR1di397N/N+/P2TS5uKQI8cBkzcXAK/q6j10idz7gDu6+bij2x53R44B5n+nDs3Fv42ssv59GLi1qs4BzmX+d2vkczFWgT7KB1IP0lHGMVGSbAQuBK7ttsOEzQU8dxyNeRPz8wATMh+TLskpwPl0z4Coqu90T3Mb+VyMVaDD6B5IPWhHjqOq7u4++kCSe5NclWTc/7D71cB7gYPd9nomcC547jgOmaS5gPmTgtuS7EiyrWs7s6qeBOhenzey6vqz0BgAfq+bi38c92Uj4GeAOeC6bhnv2iQnMQZzMXaBfiwPpB4nR44jyc8BVwLnAC8HTgeuGGGJR5XkjcCeqtpxePMCXcd6LhYZB0zQXBxma1WdB/wy8M4k54+6oGVYaAwfAZ7P/PLkk8CHRlhfP1YD5wEfqaqXAf/LmCx1jV2gH7KcB1KPo8PG8fqqerJbjjkAXMcInvi0BFuBi5LsBG5gfqnlaiZvLp4zjiSfmLC5AKCqnuhe9wA3MV/z7iQbALrXPaOrsLeFxlBVu7sToIPAPzD+c7EL2HXYf3VvZz7gRz4XYxXo4/JA6mO1yDgePGyyw/z62n2jq/LoqurKqtpYVZuAS4E7q+qtTNhcLDKOt03SXAAkOSnJyYfeA69jvubPMj8PMObzsdgYDs1F51cY87moqm8CjyV5Udf0auBrjMFc9POQ6JU0Ng+kPkaLjePOJFPML13MAm8fZZHLdAWTNReL+eSEzcWZwE3z3z+sBj5VVbcm+TJwY5LLgUeBN4+wxl4WG8PHu8tGC9gJ/O7oSuzbu5j/HToeeBj4Tbp/10c5F94pKkmNGKslF0nS8hnoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ14v8B9jwNOcuzOrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a107099940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=dataset['pickup_long'], y=dataset['dropoff_long'], s=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total distances:  6819.18\n",
      "Max distances:  46.74\n",
      "Min distances:  0.01\n",
      "Average distances:  10.057787610619469\n"
     ]
    }
   ],
   "source": [
    "print('Total distances: ', data_raw['distance_kms'].sum())\n",
    "print('Max distances: ', data_raw['distance_kms'].max())\n",
    "print('Min distances: ', data_raw['distance_kms'].min())\n",
    "print('Average distances: ', data_raw['distance_kms'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many people travelled not so far from the pickup point. As we can see, the average of travel distance is ~10 kilometers. \n",
    "It's safe to say people still travel in the same city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_long</th>\n",
       "      <th>City</th>\n",
       "      <th>weather</th>\n",
       "      <th>trip_completeness</th>\n",
       "      <th>temperature_value</th>\n",
       "      <th>rub_usd_exchange_rate</th>\n",
       "      <th>dropoff_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.329826</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>51.28</td>\n",
       "      <td>30.307655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.307313</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>51.28</td>\n",
       "      <td>30.329495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.329416</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>49.50</td>\n",
       "      <td>30.387561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.311159</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>49.53</td>\n",
       "      <td>30.308489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.308553</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>49.53</td>\n",
       "      <td>30.329402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.321773</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>49.26</td>\n",
       "      <td>30.388147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.387829</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>49.26</td>\n",
       "      <td>30.329518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.330248</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>49.55</td>\n",
       "      <td>30.359076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30.356079</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>49.55</td>\n",
       "      <td>30.388656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30.388166</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>49.55</td>\n",
       "      <td>30.329493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.321134</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>50.11</td>\n",
       "      <td>30.388775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60.805972</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>52.25</td>\n",
       "      <td>60.614053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60.803867</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>53.49</td>\n",
       "      <td>60.614159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60.612997</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>54.34</td>\n",
       "      <td>60.606300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60.606077</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>54.34</td>\n",
       "      <td>60.606094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60.612907</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>56.05</td>\n",
       "      <td>60.606187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60.612903</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>56.30</td>\n",
       "      <td>60.597542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60.612885</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>56.30</td>\n",
       "      <td>60.634999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60.614292</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>56.28</td>\n",
       "      <td>60.801553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30.339049</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>55.23</td>\n",
       "      <td>30.303298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30.310537</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>55.23</td>\n",
       "      <td>30.434048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30.273236</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.273236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.356715</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.427891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30.409457</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.634919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30.337983</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.274495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>30.338072</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.339290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30.338298</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.345696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30.345892</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.337948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30.407802</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>54.97</td>\n",
       "      <td>30.335884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.274062</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>54.97</td>\n",
       "      <td>30.337976</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>30.323961</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>-8</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>-6</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.421642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.57</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>57.57</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>30.417444</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.57</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.54</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>57.26</td>\n",
       "      <td>30.450686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>30.450660</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>57.26</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>56.94</td>\n",
       "      <td>30.356570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>30.356365</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-8</td>\n",
       "      <td>57.23</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>57.05</td>\n",
       "      <td>30.356867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>30.357698</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>57.05</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>30.419800</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>57.36</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.476147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>30.358252</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>30.323961</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>57.67</td>\n",
       "      <td>30.514868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>30.511703</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>57.67</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>59.49</td>\n",
       "      <td>30.267840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>62.05</td>\n",
       "      <td>30.345533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>30.345686</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>62.05</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>30.437160</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>62.02</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>30.467712</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>61.05</td>\n",
       "      <td>30.438965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>30.440392</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>61.05</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>30.354136</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>61.82</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>30.416977</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>61.82</td>\n",
       "      <td>30.474258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>61.54</td>\n",
       "      <td>30.386566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>62.68</td>\n",
       "      <td>30.450724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>30.451020</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>62.68</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>62.13</td>\n",
       "      <td>30.356570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>678 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pickup_long  City  weather  trip_completeness  temperature_value  \\\n",
       "0      30.329826     1        9                  1                 12   \n",
       "1      30.307313     1        9                  1                 10   \n",
       "2      30.329416     1        9                  1                 14   \n",
       "3      30.311159     1        7                  1                  3   \n",
       "4      30.308553     1        7                  1                  3   \n",
       "5      30.321773     1        8                  1                  7   \n",
       "6      30.387829     1        8                  1                  6   \n",
       "7      30.330248     1        9                  1                  6   \n",
       "8      30.356079     1        9                  1                  8   \n",
       "9      30.388166     1        9                  1                  9   \n",
       "10     30.321134     1        9                  1                 14   \n",
       "11     60.805972     2        6                  1                 32   \n",
       "12     60.803867     2        7                  1                 19   \n",
       "13     60.612997     2        9                  1                 29   \n",
       "14     60.606077     2        6                  1                 28   \n",
       "15     60.612907     2        3                  1                 17   \n",
       "16     60.612903     2        6                  1                 25   \n",
       "17     60.612885     2        8                  1                 21   \n",
       "18     60.614292     2        8                  1                 11   \n",
       "19     30.339049     1        9                  1                 19   \n",
       "20     30.310537     1        9                  1                 19   \n",
       "21     30.273236     1        9                  0                 17   \n",
       "22     30.356715     1        9                  1                 20   \n",
       "23     30.409457     1        9                  1                 21   \n",
       "24     30.337983     1        9                  1                 16   \n",
       "25     30.338072     1        8                  1                 16   \n",
       "26     30.338298     1        9                  1                 16   \n",
       "27     30.345892     1        7                  1                 15   \n",
       "28     30.407802     1        9                  1                 15   \n",
       "29     30.274062     1        9                  1                 16   \n",
       "..           ...   ...      ...                ...                ...   \n",
       "648    30.323961     1        7                  1                 -8   \n",
       "649    30.366456     1        9                  1                 -6   \n",
       "650    30.366456     1        1                  1                  0   \n",
       "651    30.366456     1        1                  1                  1   \n",
       "652    30.417444     1        4                  1                  0   \n",
       "653    30.366456     1        1                  1                  0   \n",
       "654    30.366456     1        9                  1                 -5   \n",
       "655    30.450660     1        9                  1                 -5   \n",
       "656    30.366456     1        2                  1                 -3   \n",
       "657    30.356365     1        3                  1                 -8   \n",
       "658    30.366456     1        6                  1                  2   \n",
       "659    30.357698     1        6                  1                  3   \n",
       "660    30.419800     1        4                  1                  4   \n",
       "661    30.366456     1        9                  1                  0   \n",
       "662    30.358252     1        9                  1                  4   \n",
       "663    30.323961     1        9                  1                  5   \n",
       "664    30.366456     1        9                  1                  8   \n",
       "665    30.511703     1        9                  1                 11   \n",
       "666    30.366456     1        9                  1                 15   \n",
       "667    30.366456     1        6                  1                  9   \n",
       "668    30.345686     1        6                  1                  9   \n",
       "669    30.437160     1        6                  1                 10   \n",
       "670    30.467712     1        9                  1                  9   \n",
       "671    30.440392     1        9                  1                 10   \n",
       "672    30.354136     1        6                  1                  5   \n",
       "673    30.416977     1        6                  1                  6   \n",
       "674    30.366456     1        9                  1                  7   \n",
       "675    30.366456     1        9                  1                  8   \n",
       "676    30.451020     1        9                  1                  9   \n",
       "677    30.366456     1        9                  1                  8   \n",
       "\n",
       "     rub_usd_exchange_rate  dropoff_long  \n",
       "0                    51.28     30.307655  \n",
       "1                    51.28     30.329495  \n",
       "2                    49.50     30.387561  \n",
       "3                    49.53     30.308489  \n",
       "4                    49.53     30.329402  \n",
       "5                    49.26     30.388147  \n",
       "6                    49.26     30.329518  \n",
       "7                    49.55     30.359076  \n",
       "8                    49.55     30.388656  \n",
       "9                    49.55     30.329493  \n",
       "10                   50.11     30.388775  \n",
       "11                   52.25     60.614053  \n",
       "12                   53.49     60.614159  \n",
       "13                   54.34     60.606300  \n",
       "14                   54.34     60.606094  \n",
       "15                   56.05     60.606187  \n",
       "16                   56.30     60.597542  \n",
       "17                   56.30     60.634999  \n",
       "18                   56.28     60.801553  \n",
       "19                   55.23     30.303298  \n",
       "20                   55.23     30.434048  \n",
       "21                   55.17     30.273236  \n",
       "22                   55.17     30.427891  \n",
       "23                   55.17     30.634919  \n",
       "24                   55.17     30.274495  \n",
       "25                   55.17     30.339290  \n",
       "26                   55.17     30.345696  \n",
       "27                   55.17     30.337948  \n",
       "28                   54.97     30.335884  \n",
       "29                   54.97     30.337976  \n",
       "..                     ...           ...  \n",
       "648                  57.52     30.366456  \n",
       "649                  57.52     30.421642  \n",
       "650                  57.57     30.417860  \n",
       "651                  57.57     30.417860  \n",
       "652                  57.57     30.366456  \n",
       "653                  57.54     30.417860  \n",
       "654                  57.26     30.450686  \n",
       "655                  57.26     30.366456  \n",
       "656                  56.94     30.356570  \n",
       "657                  57.23     30.366456  \n",
       "658                  57.05     30.356867  \n",
       "659                  57.05     30.366456  \n",
       "660                  57.36     30.366456  \n",
       "661                  57.52     30.476147  \n",
       "662                  57.52     30.417860  \n",
       "663                  57.52     30.366456  \n",
       "664                  57.67     30.514868  \n",
       "665                  57.67     30.366456  \n",
       "666                  59.49     30.267840  \n",
       "667                  62.05     30.345533  \n",
       "668                  62.05     30.417860  \n",
       "669                  62.02     30.366456  \n",
       "670                  61.05     30.438965  \n",
       "671                  61.05     30.366456  \n",
       "672                  61.82     30.417860  \n",
       "673                  61.82     30.474258  \n",
       "674                  61.54     30.386566  \n",
       "675                  62.68     30.450724  \n",
       "676                  62.68     30.366456  \n",
       "677                  62.13     30.356570  \n",
       "\n",
       "[678 rows x 7 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = data_raw[['pickup_long','City','weather','trip_completeness','temperature_value','rub_usd_exchange_rate','dropoff_long']]\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_long              float64\n",
       "City                       int64\n",
       "weather                    int64\n",
       "trip_completeness          int64\n",
       "temperature_value          int64\n",
       "rub_usd_exchange_rate    float64\n",
       "dropoff_long             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm checking affectness of the features for my lastest dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.  , 0.  , 0.47, 0.  , 0.  ])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor2 = data_raw[['pickup_long','City','weather','trip_completeness','temperature_value','rub_usd_exchange_rate']]\n",
    "target2 = data_raw['dropoff_long']\n",
    "\n",
    "f_regression(predictor2, target2)[1].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this point, I will put off the 'trip_completeness' since it's unneeded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there it is: An interpretable and light dataset to be putted in to my prototype app is shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_long</th>\n",
       "      <th>City</th>\n",
       "      <th>weather</th>\n",
       "      <th>temperature_value</th>\n",
       "      <th>rub_usd_exchange_rate</th>\n",
       "      <th>dropoff_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.329826</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>51.28</td>\n",
       "      <td>30.307655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.307313</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>51.28</td>\n",
       "      <td>30.329495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.329416</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>49.50</td>\n",
       "      <td>30.387561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.311159</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>49.53</td>\n",
       "      <td>30.308489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.308553</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>49.53</td>\n",
       "      <td>30.329402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.321773</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>49.26</td>\n",
       "      <td>30.388147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.387829</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>49.26</td>\n",
       "      <td>30.329518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.330248</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>49.55</td>\n",
       "      <td>30.359076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30.356079</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>49.55</td>\n",
       "      <td>30.388656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30.388166</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>49.55</td>\n",
       "      <td>30.329493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.321134</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>50.11</td>\n",
       "      <td>30.388775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60.805972</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>52.25</td>\n",
       "      <td>60.614053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60.803867</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>53.49</td>\n",
       "      <td>60.614159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60.612997</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>54.34</td>\n",
       "      <td>60.606300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60.606077</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>54.34</td>\n",
       "      <td>60.606094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60.612907</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>56.05</td>\n",
       "      <td>60.606187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60.612903</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>56.30</td>\n",
       "      <td>60.597542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60.612885</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>56.30</td>\n",
       "      <td>60.634999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60.614292</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>56.28</td>\n",
       "      <td>60.801553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30.339049</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>55.23</td>\n",
       "      <td>30.303298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30.310537</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>55.23</td>\n",
       "      <td>30.434048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30.273236</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.273236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.356715</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.427891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30.409457</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.634919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30.337983</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.274495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>30.338072</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.339290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30.338298</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.345696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30.345892</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>55.17</td>\n",
       "      <td>30.337948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30.407802</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>54.97</td>\n",
       "      <td>30.335884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.274062</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>54.97</td>\n",
       "      <td>30.337976</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>30.323961</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>-8</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-6</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.421642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.57</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>57.57</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>30.417444</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>57.57</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.54</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-5</td>\n",
       "      <td>57.26</td>\n",
       "      <td>30.450686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>30.450660</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>-5</td>\n",
       "      <td>57.26</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>56.94</td>\n",
       "      <td>30.356570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>30.356365</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-8</td>\n",
       "      <td>57.23</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>57.05</td>\n",
       "      <td>30.356867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>30.357698</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>57.05</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>30.419800</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>57.36</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.476147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>30.358252</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>30.323961</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>57.52</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>57.67</td>\n",
       "      <td>30.514868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>30.511703</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>57.67</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>59.49</td>\n",
       "      <td>30.267840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>62.05</td>\n",
       "      <td>30.345533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>30.345686</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>62.05</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>30.437160</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>62.02</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>30.467712</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>61.05</td>\n",
       "      <td>30.438965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>30.440392</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>61.05</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>30.354136</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>61.82</td>\n",
       "      <td>30.417860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>30.416977</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>61.82</td>\n",
       "      <td>30.474258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>61.54</td>\n",
       "      <td>30.386566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>62.68</td>\n",
       "      <td>30.450724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>30.451020</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>62.68</td>\n",
       "      <td>30.366456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>30.366456</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>62.13</td>\n",
       "      <td>30.356570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>678 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pickup_long  City  weather  temperature_value  rub_usd_exchange_rate  \\\n",
       "0      30.329826     1        9                 12                  51.28   \n",
       "1      30.307313     1        9                 10                  51.28   \n",
       "2      30.329416     1        9                 14                  49.50   \n",
       "3      30.311159     1        7                  3                  49.53   \n",
       "4      30.308553     1        7                  3                  49.53   \n",
       "5      30.321773     1        8                  7                  49.26   \n",
       "6      30.387829     1        8                  6                  49.26   \n",
       "7      30.330248     1        9                  6                  49.55   \n",
       "8      30.356079     1        9                  8                  49.55   \n",
       "9      30.388166     1        9                  9                  49.55   \n",
       "10     30.321134     1        9                 14                  50.11   \n",
       "11     60.805972     2        6                 32                  52.25   \n",
       "12     60.803867     2        7                 19                  53.49   \n",
       "13     60.612997     2        9                 29                  54.34   \n",
       "14     60.606077     2        6                 28                  54.34   \n",
       "15     60.612907     2        3                 17                  56.05   \n",
       "16     60.612903     2        6                 25                  56.30   \n",
       "17     60.612885     2        8                 21                  56.30   \n",
       "18     60.614292     2        8                 11                  56.28   \n",
       "19     30.339049     1        9                 19                  55.23   \n",
       "20     30.310537     1        9                 19                  55.23   \n",
       "21     30.273236     1        9                 17                  55.17   \n",
       "22     30.356715     1        9                 20                  55.17   \n",
       "23     30.409457     1        9                 21                  55.17   \n",
       "24     30.337983     1        9                 16                  55.17   \n",
       "25     30.338072     1        8                 16                  55.17   \n",
       "26     30.338298     1        9                 16                  55.17   \n",
       "27     30.345892     1        7                 15                  55.17   \n",
       "28     30.407802     1        9                 15                  54.97   \n",
       "29     30.274062     1        9                 16                  54.97   \n",
       "..           ...   ...      ...                ...                    ...   \n",
       "648    30.323961     1        7                 -8                  57.52   \n",
       "649    30.366456     1        9                 -6                  57.52   \n",
       "650    30.366456     1        1                  0                  57.57   \n",
       "651    30.366456     1        1                  1                  57.57   \n",
       "652    30.417444     1        4                  0                  57.57   \n",
       "653    30.366456     1        1                  0                  57.54   \n",
       "654    30.366456     1        9                 -5                  57.26   \n",
       "655    30.450660     1        9                 -5                  57.26   \n",
       "656    30.366456     1        2                 -3                  56.94   \n",
       "657    30.356365     1        3                 -8                  57.23   \n",
       "658    30.366456     1        6                  2                  57.05   \n",
       "659    30.357698     1        6                  3                  57.05   \n",
       "660    30.419800     1        4                  4                  57.36   \n",
       "661    30.366456     1        9                  0                  57.52   \n",
       "662    30.358252     1        9                  4                  57.52   \n",
       "663    30.323961     1        9                  5                  57.52   \n",
       "664    30.366456     1        9                  8                  57.67   \n",
       "665    30.511703     1        9                 11                  57.67   \n",
       "666    30.366456     1        9                 15                  59.49   \n",
       "667    30.366456     1        6                  9                  62.05   \n",
       "668    30.345686     1        6                  9                  62.05   \n",
       "669    30.437160     1        6                 10                  62.02   \n",
       "670    30.467712     1        9                  9                  61.05   \n",
       "671    30.440392     1        9                 10                  61.05   \n",
       "672    30.354136     1        6                  5                  61.82   \n",
       "673    30.416977     1        6                  6                  61.82   \n",
       "674    30.366456     1        9                  7                  61.54   \n",
       "675    30.366456     1        9                  8                  62.68   \n",
       "676    30.451020     1        9                  9                  62.68   \n",
       "677    30.366456     1        9                  8                  62.13   \n",
       "\n",
       "     dropoff_long  \n",
       "0       30.307655  \n",
       "1       30.329495  \n",
       "2       30.387561  \n",
       "3       30.308489  \n",
       "4       30.329402  \n",
       "5       30.388147  \n",
       "6       30.329518  \n",
       "7       30.359076  \n",
       "8       30.388656  \n",
       "9       30.329493  \n",
       "10      30.388775  \n",
       "11      60.614053  \n",
       "12      60.614159  \n",
       "13      60.606300  \n",
       "14      60.606094  \n",
       "15      60.606187  \n",
       "16      60.597542  \n",
       "17      60.634999  \n",
       "18      60.801553  \n",
       "19      30.303298  \n",
       "20      30.434048  \n",
       "21      30.273236  \n",
       "22      30.427891  \n",
       "23      30.634919  \n",
       "24      30.274495  \n",
       "25      30.339290  \n",
       "26      30.345696  \n",
       "27      30.337948  \n",
       "28      30.335884  \n",
       "29      30.337976  \n",
       "..            ...  \n",
       "648     30.366456  \n",
       "649     30.421642  \n",
       "650     30.417860  \n",
       "651     30.417860  \n",
       "652     30.366456  \n",
       "653     30.417860  \n",
       "654     30.450686  \n",
       "655     30.366456  \n",
       "656     30.356570  \n",
       "657     30.366456  \n",
       "658     30.356867  \n",
       "659     30.366456  \n",
       "660     30.366456  \n",
       "661     30.476147  \n",
       "662     30.417860  \n",
       "663     30.366456  \n",
       "664     30.514868  \n",
       "665     30.366456  \n",
       "666     30.267840  \n",
       "667     30.345533  \n",
       "668     30.417860  \n",
       "669     30.366456  \n",
       "670     30.438965  \n",
       "671     30.366456  \n",
       "672     30.417860  \n",
       "673     30.474258  \n",
       "674     30.386566  \n",
       "675     30.450724  \n",
       "676     30.366456  \n",
       "677     30.356570  \n",
       "\n",
       "[678 rows x 6 columns]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data_raw[['pickup_long','City','weather','temperature_value','rub_usd_exchange_rate', 'dropoff_long']]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = data.values\n",
    "X = array[:, 0:5]\n",
    "Y = array[:, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(678, 5)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 5\n",
    "test_size = 0.7\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [ 9.98334984e-01  3.44054310e-02  2.14458269e-03 -6.34296789e-05\n",
      " -6.27260252e-04]\n",
      "Intercept:  0.035952371526782656\n",
      "Adjusted R Square: 99.983%\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "Y_pred = model.predict(X_test)\n",
    "\n",
    "#The coefficients\n",
    "print('Coefficients: ', model.coef_)\n",
    "# The intercept\n",
    "print('Intercept: ', model.intercept_)\n",
    "\n",
    "#The adjusted r square\n",
    "n = X.shape[0]\n",
    "p = X.shape[1]\n",
    "r_square = model.score(X_train, Y_train)\n",
    "\n",
    "adj_r2 = 1-((1-r_square)*(n-1)/(n-p-1))\n",
    "\n",
    "print('Adjusted R Square: {}%'.format(adj_r2.round(5)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the average of people's travel distances are not that far,  the model must be fully-fit.\n",
    "I got 99.988% adjusted R2 Score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted R Square:99.211%\n"
     ]
    }
   ],
   "source": [
    "x_sample = X[600:610]\n",
    "y_sample = Y[600:610]\n",
    "y2 = model.predict(x_sample)\n",
    "\n",
    "#The adjusted r square\n",
    "n2 = x_sample.shape[0]\n",
    "p2 = x_sample.shape[1]\n",
    "r_square2 = model.score(x_sample, y_sample)\n",
    "\n",
    "adj_rsquare = 1-((1-r_square2)*(n2-1)/(n2-p2-1))\n",
    "\n",
    "print('Adjusted R Square:{}%'.format(adj_rsquare.round(5)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.63732  , 37.271233 , 30.3664563, 30.40226  , 30.41786  ,\n",
       "       30.359217 , 30.40226  , 30.3664563, 30.40226  , 30.3664563])"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.34839913, 37.69328037, 30.27216341, 30.3694573 , 30.40359764,\n",
       "       30.41552974, 30.35790385, 30.40121561, 30.36532635, 30.40130911])"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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