Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Citibike Case Analysis: Vinit Shah, Analytics Consultant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background, Context, and Objective\n",
    "**Client:** Mayor of New York City, Bill de Blasio<br />\n",
    "**Objective:** Help the mayor get a better understanding of citibike ridership by creating an operating report for 2017.\n",
    "\n",
    "**Ask:** <br />\n",
    "    1. Top 5 stations with the most starts (showing # of starts)\n",
    "    2. Trip duration by user type\n",
    "    3. Most popular trips based on start station and stop station)\n",
    "    4. Rider performance by Gender and Age based on avg trip distance (station to station), \n",
    "       median speed (trip duration / distance traveled)\n",
    "    5. What is the busiest bike in NYC in 2017? How many times was it used? How many minutes was it in use?\n",
    "    6. A model that can predict how long a trip will take given a starting point and destination. \n",
    "\n",
    "First, let's minimize the work and load in the data set. Aggregate all the months and save the data as a csv to not require this to be done in the future.\n",
    "\n",
    "The column names have spaces in them, would be great to remove them for working purposes. However, not necessary. If I was working on a team or a long term project, I would ocnfigure column names a little bit differently to make them easier to work with.\n",
    "    \n",
    "The dataset is massive, ~16mil rows. BigData tools would be helpful, however, most require you to pay or have an enterprise license or a limited trial. Additionally, the data is very dirty. Different files have different column names, need to account for this. Mayor de Blasio doesn't have a technical background. The graphs here are as simple yet informative as possible. I could've made more complicated plots, however, they would not be as informative for the mayor.\n",
    "\n",
    "- Lastly, my analysis tries best to follow the crisp-dm methodology outlined below. "
   ]
  },
  {
   "attachments": {
    "CRISP-DM-methodology.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![CRISP-DM-methodology.png](attachment:CRISP-DM-methodology.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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  {
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  {
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################### Load Libraries and Data ######################\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import *\n",
    "import seaborn as sns\n",
    "from matplotlib import rcParams\n",
    "import datetime as dt\n",
    "import math\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: Top 5 Stations \n",
    "Let's check if there's any noise or cleanup which needs to be done before creating the chart. <br />\n",
    "1. Any missing values?<br />\n",
    "    - Mostly for Birth year and a few for User Type. We can ignore these for now and deal with them later.<br />\n",
    "2. Let's get the data in the right format<br />\n",
    "    - Trip Duration - Int<br />\n",
    "    - Start Time - DateTime<br />\n",
    "    - Stop Time - DateTime<br />\n",
    "    - Start Station ID - Categorical<br />\n",
    "    - Start Station Name - Categorical<br />\n",
    "    - User Type - Categorical<br />\n",
    "    - Birth Year - Ordinal<br />\n",
    "    - Gender - Categorical<br />\n",
    "3. Deal with trips which lasted less than 1.5 minute (90 seconds). If so, in the ideal world, we should not include this start, we may double count. If a bike is broken, a user will dock it again within a minute or two and pick-up another one.<br />\n",
    "   - Would be ideal to not include any starts where a tip lasted less than 90 seconds *and* the start station = end station.<br /> \n",
    "4. Anomalies such as theft and broken docks shouldn't matter for this metric and can be dealth with later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The following code was used to load the Citibike data in one go. If you plan to use the same code, please make sure the only csv files in the working directory are the relevant citibike data files.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set working directory to correct folder, or add path\n",
    "#Minimize work to load data. Can be done in other ways as well. There may be a more effecient way to do this. Haven't explored this too deeply. \n",
    "\n",
    "\n",
    "#****************************************************************************************\n",
    "#Download the files\n",
    "#Comment these after the first run \n",
    "# !curl -O \"https://s3.amazonaws.com/tripdata/2017[01-12]-citibike-tripdata.csv.zip\"\n",
    "# !unzip '*.zip'\n",
    "# !rm *.zip\n",
    "#****************************************************************************************\n",
    "# files = !ls *.csv #For Ipython only\n",
    "# df = pd.concat([pd.read_csv(f, header=None, skiprows=1,low_memory=False) for f in files], keys=None) \n",
    "# print(files)\n",
    "#****************************************************************************************\n",
    "\n",
    "#Let's see what we're working with\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(\"Files/NYC-BikeShare-2015-2017-combined.csv\")\n",
    "#df=df_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Trip Duration           Start Time            Stop Time  \\\n",
       "0           0            376  2015-10-01 00:16:26  2015-10-01 00:22:42   \n",
       "1           1            739  2015-10-01 00:27:12  2015-10-01 00:39:32   \n",
       "2           2           2714  2015-10-01 00:32:46  2015-10-01 01:18:01   \n",
       "3           3            275  2015-10-01 00:34:31  2015-10-01 00:39:06   \n",
       "4           4            561  2015-10-01 00:40:12  2015-10-01 00:49:33   \n",
       "\n",
       "   Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0              3212    Christ Hospital               40.734786   \n",
       "1              3207        Oakland Ave               40.737604   \n",
       "2              3193       Lincoln Park               40.724605   \n",
       "3              3199       Newport Pkwy               40.728745   \n",
       "4              3183     Exchange Place               40.716247   \n",
       "\n",
       "   Start Station Longitude  End Station ID    End Station Name  \\\n",
       "0               -74.050444            3207         Oakland Ave   \n",
       "1               -74.052478            3212     Christ Hospital   \n",
       "2               -74.078406            3193        Lincoln Park   \n",
       "3               -74.032108            3187           Warren St   \n",
       "4               -74.033459            3192  Liberty Light Rail   \n",
       "\n",
       "   End Station Latitude  End Station Longitude  Bike ID   User Type  \\\n",
       "0             40.737604             -74.052478    24470  Subscriber   \n",
       "1             40.734786             -74.050444    24481  Subscriber   \n",
       "2             40.724605             -74.078406    24628  Subscriber   \n",
       "3             40.721124             -74.038051    24613  Subscriber   \n",
       "4             40.711242             -74.055701    24668    Customer   \n",
       "\n",
       "   Birth Year  Gender  Trip_Duration_in_min  \n",
       "0      1960.0       1                     6  \n",
       "1      1960.0       1                    12  \n",
       "2      1983.0       1                    45  \n",
       "3      1975.0       1                     5  \n",
       "4      1984.0       0                     9  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>7.355020e+05</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "      <td>735502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10582.401084</td>\n",
       "      <td>9.343125e+02</td>\n",
       "      <td>3206.764183</td>\n",
       "      <td>40.723125</td>\n",
       "      <td>-74.046412</td>\n",
       "      <td>3203.387325</td>\n",
       "      <td>40.722371</td>\n",
       "      <td>-74.045415</td>\n",
       "      <td>24914.009133</td>\n",
       "      <td>1979.544239</td>\n",
       "      <td>1.123668</td>\n",
       "      <td>15.570669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>7679.871929</td>\n",
       "      <td>4.063878e+04</td>\n",
       "      <td>26.546683</td>\n",
       "      <td>0.008121</td>\n",
       "      <td>0.011191</td>\n",
       "      <td>64.358120</td>\n",
       "      <td>0.095296</td>\n",
       "      <td>0.173044</td>\n",
       "      <td>753.768581</td>\n",
       "      <td>9.371192</td>\n",
       "      <td>0.519694</td>\n",
       "      <td>677.312437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>3183.000000</td>\n",
       "      <td>40.692640</td>\n",
       "      <td>-74.096937</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.096937</td>\n",
       "      <td>14552.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>4484.000000</td>\n",
       "      <td>2.460000e+02</td>\n",
       "      <td>3186.000000</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.050656</td>\n",
       "      <td>3186.000000</td>\n",
       "      <td>40.716540</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24486.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>9156.000000</td>\n",
       "      <td>3.830000e+02</td>\n",
       "      <td>3202.000000</td>\n",
       "      <td>40.721525</td>\n",
       "      <td>-74.044247</td>\n",
       "      <td>3199.000000</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>24602.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>15003.000000</td>\n",
       "      <td>6.520000e+02</td>\n",
       "      <td>3211.000000</td>\n",
       "      <td>40.727596</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>3211.000000</td>\n",
       "      <td>40.727224</td>\n",
       "      <td>-74.036486</td>\n",
       "      <td>24711.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>34148.000000</td>\n",
       "      <td>2.026021e+07</td>\n",
       "      <td>3426.000000</td>\n",
       "      <td>40.752559</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3442.000000</td>\n",
       "      <td>40.801343</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29296.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>337670.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count  735502.000000   7.355020e+05     735502.000000           735502.000000   \n",
       "mean    10582.401084   9.343125e+02       3206.764183               40.723125   \n",
       "std      7679.871929   4.063878e+04         26.546683                0.008121   \n",
       "min         0.000000   6.100000e+01       3183.000000               40.692640   \n",
       "25%      4484.000000   2.460000e+02       3186.000000               40.717732   \n",
       "50%      9156.000000   3.830000e+02       3202.000000               40.721525   \n",
       "75%     15003.000000   6.520000e+02       3211.000000               40.727596   \n",
       "max     34148.000000   2.026021e+07       3426.000000               40.752559   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count            735502.000000   735502.000000         735502.000000   \n",
       "mean                -74.046412     3203.387325             40.722371   \n",
       "std                   0.011191       64.358120              0.095296   \n",
       "min                 -74.096937      147.000000              0.000000   \n",
       "25%                 -74.050656     3186.000000             40.716540   \n",
       "50%                 -74.044247     3199.000000             40.721124   \n",
       "75%                 -74.038051     3211.000000             40.727224   \n",
       "max                 -74.032108     3442.000000             40.801343   \n",
       "\n",
       "       End Station Longitude        Bike ID     Birth Year         Gender  \\\n",
       "count          735502.000000  735502.000000  735502.000000  735502.000000   \n",
       "mean              -74.045415   24914.009133    1979.544239       1.123668   \n",
       "std                 0.173044     753.768581       9.371192       0.519694   \n",
       "min               -74.096937   14552.000000    1900.000000       0.000000   \n",
       "25%               -74.050444   24486.000000    1975.000000       1.000000   \n",
       "50%               -74.043845   24602.000000    1982.000000       1.000000   \n",
       "75%               -74.036486   24711.000000    1986.000000       1.000000   \n",
       "max                 0.000000   29296.000000    2000.000000       2.000000   \n",
       "\n",
       "       Trip_Duration_in_min  \n",
       "count         735502.000000  \n",
       "mean              15.570669  \n",
       "std              677.312437  \n",
       "min                1.000000  \n",
       "25%                4.000000  \n",
       "50%                6.000000  \n",
       "75%               11.000000  \n",
       "max           337670.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "#df.describe(include = all)\n",
    "#df.dtypes\n",
    "#Scrappy way to get column names in the header.\n",
    "# df2 = pd.read_csv('Files/NYC-BikeShare-2015-2017-combined.csv')\n",
    "# df.columns = df2.columns[1:16]\n",
    "# del[df2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Trip Duration', 'Start Time', 'Stop Time', 'Start Station ID',\n",
      "       'Start Station Name', 'Start Station Latitude',\n",
      "       'Start Station Longitude', 'End Station ID', 'End Station Name',\n",
      "       'End Station Latitude', 'End Station Longitude', 'Bike ID', 'User Type',\n",
      "       'Birth Year', 'Gender', 'Trip_Duration_in_min'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trip Duration           Start Time            Stop Time  Start Station ID  \\\n",
       "0            376  2015-10-01 00:16:26  2015-10-01 00:22:42              3212   \n",
       "1            739  2015-10-01 00:27:12  2015-10-01 00:39:32              3207   \n",
       "2           2714  2015-10-01 00:32:46  2015-10-01 01:18:01              3193   \n",
       "3            275  2015-10-01 00:34:31  2015-10-01 00:39:06              3199   \n",
       "4            561  2015-10-01 00:40:12  2015-10-01 00:49:33              3183   \n",
       "\n",
       "  Start Station Name  Start Station Latitude  Start Station Longitude  \\\n",
       "0    Christ Hospital               40.734786               -74.050444   \n",
       "1        Oakland Ave               40.737604               -74.052478   \n",
       "2       Lincoln Park               40.724605               -74.078406   \n",
       "3       Newport Pkwy               40.728745               -74.032108   \n",
       "4     Exchange Place               40.716247               -74.033459   \n",
       "\n",
       "   End Station ID    End Station Name  End Station Latitude  \\\n",
       "0            3207         Oakland Ave             40.737604   \n",
       "1            3212     Christ Hospital             40.734786   \n",
       "2            3193        Lincoln Park             40.724605   \n",
       "3            3187           Warren St             40.721124   \n",
       "4            3192  Liberty Light Rail             40.711242   \n",
       "\n",
       "   End Station Longitude  Bike ID   User Type  Birth Year  Gender  \\\n",
       "0             -74.052478    24470  Subscriber      1960.0       1   \n",
       "1             -74.050444    24481  Subscriber      1960.0       1   \n",
       "2             -74.078406    24628  Subscriber      1983.0       1   \n",
       "3             -74.038051    24613  Subscriber      1975.0       1   \n",
       "4             -74.055701    24668    Customer      1984.0       0   \n",
       "\n",
       "   Trip_Duration_in_min  \n",
       "0                     6  \n",
       "1                    12  \n",
       "2                    45  \n",
       "3                     5  \n",
       "4                     9  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###################### Code above only needs to be run once ######################\n",
    "#Load aggregated dataset to minimize time needed to run the code\n",
    "df = df.drop(['Unnamed: 0'], axis = 1)\n",
    "print(df.columns)\n",
    "df_copy = df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Trip_Duration_in_min</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Gender</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Birth Year</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>User Type</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bike ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>End Station Longitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>End Station Latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>End Station Name</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>End Station ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Start Station Longitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Start Station Latitude</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Start Station Name</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Start Station ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Stop Time</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Start Time</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Trip Duration</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Total  Percent\n",
       "Trip_Duration_in_min         0      0.0\n",
       "Gender                       0      0.0\n",
       "Birth Year                   0      0.0\n",
       "User Type                    0      0.0\n",
       "Bike ID                      0      0.0\n",
       "End Station Longitude        0      0.0\n",
       "End Station Latitude         0      0.0\n",
       "End Station Name             0      0.0\n",
       "End Station ID               0      0.0\n",
       "Start Station Longitude      0      0.0\n",
       "Start Station Latitude       0      0.0\n",
       "Start Station Name           0      0.0\n",
       "Start Station ID             0      0.0\n",
       "Stop Time                    0      0.0\n",
       "Start Time                   0      0.0\n",
       "Trip Duration                0      0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of missing data.\n",
    "def missing_data(df):\n",
    "    total = df.isnull().sum().sort_values(ascending=False)\n",
    "    percent = (df.isnull().sum()/df.isnull().count()*100).sort_values(ascending=False)\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    return missing_data\n",
    "missing_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Trip Duration</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "      <td>735502.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>934.31</td>\n",
       "      <td>3206.76</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>3203.39</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>24914.01</td>\n",
       "      <td>1979.54</td>\n",
       "      <td>15.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>40638.78</td>\n",
       "      <td>26.55</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>64.36</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>753.77</td>\n",
       "      <td>9.37</td>\n",
       "      <td>677.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>61.00</td>\n",
       "      <td>3183.00</td>\n",
       "      <td>40.69</td>\n",
       "      <td>-74.10</td>\n",
       "      <td>147.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-74.10</td>\n",
       "      <td>14552.00</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>246.00</td>\n",
       "      <td>3186.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>3186.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>24486.00</td>\n",
       "      <td>1975.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>383.00</td>\n",
       "      <td>3202.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>3199.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>24602.00</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>652.00</td>\n",
       "      <td>3211.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>3211.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>24711.00</td>\n",
       "      <td>1986.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>20260211.00</td>\n",
       "      <td>3426.00</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-74.03</td>\n",
       "      <td>3442.00</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29296.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>337670.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count      735502.00         735502.00               735502.00   \n",
       "mean          934.31           3206.76                   40.72   \n",
       "std         40638.78             26.55                    0.01   \n",
       "min            61.00           3183.00                   40.69   \n",
       "25%           246.00           3186.00                   40.72   \n",
       "50%           383.00           3202.00                   40.72   \n",
       "75%           652.00           3211.00                   40.73   \n",
       "max      20260211.00           3426.00                   40.75   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count                735502.00       735502.00             735502.00   \n",
       "mean                    -74.05         3203.39                 40.72   \n",
       "std                       0.01           64.36                  0.10   \n",
       "min                     -74.10          147.00                  0.00   \n",
       "25%                     -74.05         3186.00                 40.72   \n",
       "50%                     -74.04         3199.00                 40.72   \n",
       "75%                     -74.04         3211.00                 40.73   \n",
       "max                     -74.03         3442.00                 40.80   \n",
       "\n",
       "       End Station Longitude    Bike ID  Birth Year  Trip_Duration_in_min  \n",
       "count              735502.00  735502.00   735502.00             735502.00  \n",
       "mean                  -74.05   24914.01     1979.54                 15.57  \n",
       "std                     0.17     753.77        9.37                677.31  \n",
       "min                   -74.10   14552.00     1900.00                  1.00  \n",
       "25%                   -74.05   24486.00     1975.00                  4.00  \n",
       "50%                   -74.04   24602.00     1982.00                  6.00  \n",
       "75%                   -74.04   24711.00     1986.00                 11.00  \n",
       "max                     0.00   29296.00     2000.00             337670.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ensure data is formatted correctly to avoid errors in the visuals\n",
    "df['Start Time'] = to_datetime(df['Start Time'])\n",
    "df['Stop Time'] = to_datetime(df['Stop Time'])\n",
    "df['Start Station Name'] = df['Start Station Name'].astype('category')\n",
    "df['End Station Name'] = df['End Station Name'].astype('category')\n",
    "df['User Type'] = df['User Type'].astype('category')\n",
    "df['Gender'] = df['Gender'].astype('category')\n",
    "round(df.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Quasi Confirm Hypothesis in point #3\n",
    "df_bikenum = pd.DataFrame()\n",
    "df_bikenum['First Bike'] = df[df['Trip Duration'] < 90]['Start Station Name'] \n",
    "df_bikenum['Second Bike'] = df[df['Trip Duration'] < 90]['End Station Name']\n",
    "#df_bikenum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clear up enviornment and drop double count\n",
    "del(df_bikenum)\n",
    "df = df.drop(df.index[(df['Trip Duration'] < 90) & \n",
    "                          (df['Start Station Latitude'] == df['End Station Latitude'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data for Top 5 Stations visual\n",
    "top5 = pd.DataFrame() \n",
    "top5['Station']=df['Start Station Name'].value_counts().head().index\n",
    "top5['Number of Starts']=df['Start Station Name'].value_counts().head().values\n",
    "top5['Station'] = top5['Station'].cat.remove_unused_categories()\n",
    "top5['Station'] = top5['Station'].astype('object')\n",
    "#top5.sort_values(by = 'Number of Starts', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot for Part 1: Top 5 Stations\n",
    "ax = sns.barplot('Station', 'Number of Starts', data = top5, palette=\"GnBu_d\")\n",
    "ax.set_title('Top 5 Citi Bike Stations by Number of Starts', fontsize = 12)\n",
    "rcParams['figure.figsize'] = 12,7\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=40, ha = 'right')\n",
    "for index, row in top5.iterrows():\n",
    "    ax.text(index,row['Number of Starts']-4000,row['Number of Starts'], \n",
    "            color='white', ha=\"center\", fontsize = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Trip Duration by User Type\n",
    "This question is a bit unclear in terms of what to do with the anomalies, so I'll be making two graphs. One with anomalies, one without.<br />\n",
    "\n",
    "*There are NA values in the dataset for usertype as can be seen from missing_table. Since it's only 0.09% of the data, it's safe to remove.*<br />\n",
    "\n",
    "**According to Citi Bikes' website:** The first 45 minutes of each ride is included for Annual Members, and the first 30 minutes of each ride is included for Day Pass users. If you want to keep a bike out for longer, it’s only an extra $4 for each additional 15 minutes.<br />\n",
    "\n",
    "It's safe to assume, no one (or very few people) will be willing to rent a bike for more than 2 hours, especially a clunky citibike. If they did, it would cost them an additional $20 assuming they're annual subscribers. It would be more economical for them to buy a bike if they want that workout or use one of the tour bikes in central park if they want to tour and explore the city on a bike. There may be a better way to choose an optimal cutoff, however, time is key in a client project. Or just docing and getting another bike. The real cost of a bike is accrued ~24 hours. <br />\n",
    "\n",
    "**Anomalies**: Any trip which lasts longer than 2 hours (7,200 seconds) probably indicates a stolen bike, an anomaly, or incorrect docking of the bike. As an avid Citibike user, I know first hand that it doesn't make any sense for one to use a bike for more than one hour! However, I've added a one hour cushion just in case. No rider would plan to go over the maximum 45 minutes allowed. However, I wplan to reduce this to one hour in the future for modelling purposes. \n",
    "\n",
    "1. *First Half- with anomalies in dataset*\n",
    "    - The graph under ax2 is a bargraph of average trip duration for each user type. It's helpful, but would be better to see a boxplot and get an idea of the distribution and see mintues instead of seconds.<br />\n",
    "    - Second graph is a basic Boxplot based with anomalies included. As we can see, there is too much noise for this to be useful. It'll be better to look at this without anomalies.<br />\n",
    "\n",
    "2. *Second Half - without anomalies in dataset*\n",
    "    - Still not useful, let's add a column with minutes for trip Duration.<br />\n",
    "    - Boxplot with minutes is much more useful. There are still some outliers, however, it is informative. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(top5)\n",
    "#Drop NA Usertype\n",
    "df = df.dropna(subset=['User Type']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate trip duration\n",
    "TD_user = pd.DataFrame()\n",
    "TD_user['Avg. Trip Duration'] = round(df.groupby('User Type')['Trip Duration'].mean(),2)\n",
    "TD_user = TD_user.reset_index()\n",
    "TD_user['User Type'] = TD_user['User Type'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average trip Duration per User Type with Anomalies\n",
    "ax2 = sns.barplot('User Type', 'Avg. Trip Duration', data = TD_user,palette=\"GnBu_d\")\n",
    "ax2.set_title('Average Trip Duration by User Type (with anomalies)')\n",
    "#rcParams['figure.figsize'] = 12,7\n",
    "ax2.set_xticklabels(ax2.get_xticklabels(),rotation=40, ha = 'right')\n",
    "ax2.set_ylabel('Avg. Trip Duration (Seconds)')\n",
    "for index, row in TD_user.iterrows():\n",
    "    ax2.text(index,row['Avg. Trip Duration']-70,(str(row['Avg. Trip Duration'])+\"  Seconds\"), \n",
    "             color='white', ha=\"center\", fontsize = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplots are more informative to visualize breakdown of data\n",
    "del(TD_user)\n",
    "df.boxplot('Trip Duration', by = 'User Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove anomalies based on definition above\n",
    "df = df.drop(df.index[(df['Trip Duration'] > 7200)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplots are more informative to visualize breakdown of data\n",
    "df.boxplot('Trip Duration', by = 'User Type')\n",
    "plt.show()\n",
    "#Boxplot without outliers\n",
    "df.boxplot('Trip Duration', by = 'User Type',showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add Minutes column for Trip Duration\n",
    "df['Minutes'] = df['Trip Duration']/60\n",
    "#For Visual purposes, rounded\n",
    "df['Minutes'] = round(df['Minutes'])\n",
    "df['Minutes'] = df['Minutes'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ekzewkT1G0jUR8Zp8+X2SviXpHRHxeM12e0u6RNk87R0k3SRpXES8uKHjjx8/PubNm1fnWwD6R0/TR7r05t8RAGDD6GfRrGzfERHje3qtN7f++5Gk30ja3fZi28dJ+q6kYZJ+Yfsu22dLUkTcI+lySfdKukHSZzcWtIFmdPLJJ5ddAgBU2owZM8ouASjERudsR8RHelh93ga2nyGJfyGotHvuuafsEgCgsgYPHqzW1lYNHjz4b3O2gWbVq2kkqTGNBM2A05sAkBb9LJpVQ9NIALzU9OnTyy4BACpr1qxZuv766zVr1qyySwEaRtgG6vDCCy+UXQIAVNa0adN055139ni/baDZMI0E6CVObwJAWvSzaFZMIwEKduyxx5ZdAgBUTlfYPv744/Wzn/1Mxx9//HrrgWbEyDbQS4y4AEBa9LNoVoxsAwAAACUgbAN9FBHq6OhglAUAEqGfRZVs9KE2ANbH3EEASIt+FlXCyDYAAACQCGEb6KPNNttMZ5xxhjbbjH8+AJDK1KlTyy4BKAR3IwF6iavkASAt+lk0K+5GAhRsypQpZZcAAJXV2tqqs846S62trWWXAjSMsA3UYfjw4WWXAAAAmgDTSIBe4vQmAKRFP4tmxTQSoGDHHHNM2SUAQGUxjQRVQtgG6jBy5MiySwAAAE2AaSRAL3F6EwDSop9Fs2IaCVCwiRMnll0CAFRWW1ubLrjgArW1tZVdCtAwwjZQh3Xr1pVdAgBU1qpVq/Tcc89p1apVZZcCNIxpJEAvcXoTANKin0WzYhoJULCjjz667BIAoLJGjhyp2bNnczE6KoGwDdShpaWl7BIAoLKGDBmilpYWDRkypOxSgIYxjQToJU5vAkBa9LNoVkwjAQrEwxYAIL1p06aVXQJQCMI20EcrV67U//7v/2rlypVllwIAlTRy5EjtsccezNlGJTCNBOglTm8CQFr0s2hWTCMBCvahD32o7BIAoNKmT59edglAIQjbQB+1trbqoIMOYs42ACTEVD1UBdNIgF7i9CYApEU/i2bFNBKgYAceeGDZJQBAZc2aNUvXX3+9Zs2aVXYpQMMY2QZ6iREXAEiLfhbNipFtAAAAoASEbaCPIkIdHR2MsgBAArbX62c3NNoNNIOWsgsAmg0dPwCkQ8BG1TCyDQAAACRC2Abq8K53vavsEgCgsgYNGqRvfetbGjRoUNmlAA3jbiRAL3GVPACkRT+LZsXdSICCzZgxo+wSAKDSvvKVr5RdAlAIwjZQh7vvvrvsEgCgslpbWzV69Gi1traWXQrQMKaRAL3E6U0ASIt+Fs2KaSRAwbhAEgDSOvHEE8suASgEYRuow0EHHVR2CQBQaUOGDCm7BKAQTCMBeonTmwCQFv0smhXTSICCTZs2rewSAKDSuBsJqoKwDdSBu5EAQFovvPBC2SUAhWAaCdBLnN4EgLToZ9GsmEYCFOz9739/2SUAQKXx8DBUxUbDtu3zbS+3/YeadaNs/8L2g/nfI/P1tv1fthfYnm97/5TFA2XZc889yy4BACpt9erVZZcAFKI3I9s/kPS+butOknRTRIyTdFO+LEmHSBqX/5ks6axiygQGllmzZpVdAgBU2qmnnlp2CUAhNhq2I+JmSU92W324pDn513MkHVGz/sLI3CpphO3tiyoWGCj22muvsksAgMpqa2vTBRdcoLa2trJLARpW75zttohYKkn539vm60dLerRmu8X5OqBSxowZU3YJAFBZy5Yt05IlS7Rs2bKySwEa1lLw8Xq6jLjHy4dtT1Y21URtbW3q7OwsuBQgneuuu269ZT6/AFCsU045Zb1l+lk0q3rD9jLb20fE0nyayPJ8/WJJO9Vst6OkJT0dICLOlXSulN36r729vc5SgP636667asGCBX9b5vMLAMWaMWPGeoGbfhbNqt5pJHMlTcq/niTp6pr1E/O7krxJ0tNd002AKhkyZEjZJQBAZbW1tWmHHXZgzjYqYaMPtbH9I0ntkraRtEzSdElXSbpc0qskPSLpgxHxpLO70X9X2d1L/irp4xGx0afV8FAbNAMetgAAadHPolk19FCbiPhIRGwfEYMjYseIOC8i/hIRB0fEuPzvJ/NtIyI+GxGvjoh9ehO0gWb0lre8pewSAKDSpk6dWnYJQCF4XDvQS4y4AEBa9LNoVjyuHQAAACgBYRvoo4hQR0cHoywAkAj9LKqk6PtsA5W3odOcAIDG0c+iShjZBgAAABIhbAN1+OQnP1l2CQBQWa2trTrrrLPU2tpadilAw7gbCdBLXCUPAGnRz6JZcTcSoGATJ04suwQAqLRPf/rTZZcAFIKwDdSBx7UDQFqvfOUryy4BKATTSIBe4vQmAKRFP4tmxTQSoGBHH3102SUAQKV94hOfKLsEoBCEbaAOLS3coh4AUho8eHDZJQCFYBoJ0Euc3gSAtOhn0ayYRgIU7AMf+EDZJQBApTGNBFVB2AbqMHTo0LJLAAAATYBpJEAvcXoTANKin0WzYhoJULADDzyw7BIAoNKmTp1adglAIQjbQB223XbbsksAgEp75plnyi4BKATTSIBe4vQmAKRFP4tmxTQSoGD77rtv2SUAQKWdfPLJZZcAFIKwDdRh6623LrsEAKi01atXl10CUAimkQC9xOlNAEiLfhbNimkkQMH222+/sksAgEqbOHFi2SUAhSBsA3VghAUA0ho9enTZJQCFYBoJ0Euc3gSAtOhn0ayYRgIUrL29vewSAKDSpk2bVnYJQCEY2QZ6iREXAEiLfhbNipFtAAAAoASEbaCPIkIdHR2MsgBAIvSzqJKWsgsAms2GTnMCABpHP4sqYWQbAAAASISwDdThqKOOKrsEAKg07kaCquBuJEAvcZU8AKRFP4tmxd1IgIIdeeSRZZcAAJU2ffr0sksACkHYBuowf/78sksAgEp78MEHyy4BKARhG6jDAw88UHYJAFBpl1xySdklAIUgbAN12H333csuAQAqbeLEiWWXABSCsA3UYZ999im7BACotF122aXsEoBCcDcSoJe4Sh4A0qKfRbPibiRAwRhxAYC0JkyYUHYJQCEI20AdhgwZUnYJAFBpI0aMKLsEoBCEbaAO999/f9klAEClXXjhhWWXABSCsA3UYe+99y67BACotAMPPLDsEoBCELaBOvz1r38tuwQAqLRRo0aVXQJQCMI2UIeHHnqo7BIAoNLmzp1bdglAIQjbQB123XXXsksAgErjoTaoCsI2UIfNN9+87BIAoNIWLlxYdglAIQjbQB3uueeesksAgEq75ZZbyi4BKARhG6jD2LFjyy4BACqtvb297BKAQjQUtm3/i+17bP/B9o9sD7W9i+3bbD9o+zLbPP0DlcPpTQBIq7Ozs+wSgELUHbZtj5b0eUnjI+I1kgZJOkrS6ZLOjIhxklZIOq6IQgEAAIBm0+g0khZJW9hukbSlpKWS3inpivz1OZKOaLANYECJCHV0dCgiyi4FACqJfhZV0lLvjhHxmO1vSnpE0mpJN0q6Q9JTEbE232yxpNE97W97sqTJktTW1sbpIjQN2y9Zx+cXAIpDP4sqcb2/NdoeKeknkj4s6SlJP86Xp0fErvk2O0m6LiL22dCxxo8fH/PmzaurDqC/9NT5d2H0BQAaRz+LZmX7jogY39NrjUwjeZekhyLi8YhYI+lKSW+RNCKfViJJO0pa0kAbwIA0ZsyYsksAgErbf//9yy4BKEQjYfsRSW+yvaWzX0UPlnSvpA5JR+bbTJJ0dWMlAgPPokWLyi4BACrtzjvvLLsEoBB1h+2IuE3ZhZB3Svp9fqxzJX1J0gm2F0h6haTzCqgTGFD23XffsksAgErjce2oirovkJSkiJguaXq31QslHdDIcYGBbsSIEWWXAACV9txzz5VdAlCIui+QLBIXSKIZcOEOAKRFP4tmleoCSWCTteOOO5ZdAgBU2oQJE8ouASgEYRuow6hRo8ouAQAqjel6qArCNlCH+fPnl10CAFTahRdeWHYJQCEI20AdRo/u8cGoAICCcNcnVAVhG6jDhi7iAQA0joeHoSoI20AdFi9eXHYJAFBpc+fOLbsEoBCEbaAOXLgDAGkdc8wxZZcAFIKwDdRhq622KrsEAKg0LkRHVRC2gTo89thjZZcAAJVG2EZVELaBOowcObLsEgCg0rgbCaqCsA3UgccGA0BahG1UBWEbqMNTTz1VdgkAUGkXXXRR2SUAhSBsAwCAAWfChAlllwAUgrAN1IE52wCQ1qJFi8ouASgEYRuow4oVK8ouAQAqjbuRoCoI2wAAYMCZOHFi2SUAhSBsAwCAAefCCy8suwSgEIRtAAAAIBHCNtBHEaGOjg7utQ0AidDPokpayi4AaDa2yy4BACqNfhZVwsg2AAAAkAhhGwAADDi77rpr2SUAhSBsAwCAAWfBggVllwAUgrANAAAGnOHDh5ddAlAIwjZQh2233bbsEgCg0saNG1d2CUAhCNtAHZYvX152CQBQafPmzSu7BKAQhG2gDmPGjCm7BACotN12263sEoBCELaBOixatKjsEgCg0pYtW1Z2CUAhCNsAAGDAefrpp8suASgEYRuow+677152CQBQaaNGjSq7BKAQhG2gDs8//3zZJQBApW2zzTZllwAUgrAN1IE52wCQ1gMPPFB2CUAhCNtAHQYPHlx2CQBQadtvv33ZJQCFIGwDdRg7dmzZJQBApTFdD1VB2AbqcP/995ddAgBU2pNPPll2CUAhCNtAHdra2souAQAqbfjw4WWXABSCsA3UgREXAEhryy23LLsEoBCEbaAOa9asKbsEAKi0pUuXll0CUAjCNgAAGHC22267sksACkHYBurAnG0ASGv16tVllwAUgrAN1GHZsmVllwAAlfb000+XXQJQCMI2UIdBgwaVXQIAVNqYMWPKLgEoBGEbqMOLL75YdgkAUGmLFi0quwSgEIRtAAAAIBHCNtBHEaGOjg5FRNmlAEAl0c+iSlrKLgBoNrbLLgEAKo1+FlXCyDYAAACQCGEbqMNuu+1WdgkAUGk777xz2SUAhWgobNseYfsK23+0fZ/tN9seZfsXth/M/x5ZVLHAQPHAAw+UXQIAVNrDDz9cdglAIRod2f5PSTdExB6S9pN0n6STJN0UEeMk3ZQvA5UyduzYsksAgErbYostyi4BKETdYdv21pLeLuk8SYqIFyLiKUmHS5qTbzZH0hGNFgkAADYtw4YNK7sEoBCN3I1krKTHJV1gez9Jd0j6gqS2iFgqSRGx1Pa2Pe1se7KkyZLU1tamzs7OBkoB+tfChQvXW+bzCwDFWr58+XrL9LNoVq73Hpa2x0u6VdJbI+I22/8p6RlJUyJiRM12KyJig/O2x48fH/PmzaurDqC/bOhWVNwLFgAaV9vPtrS0aO3atX9bpp/FQGb7jogY39NrjczZXixpcUTcli9fIWl/Sctsb583vL2k5S+zP9C0mLMNAGkNHjy47BKAQtQdtiPiz5Ietb17vupgSfdKmitpUr5ukqSrG6oQGIC6TyMBABRr9erVZZcAFKLRJ0hOkXSx7SGSFkr6uLIAf7nt4yQ9IumDDbYBAAAANKWGwnZE3CWpp/kpBzdyXAAAsGnrPmcbaFY8QRIAAAw4BG1UBWEbAAAASISwDQAAACRC2AYAAAASIWwDddhsM/7pAACAjSMxAHVYt25d2SUAAIAmQNgGAAAAEiFsA30UEero6FBElF0KAAAY4Bp9giSwybGtzTbbjKkkAABgowjb2OTZ7vM+3YN2b4/BaDgAAJsWppFgkxcRff6z85euqWs/AACwaSFsAwAAAIkQtgEAAIBECNsAAGBA4a5PqBIukAQAAANKPReuAwMVI9sAAABAIoRtAAAAIBHCNgAAAJAIYRsAAABIhLANAAAAJELYBgAAABIhbAMAAACJELYBAACARAjbAAAAQCKEbQAAACARwjYAAACQCGEbAAAASISwDQAAACRC2AYAAAASIWwDAAAAiRC2AQAAgEQI2wAAAEAihG0AAAAgEcI2AAAAkAhhGwAAAEiEsA0AAAAkQtgGAAAAEiFsAwAAAIkQtgEAAIBECNsAAGBAiQh1dHQoIsouBWhYS9kFAAAA1LJddglAYRjZBgAAABIhbAMAAACJELYBAACARAjbAAAAQOCtsqwAAA9GSURBVCKEbQAAACARwjYAAACQCGEbAAAASISwDQAAACTScNi2Pcj272xfky/vYvs22w/avsz2kMbLBAAAAJpPESPbX5B0X83y6ZLOjIhxklZIOq6ANgAAAICm01DYtr2jpEMlfT9ftqR3Sroi32SOpCMaaQMAAABoVi0N7v9tSSdKGpYvv0LSUxGxNl9eLGl0TzvanixpsiS1tbWps7OzwVKA/sVnFgD6D30umlXdYdv2YZKWR8Qdttu7VvewafS0f0ScK+lcSRo/fny0t7f3tBkwMN1wrfjMAkD/oc9Fs2pkZPutkibYfr+koZK2VjbSPcJ2Sz66vaOkJY2XCQAAADSfuudsR8TJEbFjRIyRdJSkX0bERyV1SDoy32ySpKsbrhIAAABoQinus/0lSSfYXqBsDvd5CdoAAAAABrxGL5CUJEVEp6TO/OuFkg4o4rgAAABAM+MJkgAAAEAihG0AAAAgEcI2AAAAkAhhGwAAAEiEsA0AAAAkQtgGAAAAEiFsAwAAAIkQtgEAwIASEero6FBElF0K0LBCHmoDAABQFNtllwAUhpFtAAAAIBHCNgAAAJAIYRsAAABIhLANAAAAJELYBgAAABIhbAMAAACJELYBAACARAjbAAAAQCKEbQAAACARwjYAAACQCGEbAAAASISwDQAAACRC2AYAAAASIWwDAAAAiRC2AQAAgEQI2wAAAEAihG0AAAAgEcI2AAAAkAhhGwAAAEiEsA0AAAAk0lJ2AUCR9vv6jXp69Zp+aWvMSdcmb2P4FoN19/T3JG8HAACkQdhGpTy9eo0WzTw0eTudnZ1qb29P3k5/BHoAAJAO00gAAACARAjbAAAAQCKEbQAAMKBEhDo6OhQRZZcCNIw52wAAYECxXXYJQGEY2QYAAAASIWwDAAAAiRC2AQAAgEQI2wAAAEAihG0AAAAgEcI2AAAAkAhhGwAAAEiEsA0AAAAkQtgGAAAAEiFsAwAAAIkQtgEAAIBECNsAAABAIoRtAAAAIJG6w7btnWx32L7P9j22v5CvH2X7F7YfzP8eWVy5AAAAQPNoZGR7raRpEbGnpDdJ+qztvSSdJOmmiBgn6aZ8GQAAANjk1B22I2JpRNyZf/2spPskjZZ0uKQ5+WZzJB3RaJEAAABAMypkzrbtMZJeJ+k2SW0RsVTKArmkbYtoAwAAAGg2LY0ewHarpJ9ImhoRz9ju7X6TJU2WpLa2NnV2djZaCiBJ/fJZWrlyZb99Zvm3AQD0hWheDYVt24OVBe2LI+LKfPUy29tHxFLb20ta3tO+EXGupHMlafz48dHe3t5IKUDmhmvVH5+lzs7Ofmmnv94PAAwkEfG3frZrEI++EM2q7rDt7NN/nqT7IuJbNS/NlTRJ0sz876sbqhAAAGxSenuWHGgGjYxsv1XSxyT93vZd+bovKwvZl9s+TtIjkj7YWIkAAKCZFRGee3uMiGi4LaBIdYftiLhF0st98g+u97gAAKBa6gnAY066VotmHpqgGqB/8QRJAAAAIJGG70YCDCTD9jxJ+8zpp+cozdn4Jo0atqckMbIDAECzImyjUp69b2a/nHbsr7uRjDnp2uRtAACAdJhGAgAAACRC2AYAAAASIWwDAAAAiRC2AQAAgEQI2wAAAEAihG0AAAAgEcI2AAAAkAhhGwAAAEiEsA0AAAAkQtgGAAAAEiFsAwAAAIkQtgEAAIBECNsAAABAIoRtAAAAIBHCNgAAAJAIYRsAAABIhLANAAAAJELYBgAAABIhbAMAAACJELYBAACARAjbAAAAQCItZRcAFG3MSdf2T0M3pG9n+BaDk7cBAH2x39dv1NOr1/RLW/3Rnw/fYrDunv6e5O1g00XYRqUsmnlov7Qz5qRr+60tABhInl69pl/6v87OTrW3tydvp98GaLDJYhoJAAAAkAhhGwAAAEiEsA0AAAAkQtgGAAAAEiFsAwAAAIkQtgEAAIBECNsAAABAIoRtAAAAIBHCNgAAAJAIT5AEAAC9NmzPk7TPnJP6p7E56ZsYtqck8URgpEPYBgAAvfbsfTN5XDvQB0wjAQAAABIhbAMAAACJELYBAACARAjbAAAAQCJcIAkAAPqk3y4qvCF9O8O3GJy8DWzaCNsAAKDX+uNOJFIW6PurLSAlppEAAAAAiRC2AQAAgEQI2wAAAEAihG0AAAAgEcI2AAAAkAhhGwAAAEgkWdi2/T7b99teYPukVO0AAAAAA1WS+2zbHiTpe5LeLWmxpNttz42Ie1O0BzTCdn37nd73fSKirrYAoJnRz2JTlmpk+wBJCyJiYUS8IOlSSYcnagtoSET0+U9HR0dd+wHApoh+FpuyVGF7tKRHa5YX5+sAAACATUaqx7X3dL5ovV83bU+WNFmS2tra1NnZmagUoHgrV67kMwsACdHPoipShe3FknaqWd5R0pLaDSLiXEnnStL48eOjvb09USlA8To7O8VnFgDSoZ9FVaSaRnK7pHG2d7E9RNJRkuYmagsAAAAYkJKMbEfEWtufk/RzSYMknR8R96RoCwAAABioUk0jUURcJ+m6VMcHAAAABjqeIAkAAAAkQtgGAAAAEiFsAwAAAIkQtgEAAIBECNsAAABAIoRtAAAAIBHCNgAAAJAIYRsAAABIhLANAAAAJOKIKLsG2X5c0sNl1wH0wTaSnii7CACoMPpZNJOdI+KVPb0wIMI20Gxsz4uI8WXXAQBVRT+LqmAaCQAAAJAIYRsAAABIhLAN1OfcsgsAgIqjn0UlMGcbAAAASISRbQAAACARwjYAAACQCGEblWV7O9uX2v6T7XttX2d7tz4e4wjbe6WqEQCake1TbN9je77tu2y/cQPbfs32Fwtuf7zt/0p1fKBILWUXAKRg25J+KmlORByVr3utpDZJD/ThUEdIukbSvYUX+TJsD4qIF/urPQDoC9tvlnSYpP0j4nnb20ga0o/tt0TEPEnzCjiWlV2/tq7xyoCeMbKNqjpI0pqIOLtrRUTcJWmQ7Wu61tn+ru1j869n5iPg821/0/ZbJE2QdEY+cvNq26+1fWu+zU9tj8z37bR9pu2bbd9n+w22r7T9oO1Ta9o7xvZv8+OdY3tQvn6l7X+3fZukN/fHNwgA6rS9pCci4nlJiognImKJ7UV58O4aee6s2Wc/27/M+8RP5dtsn/eZd9n+g+235evfZ/tO23fbvilf9zXb59q+UdKFtttr+/Kejp/v96+2b8/77K/n68bk/fR/S7pT0k7JvlOAGNlGdb1G0h293dj2KEn/KGmPiAjbIyLiKdtzJV0TEVfk282XNCUifmX73yVNlzQ1P8wLEfF221+QdLWk10t6UtKfbJ8paVtJH5b01ohYk3f0H5V0oaStJP0hIv6tgPcOACndKOnfbD8g6X8kXRYRv9rIPvtKepOyvu53tq+V9BFJP4+IGfnAw5a2XylptqS3R8RDed/c5fWSDoyI1bbbe3H810gaJ+kASZY01/bbJT0iaXdJH4+If67zewD0GmEbyDwj6TlJ38876Wu6b2B7uKQRNf+pzJH045pN5uZ//17SPRGxNN9vobKRkwOV/Wdxe3bmUltIWp7v86KknxT5hgAghYhYafv1kt6m7CziZbZP2shuV0fEakmrbXcoC8C3Szrf9mBJV0XEXXmIvjkiHsrberLmGHPzY/T2+AdKeo+k3+XbtCoL349Iejgibu3bOwfqQ9hGVd0j6cge1q/V+tOnhkpSRKy1fYCkgyUdJelzkt7Zxzafz/9eV/N113KLspGVORFxcg/7Psc8bQDNIu+vOiV12v69pElav38d2n2Xlx4ibs5Hmg+V9EPbZ0h6qodtu6zaUEk9LFvSNyLinNoXbI/ZyLGAQjFnG1X1S0mbd5u79wZJgyTtZXvzfKT64Py1VknDI+I6ZdNCXpvv9qykYZIUEU9LWtE1r1DSxyRt7NRprZskHWl727zNUbZ3rvcNAkAZbO9ue1zNqtdKeljSImVn7yTpn7rtdrjtobZfIald2Rm+nSUtj4jZks6TtL+k30h6h+1d8rZGqXdecnxJP5f0ibx/l+3RXf0v0J8Y2UYl5fOu/1HSt/PTm88p+49gqqTLJc2X9KD+fnpxmKSrbQ9VNhryL/n6SyXNtv15ZSPlkySdbXtLSQslfbwPNd1r+yuSbrS9maQ1kj6r7D8pAGgWrZK+Y3uEstHsBZImS9pT0nm2vyzptm77/FbStZJeJek/8gsqJ0n6V9trJK2UNDEiHrc9WdKVeT+5XNK7e1HTS44vaYntPSX9Jp+6t1LSMcqm7QH9hse1AwAAAIkwjQQAAABIhLANAAAAJELYBgAAABIhbAMAAACJELYBAACARLj1HwCULH/IxjUR8ZqadV+TtDIivllQG69Qdq93SdpO2e3PHs+XD4iIF4poBwCwPsI2AFSU7ZaIWCtJEfEX5Q9rKjrIAwBeHtNIAGCAs/152/fanm/70nzdVrbPt3277d/ZPjxff6ztH9v+maQbe3n8b9j+bM3y6bb/2fa7bHfYvipv/3vOnw5i+xDbv7F9p+3LbG+V4K0DQNMjbAPAwHeSpNdFxL6SPpOvO0XSLyPiDZIOknRGTeB9s6RJEfHOXh7/+5KOlSTbgyR9UNKP8tfeqOzJq/soe0Lg4fkjr0+SdHBE7K/siaxfqP/tAUB1MY0EAMr3co/y7Vo/X9LFtq+SdFW+7j2SJtj+Yr48VNmjqiXpFxHxZK8bj/iT7Wdt7yNpZ0m/jYgV+SD2rRGxSJLyUfUD8932kvTrfJshkm7pbXsAsCkhbANA+f4iaWS3daMkPZR/faikt0uaIOmrtveWZEn/FBH31+5k+42SVtVRw3nKRrfHSDqnZn33XwQib/uGiPhYHe0AwCaFaSQAULKIWClpqe2DJcn2KEnvk3SL7c0k7RQRHZJOlDRCUqukn0uaUjOH+nUNlvETSf+g7CLK/6lZ/ybbr8qnl3xI2Qj2ryW9w/bYvO2tbI9rsH0AqCRGtgFgYJgo6Xu2Z+XLX8+ndwyWdJHt4cpGlM+MiKds/4ekb0uanwfuRZIOq7fxiHjO9s2S/hwR62pe+rWkWZL2ltQpaW5EhO3jJF1me0i+3ZclPVhv+wBQVY54uamCAIBNRT6CfpekIyJiYb7uXZI+FxFHlFocADQxppEAwCYuvzDyT8rmYS8sux4AqBJGtgEAAIBEGNkGAAAAEiFsAwAAAIkQtgEAAIBECNsAAABAIoRtAAAAIJH/D1suMJkGlLt5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Final Boxplot with some outliers. Could turn of outliers with showfliers = False\n",
    "df.boxplot('Minutes', by = 'User Type')\n",
    "plt.show()\n",
    "df.boxplot('Minutes', by = 'User Type', showfliers = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_user2 = pd.DataFrame()\n",
    "TD_user2['Avg. Trip Duration'] = round(df.groupby('User Type')['Minutes'].mean(),1)\n",
    "TD_user2 = TD_user2.reset_index()\n",
    "TD_user2['User Type'] = TD_user2['User Type'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average Trip Duration Based on Minutes\n",
    "ax3 = sns.barplot('User Type', 'Avg. Trip Duration', data = TD_user2,palette=\"GnBu_d\")\n",
    "ax3.set_title('Average Trip Duration by User Type')\n",
    "#rcParams['figure.figsize'] = 12,10\n",
    "ax3.set_xticklabels(ax3.get_xticklabels(),rotation=40, ha = 'right')\n",
    "for index, row in TD_user2.iterrows():\n",
    "    ax3.text(row.name,row['Avg. Trip Duration']-1,(str(row['Avg. Trip Duration'])+\"  Minutes\"), \n",
    "             color='white', ha=\"center\", fontsize = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(TD_user2)\n",
    "#Undo rounding for modelling purposes\n",
    "df['Minutes'] = df['Trip Duration']/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3: Most Popular Trip\n",
    "To get most popular trips, the most convenient way to do this is by using the groupby function in pandas. It's analogous to a Pivot table. <br />\n",
    "\n",
    "The groupby function makes it extremely easy and convenient to identify the most popular trips. Visuals and transformations can be found below.<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify the 10 most popular trips\n",
    "trips_df = pd.DataFrame()\n",
    "trips_df = df.groupby(['Start Station Name','End Station Name']).size().reset_index(name = 'Number of Trips')\n",
    "trips_df = trips_df.sort_values('Number of Trips', ascending = False)\n",
    "trips_df[\"Start Station Name\"] = trips_df[\"Start Station Name\"].astype(str)\n",
    "trips_df[\"End Station Name\"] = trips_df[\"End Station Name\"].astype(str)\n",
    "trips_df[\"Trip\"] = trips_df[\"Start Station Name\"] + \" to \" + trips_df[\"End Station Name\"]\n",
    "trips_df = trips_df[:10]\n",
    "trips_df = trips_df.drop(['Start Station Name', \"End Station Name\"], axis = 1)\n",
    "trips_df = trips_df.reset_index()\n",
    "#trips_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax4 = sns.barplot('Number of Trips','Trip', data = trips_df,palette=\"GnBu_d\")\n",
    "ax4.set_title('Most Popular Trips', fontsize = 20)\n",
    "ax4.set_ylabel(\"Trip\",fontsize=16)\n",
    "ax4.set_xlabel(\"Number of Trips\",fontsize=16)\n",
    "for index, row in trips_df.iterrows():\n",
    "    ax4.text(row['Number of Trips']-220,index,row['Number of Trips'], \n",
    "             color='white', ha=\"center\",fontsize = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 4: Rider Performance by Gender and Age\n",
    "\n",
    "**Ask:** Rider performance by Gender and Age based on avg trip distance (station to station), median speed (trip duration/distance traveled)<br />\n",
    "\n",
    "Let's make sure the data we're working with here is clean.<br />\n",
    "\n",
    "1. Missing Gender and Birth Year values - Check missing_table above\n",
    "    - No for Gender. Yes for Birth Year\n",
    "    - ~10% Missing Birth year. Not a big chunk of data. Can either impute missing values or drop it. Since it's less                                         than 10% of the data, it's safe to assume the rest of the 90% is a representative sample of data and we can replace the birth year with the median, based on gender and Start Station ID. I chose this method because most people the same age live in similar neighborhoods (i.e: young people in east village, older people in Upper West Side, etc.). This will be done after anomalies are removed and speed is calculated.\n",
    "2. Are there anomalies?\n",
    "    - For Birth Year, there are some people born prior to 1956. I can believe some 60 year olds can ride a bike and that's a stretch, however, anyone \"born\" prior to that riding a citibike is an anomaly and false data. There could be a few senior citizens riding a bike, but probably not likely.\n",
    "    - My approach is to identify the age 2 standard deviations lower than the mean. After calculating this number, mean-2stdev, I removed the tail end of the data, birth year prior to 1956.\n",
    "3. Caulculate an Age column to make visuals easier to interpret.\n",
    "4. Calculate trip distance (Miles)\n",
    "    - No reliable way to calculate bike route since we can't know what route a rider took without GPS data from each bike. \n",
    "    - Could use Google maps and use lat,long coordinates to find bike route distance. However, this would require more than the daily limit on API calls. Use the geopy.distance packge which uses Vincenty distance uses more accurate ellipsoidal models. This is more accurate than Haversine formula, but doesn't matter much for our purposes.\n",
    "5. Caulculate Speed (min/mile) and (mile/hr)\n",
    "    - (min/mile): Can be used like sprint time (how fast does this person run)\n",
    "    - (mile/hr): Conventional approach. Miles/hour is an easy to understand unit of measure and one most people are used to seeing. So the visual will be created based on this understanding.\n",
    "6. Dealing with \"circular\" trips\n",
    "    - Circular trips are trips which start and end at the same station. The distance for these trips will come out to 0, however, that is not the case. These points will skew the data and visuals. Will be removing them to account for this issue. \n",
    "    - For the model, this data is also irrelevant. Because if someone is going on a circular trip, the only person who knows how long the trip is going to take is therider themself, assuming they know that. So it's safe to drop this data for the model. <br />\n",
    "7. Rename Gender Values in Legend from 0,1,2 to Unknown, Male, Female, respectively. \n",
    "    - The rows where Gender is unknown throws the visual off. There are a few ways to handle this: \n",
    "      - Remove the missing data. This would not result in a significant loss of information since only 58073 rows have gender as unknown.\n",
    "       - We can impute missing values, however given the proportion of unknowns the information gain would be negligible.\n",
    "       - Based on the reasons above, I've decided to remove data with unknown gender. These rows should not have a  significant imact on the predictive model later on. However, I will confirm this.\n",
    "8. Determine Gender and Age performance based on Average Trip distance<br />\n",
    "    - Similar to graphs for speed. Pretty straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(trips_df)\n",
    "#Drop the tail end of birth years 2 standard deviations below the mean\n",
    "#df['Birth Year'].mean()-(2*df['Birth Year'].std())\n",
    "\n",
    "df = df.drop(df.index[(df['Birth Year'] < 1956)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine coordinates to calculate distance based on Vincenty\n",
    "df['Start Coordinates'] = list(zip(df['Start Station Latitude'], df['Start Station Longitude']))\n",
    "df['End Coordinates'] = list(zip(df['End Station Latitude'], df['End Station Longitude']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the future, for a dataset of this size, I would consider using the Haversine formula to calculate distance \n",
    "# # if it's faster.\n",
    "# import geopy.distance\n",
    "# dist = []\n",
    "# for i in range(len(df)):\n",
    "#     dist.append(geopy.distance.distance(df.iloc[i]['Start Coordinates'],df.iloc[i]['End Coordinates']).miles)\n",
    "#     if (i%1000000==0):\n",
    "#         print(i)\n",
    "\n",
    "#Code above takes a long time to run, please use Distance.csv file instead\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle_out = open(\"distance.pickle\",\"wb\")\n",
    "# pickle.dump(dist, pickle_out)\n",
    "# pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in =  open(\"distance.pickle\",\"rb\")\n",
    "dist=pickle.load(pickle_in)\n",
    "df['Distance'] = dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset Index to avoid issues in future calculations\n",
    "df = df.reset_index()\n",
    "df = df.drop('index',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(dist)\n",
    "#Replace missing birth year by median based on speed and gender\n",
    "df['Birth Year'] = df.groupby(['Gender','Start Station ID'])['Birth Year'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Still have a few nulls, but it's only 2342 entries now. Comfortable dropping these.\n",
    "df = df.dropna(subset=['Birth Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate age and drop circular/roundtrips\n",
    "df['Age'] = 2018 - df['Birth Year']\n",
    "df['Age'] = df['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df.index[(df['Distance'] == 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1\n",
    "#df[df['Trip Duration']<90]\n",
    "#2. Followed the same reasoning as behind Birth Year. People in similar locations tend to also work in a similar industry or location\n",
    "df['Distance'] = df.groupby(['Gender','Start Station ID'])['Distance'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['min_mile'] = round(df['Minutes']/df['Distance'], 2)\n",
    "df['mile_hour'] = round(df['Distance']/(df['Minutes']/60),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Trip Duration</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "      <td>682576.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>512.73</td>\n",
       "      <td>3206.93</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>3203.39</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>24918.80</td>\n",
       "      <td>1980.02</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.55</td>\n",
       "      <td>0.69</td>\n",
       "      <td>37.98</td>\n",
       "      <td>14.06</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>491.07</td>\n",
       "      <td>26.80</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>64.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>757.05</td>\n",
       "      <td>8.67</td>\n",
       "      <td>8.19</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.67</td>\n",
       "      <td>21.43</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>61.00</td>\n",
       "      <td>3183.00</td>\n",
       "      <td>40.69</td>\n",
       "      <td>-74.10</td>\n",
       "      <td>152.00</td>\n",
       "      <td>40.69</td>\n",
       "      <td>-74.10</td>\n",
       "      <td>14552.00</td>\n",
       "      <td>1956.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>242.00</td>\n",
       "      <td>3186.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>3186.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>24486.00</td>\n",
       "      <td>1976.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.40</td>\n",
       "      <td>32.00</td>\n",
       "      <td>8.48</td>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>367.00</td>\n",
       "      <td>3202.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>3198.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>24603.00</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.12</td>\n",
       "      <td>0.55</td>\n",
       "      <td>36.00</td>\n",
       "      <td>10.22</td>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>601.00</td>\n",
       "      <td>3211.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>3211.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>24712.00</td>\n",
       "      <td>1986.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.02</td>\n",
       "      <td>0.88</td>\n",
       "      <td>42.00</td>\n",
       "      <td>12.93</td>\n",
       "      <td>7.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>7187.00</td>\n",
       "      <td>3426.00</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-74.03</td>\n",
       "      <td>3426.00</td>\n",
       "      <td>40.78</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>29296.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>119.78</td>\n",
       "      <td>5.26</td>\n",
       "      <td>62.00</td>\n",
       "      <td>865.69</td>\n",
       "      <td>16.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count      682576.00         682576.00               682576.00   \n",
       "mean          512.73           3206.93                   40.72   \n",
       "std           491.07             26.80                    0.01   \n",
       "min            61.00           3183.00                   40.69   \n",
       "25%           242.00           3186.00                   40.72   \n",
       "50%           367.00           3202.00                   40.72   \n",
       "75%           601.00           3211.00                   40.73   \n",
       "max          7187.00           3426.00                   40.75   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count                682576.00       682576.00             682576.00   \n",
       "mean                    -74.05         3203.39                 40.72   \n",
       "std                       0.01           64.33                  0.01   \n",
       "min                     -74.10          152.00                 40.69   \n",
       "25%                     -74.05         3186.00                 40.72   \n",
       "50%                     -74.04         3198.00                 40.72   \n",
       "75%                     -74.04         3211.00                 40.73   \n",
       "max                     -74.03         3426.00                 40.78   \n",
       "\n",
       "       End Station Longitude    Bike ID  Birth Year  Trip_Duration_in_min  \\\n",
       "count              682576.00  682576.00   682576.00             682576.00   \n",
       "mean                  -74.05   24918.80     1980.02                  8.54   \n",
       "std                     0.01     757.05        8.67                  8.19   \n",
       "min                   -74.10   14552.00     1956.00                  1.00   \n",
       "25%                   -74.05   24486.00     1976.00                  4.00   \n",
       "50%                   -74.04   24603.00     1982.00                  6.00   \n",
       "75%                   -74.04   24712.00     1986.00                 10.00   \n",
       "max                   -73.96   29296.00     2000.00                120.00   \n",
       "\n",
       "         Minutes   Distance        Age   min_mile  mile_hour  \n",
       "count  682576.00  682576.00  682576.00  682576.00  682576.00  \n",
       "mean        8.55       0.69      37.98      14.06       5.80  \n",
       "std         8.18       0.45       8.67      21.43       2.00  \n",
       "min         1.02       0.09      18.00       3.62       0.07  \n",
       "25%         4.03       0.40      32.00       8.48       4.64  \n",
       "50%         6.12       0.55      36.00      10.22       5.87  \n",
       "75%        10.02       0.88      42.00      12.93       7.08  \n",
       "max       119.78       5.26      62.00     865.69      16.58  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check for data integrity to make sure all the numbers look as expected. Only numerical data included\n",
    "round(df.describe(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "- We still have trips less than 90 seconds, however they seem to be legitimate trips. Checked using the code in cell above. \n",
    "- We have some Start Coordinates as (0.0,0.0). These are trips which were taken away for repair or for other purposes. These should be dropped. If kept, the distance for these trips is 5,389 miles. For this reason I've dropped any points where the distance is greater than 30 miles. Additionally, we have some missing values. Since it's a tiny portion, let's replace missing values based on Gender and start location. These\n",
    "- One some trips, the speed of the biker is more than 200 mph. This could be due to the formula used for distance calculation or some other error. The fastest cyclist in the world on a flat surface ever recorded biked at 82mph. It's safe to assume none of the citibike riders can approach this speed. Due to this and the fact that an average cyclist speed is 10mph, I've decided to remove all data where the speed in mph is greater than 20 mph and less than 0.1 mph. ~1.5k data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Distance'] < 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.1-Done in two steps to ensure data integrity, could've used an or statement as well.\n",
    "df = df[df['mile_hour']<20]\n",
    "#3.2\n",
    "df = df[df['mile_hour']> (df['mile_hour'].mean()-(2*df['mile_hour'].std()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping unknown to make the visual more informative. \n",
    "#Unknown gender may be important for the model, which is why I created a copy of the original dataframe.\n",
    "df1 = df.drop(df.index[(df['Gender'] == 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rider performance by age and Gender in Min/Mile \n",
    "fig, ax5 = plt.subplots(figsize=(11,5))\n",
    "df1.groupby(['Age','Gender']).median()['min_mile'].unstack().plot(ax=ax5, color =\"bg\")\n",
    "ax5.legend(['Female','Male'])\n",
    "plt.ylabel('Median Speed (min/mile)')\n",
    "plt.title('Rider Performance Based on Gender and Age (Median Speed in min/mile)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rider performance by age and Gender in Miles/hr\n",
    "del([fig,ax5])\n",
    "fig1, ax6 = plt.subplots(figsize=(11,5))\n",
    "df1.groupby(['Age','Gender']).median()['mile_hour'].unstack().plot(ax=ax6,color =\"bg\")\n",
    "ax6.legend(['Female', 'Male'])\n",
    "plt.ylabel('Median Speed (miles/hr)')\n",
    "plt.title('Rider Performance Based on Gender and Age (Median Speed in miles/hr)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rider performance by age and Gender in Averge Distance\n",
    "del([fig1,ax6])\n",
    "fig2, ax7 = plt.subplots(figsize=(11,5))\n",
    "df1.groupby(['Age','Gender']).mean()['Distance'].unstack().plot(ax=ax7,color =\"bg\")\n",
    "ax7.legend(['Female', 'Male'])\n",
    "plt.ylabel('Average Distance (miles)')\n",
    "plt.title('Rider Performance Based on Gender and Age (Average Distance in Miles)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 5: Busiest Bike by Times and Minutes Used\n",
    "*Ask:* \n",
    " 1. What is the busiest bike in NYC in 2017? \n",
    "    - Bike 25738\n",
    " 2. How many times was it used?\n",
    "     - 2355 times\n",
    " 3. How many minutes was it in use?\n",
    "      - 31,340 Minutes\n",
    "\n",
    "- Busiest bike and count can be identified by a groupby function\n",
    "- Function above will also identify the number of times the bike was used\n",
    "- A similar groupby function which calls for the sum on minutes can identify the number of minutes the bike was used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bike usage based on number of times used\n",
    "del(df1)\n",
    "bike_use_df = pd.DataFrame()\n",
    "bike_use_df = df.groupby(['Bike ID']).size().reset_index(name = 'Number of Times Used')\n",
    "bike_use_df = bike_use_df.sort_values('Number of Times Used', ascending = False)\n",
    "#bike_use_df.to_csv('Q5.csv')\n",
    "bike_use_df = bike_use_df[:10]\n",
    "bike_use_df['Bike ID'] = bike_use_df['Bike ID'].astype(str)\n",
    "bike_use_df['Bike ID'] = ('Bike ' + bike_use_df['Bike ID'])\n",
    "bike_use_df = bike_use_df.reset_index()\n",
    "#bike_use_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visual of most used bike based on Number of Trips\n",
    "ax8 = sns.barplot('Number of Times Used', 'Bike ID',data = bike_use_df, palette=\"GnBu_d\")\n",
    "ax8.set_title('Most Popular Bikes by Number of Times Used')\n",
    "for index, row in bike_use_df.iterrows():\n",
    "    ax8.text(row['Number of Times Used']-90,index,row['Number of Times Used'], color='white', ha=\"center\", fontsize =10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bike usage based on minutes used\n",
    "#del(ax8)\n",
    "bike_min_df = pd.DataFrame()\n",
    "bike_min_df['Minutes Used'] = df.groupby('Bike ID')['Minutes'].sum()\n",
    "bike_min_df = bike_min_df.reset_index()\n",
    "bike_min_df = bike_min_df.sort_values('Minutes Used', ascending = False)\n",
    "bike_min_df['Bike ID'] = bike_min_df['Bike ID'].astype(str)\n",
    "bike_min_df['Bike ID'] = ('Bike ' + bike_min_df['Bike ID'])\n",
    "bike_min_df = bike_min_df[:10]\n",
    "bike_min_df = bike_min_df.reset_index()\n",
    "#bike_min_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visual of most used bike based on number of minutes used\n",
    "ax9 = sns.barplot('Minutes Used', 'Bike ID',data = bike_min_df, palette=\"GnBu_d\")\n",
    "ax9.set_title('Most Popular Bikes by Minutes Used')\n",
    "rcParams['figure.figsize'] = 11,6\n",
    "for index, row in bike_min_df.iterrows():\n",
    "    ax9.text(row['Minutes Used']-2800,index,str(round(row['Minutes Used'],2))+' Minutes', \n",
    "             color='white', ha=\"center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6.1: Predictive Model - Baseline Model\n",
    "Ask: Build a model that can predict how long a trip will take given a starting \n",
    "     point and destination. \n",
    "\n",
    "***Assumptions on how the Kiosk will work:*** After speaking to Daniel Yawitz (if you're \n",
    "looking at this, thanks for the clarification), I was told that we should assume that when\n",
    "a user inputs the start and end station, they swipe their key fob (if they're a subscriber)\n",
    "and enter their info on the kiosk (if they're a \"Customer\") prior to entering the start \n",
    "and end station. This means that we would know their gender and age. Thus these variables\n",
    "can be used in building the model.\n",
    "\n",
    "Step 1.  \n",
    "   - This dataset is massive. Almost 14 million rows. Let's work on a *random* subsample while we build and evaluate models. If I tried to build and evaluate a model on the entire dataset, each run would take me ~10+ minutes depending on the model. One good way to decide what portion of your data to work with is using a learning curve. However, my kernel keeps crashing while trying to create that learning curve. However, given the size of the data and from experience by working with senior data scientists on projects with BAML and other firms I know that I can comfortably work with a few thoushand rows of data given the fact that this is only one year of data. If we were working with data for multiple years, I'd need to reconsider this approach. However, given the reasons above, I've decided to sample 10% of the data. It's stil ~1.3 million rows and should be a representative sample since it's randomly selected. I'll be evaluating my model on df_sample.\n",
    "  - I also made the same visuals on the sample to visualize the data.\n",
    "          \n",
    "          \n",
    "Step 2. \n",
    "  - Let's get a baseline. If I were to just run a simple multi-variate linear regression, what would my model look like and how accurate would it be? Need to prepare the data for a multivariate regression\n",
    "      1. Drop irrelevant columns\n",
    "           - Trip Duration: We have the minutes column, which is the target variable\n",
    "            - Stop Time: In the real world, we won't have this information when predicting the trip duration.\n",
    "             - Start Station ID: Start Station Name captures this information\n",
    "            - Start Station Latitude: Start Station Name captures this information\n",
    "            - Start Station Longitude: Start Station Name captures this information\n",
    "            - Start Coordinates: Start Station Name captures this information\n",
    "            - End Station ID: End Station Name captures this information\n",
    "           - End Station Latitude: End Station Name captures this information\n",
    "            - End Station Longitude: End Station Name captures this information\n",
    "            - End Coordinates: End Station Name captures this information\n",
    "             - Bike Id: We won't know what bike the user is going to end up using\n",
    "             - Min_Mile: Effectively the same information as end time when combined with distance. We won't have this information in the real world.\n",
    "             - mile_hour: Effectively the same information as end time when combined with distance. We won't have this information in the real world.\n",
    "                    (Speed * Distance = Trip Duration): Which is why speed is dropped\n",
    "             - Birth Year: Age captures this information\n",
    "             - Start Station Name and End Station Name: The distance variable captures the same information. For the model, if a user is inputting start and end station, we can build a simple function to calculate the distance which would capture the same information.\n",
    "      2. Basic cleaning of data FOR NOW.This is only being done for the baseline model\n",
    "          - Start Time: Requires reformatting. Will do this after baseline model\n",
    "           - Dumify categorical variables\n",
    "            - Scale Age\n",
    "            - Don't scale distance, since it does not just represent distance, but is also indicative of the trip the rider is making (start and station)\n",
    "      3. Anomalies in Trip Duration\n",
    "          - I'm going to come back to an observation from earlier. Any trip which lasts longer than *45 minutes(2,700 seconds)* probably indicates a stolen bike, incorrect docking of the bike, or an anomaly. No rider would plan to go over the maximum 45 minutes allowed. Additionally, there are only ~200k rows where the trips last longer than 45 minutes. That is less than 1% of the data and can be considered anomalies. Even if we include it, the model won't be able to learn from it due to the small sample. Lastly, as an avid Citibike user, I know first hand that it doesn't make any sense for one to use a bike for more than 45 minutes! The only way a trip actually lasts more than 45 minutes is if a user couldn't find a dock at their desired station. \n",
    "\n",
    "- Age was removed after an initial run indicated it had no effect on the model. This was also clearly indicated in some of the visuals above. Reasons for not binning age are in section 6.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Trip Duration</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "      <td>655111.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>455.48</td>\n",
       "      <td>3206.99</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>3203.33</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>24919.97</td>\n",
       "      <td>1980.02</td>\n",
       "      <td>7.59</td>\n",
       "      <td>7.59</td>\n",
       "      <td>0.70</td>\n",
       "      <td>37.98</td>\n",
       "      <td>11.12</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>332.92</td>\n",
       "      <td>26.82</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>63.92</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>758.03</td>\n",
       "      <td>8.67</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.67</td>\n",
       "      <td>4.26</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>61.00</td>\n",
       "      <td>3183.00</td>\n",
       "      <td>40.69</td>\n",
       "      <td>-74.10</td>\n",
       "      <td>152.00</td>\n",
       "      <td>40.69</td>\n",
       "      <td>-74.10</td>\n",
       "      <td>14552.00</td>\n",
       "      <td>1956.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>237.00</td>\n",
       "      <td>3186.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>3186.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.05</td>\n",
       "      <td>24486.00</td>\n",
       "      <td>1976.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.41</td>\n",
       "      <td>32.00</td>\n",
       "      <td>8.41</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>354.00</td>\n",
       "      <td>3202.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>3198.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>24603.00</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.90</td>\n",
       "      <td>0.56</td>\n",
       "      <td>36.00</td>\n",
       "      <td>10.06</td>\n",
       "      <td>5.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>565.00</td>\n",
       "      <td>3211.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>3211.00</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-74.04</td>\n",
       "      <td>24712.00</td>\n",
       "      <td>1986.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.42</td>\n",
       "      <td>0.88</td>\n",
       "      <td>42.00</td>\n",
       "      <td>12.45</td>\n",
       "      <td>7.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>5985.00</td>\n",
       "      <td>3426.00</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-74.03</td>\n",
       "      <td>3426.00</td>\n",
       "      <td>40.78</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>29296.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>99.75</td>\n",
       "      <td>5.26</td>\n",
       "      <td>62.00</td>\n",
       "      <td>33.24</td>\n",
       "      <td>16.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count      655111.00         655111.00               655111.00   \n",
       "mean          455.48           3206.99                   40.72   \n",
       "std           332.92             26.82                    0.01   \n",
       "min            61.00           3183.00                   40.69   \n",
       "25%           237.00           3186.00                   40.72   \n",
       "50%           354.00           3202.00                   40.72   \n",
       "75%           565.00           3211.00                   40.73   \n",
       "max          5985.00           3426.00                   40.75   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count                655111.00       655111.00             655111.00   \n",
       "mean                    -74.05         3203.33                 40.72   \n",
       "std                       0.01           63.92                  0.01   \n",
       "min                     -74.10          152.00                 40.69   \n",
       "25%                     -74.05         3186.00                 40.72   \n",
       "50%                     -74.04         3198.00                 40.72   \n",
       "75%                     -74.04         3211.00                 40.73   \n",
       "max                     -74.03         3426.00                 40.78   \n",
       "\n",
       "       End Station Longitude    Bike ID  Birth Year  Trip_Duration_in_min  \\\n",
       "count              655111.00  655111.00   655111.00             655111.00   \n",
       "mean                  -74.05   24919.97     1980.02                  7.59   \n",
       "std                     0.01     758.03        8.67                  5.56   \n",
       "min                   -74.10   14552.00     1956.00                  1.00   \n",
       "25%                   -74.05   24486.00     1976.00                  4.00   \n",
       "50%                   -74.04   24603.00     1982.00                  6.00   \n",
       "75%                   -74.04   24712.00     1986.00                  9.00   \n",
       "max                   -73.96   29296.00     2000.00                100.00   \n",
       "\n",
       "         Minutes   Distance        Age   min_mile  mile_hour  \n",
       "count  655111.00  655111.00  655111.00  655111.00  655111.00  \n",
       "mean        7.59       0.70      37.98      11.12       6.00  \n",
       "std         5.55       0.45       8.67       4.26       1.78  \n",
       "min         1.02       0.09      18.00       3.62       1.81  \n",
       "25%         3.95       0.41      32.00       8.41       4.82  \n",
       "50%         5.90       0.56      36.00      10.06       5.96  \n",
       "75%         9.42       0.88      42.00      12.45       7.14  \n",
       "max        99.75       5.26      62.00      33.24      16.58  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.describe(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "- We have some unreasonable speeds in min_mile column, but that's fine. Some people may have walked with their bike or stopped at multiple destinations before docking.\n",
    "- The sample data below seems to be representative of the entire dataset above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cleaning up anomalies\n",
    "df = df.drop(df.index[(df['Trip Duration'] > 2700)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's work with a random sample of the data and inspect it thoroughly to ensure it's representative\n",
    "df_sample = df.sample(frac = 0.1, random_state = 0)\n",
    "#round(df_sample.describe(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()\n",
    "df2 = df.copy() \n",
    "#df = df.drop(['level_0', 'level_1'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Irrelevant data\n",
    "def drop_data(df):\n",
    "    df = df.drop(['Trip Duration','Stop Time','Start Station ID','Start Station Latitude','Start Station Longitude',\n",
    "                  'Start Coordinates','End Station ID', 'End Station Latitude', 'End Station Longitude', \n",
    "                  'End Coordinates','Bike ID', 'Start Station Name','Birth Year','End Station Name','min_mile',\n",
    "                  'mile_hour','Age'], axis = 1)\n",
    "    return df\n",
    "\n",
    "df_basemodel = drop_data(df_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_basemodel = df_basemodel.drop('Start Time', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_basemodel = pd.get_dummies(df_basemodel, drop_first = True)\n",
    "#df_basemodel.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trip_Duration_in_min    0.998603\n",
       "Minutes                 1.000000\n",
       "Distance                0.790130\n",
       "User Type_Subscriber   -0.243266\n",
       "Gender_1               -0.110553\n",
       "Gender_2                0.002184\n",
       "Name: Minutes, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_basemodel.corr().loc[:,'Minutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "X = df_basemodel.iloc[:,1:]\n",
    "#Target variable\n",
    "y = df_basemodel.iloc[:,0]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.997164986346871"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Trip_Duration_in_min</td> <th>  R-squared:         </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>    <th>  F-statistic:       </th> <td>3.757e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Sun, 10 Nov 2019</td>   <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>01:45:29</td>       <th>  Log-Likelihood:    </th> <td> -9302.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td> 52371</td>        <th>  AIC:               </th> <td>1.862e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td> 52365</td>        <th>  BIC:               </th> <td>1.867e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     5</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td>   -0.0057</td> <td>    0.007</td> <td>   -0.822</td> <td> 0.411</td> <td>   -0.019</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Minutes</th>              <td>    1.0001</td> <td>    0.000</td> <td> 2535.337</td> <td> 0.000</td> <td>    0.999</td> <td>    1.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    0.0007</td> <td>    0.005</td> <td>    0.145</td> <td> 0.885</td> <td>   -0.008</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>    0.0077</td> <td>    0.012</td> <td>    0.661</td> <td> 0.508</td> <td>   -0.015</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.0065</td> <td>    0.010</td> <td>   -0.651</td> <td> 0.515</td> <td>   -0.026</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td> 8.303e-05</td> <td>    0.010</td> <td>    0.008</td> <td> 0.994</td> <td>   -0.020</td> <td>    0.020</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>47107.390</td> <th>  Durbin-Watson:     </th> <td>   1.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>3151.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.001</td>   <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.798</td>   <th>  Cond. No.          </th> <td>    132.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                             \n",
       "================================================================================\n",
       "Dep. Variable:     Trip_Duration_in_min   R-squared:                       0.997\n",
       "Model:                              OLS   Adj. R-squared:                  0.997\n",
       "Method:                   Least Squares   F-statistic:                 3.757e+06\n",
       "Date:                  Sun, 10 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                          01:45:29   Log-Likelihood:                -9302.2\n",
       "No. Observations:                 52371   AIC:                         1.862e+04\n",
       "Df Residuals:                     52365   BIC:                         1.867e+04\n",
       "Df Model:                             5                                         \n",
       "Covariance Type:              nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                   -0.0057      0.007     -0.822      0.411      -0.019       0.008\n",
       "Minutes                  1.0001      0.000   2535.337      0.000       0.999       1.001\n",
       "Distance                 0.0007      0.005      0.145      0.885      -0.008       0.010\n",
       "User Type_Subscriber     0.0077      0.012      0.661      0.508      -0.015       0.031\n",
       "Gender_1                -0.0065      0.010     -0.651      0.515      -0.026       0.013\n",
       "Gender_2              8.303e-05      0.010      0.008      0.994      -0.020       0.020\n",
       "==============================================================================\n",
       "Omnibus:                    47107.390   Durbin-Watson:                   1.997\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3151.492\n",
       "Skew:                           0.001   Prob(JB):                         0.00\n",
       "Kurtosis:                       1.798   Cond. No.                         132.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using Statsmodel because it has the summary function.\n",
    "import statsmodels.api as sm\n",
    "X_train = sm.add_constant(X_train)\n",
    "X_test = sm.add_constant(X_test)\n",
    "regressor_OLS = sm.OLS(y_train, X_train).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6.2: Predictive Model - Including Date \n",
    "\n",
    "**Baseline Model:** Adjusted R^2 and R^2 = 56.5% (depending on random_state)        \n",
    " 1. The baseline model is ok, but nothing spectacular. The R_Squared and Adjusted R_Squared is \n",
    "    pretty much the same, 53.6%. The F-Stat is also 0.00 which is a good sign. The gender female's \n",
    "    p-value is high, however, we can't loose this information due to the fact that this is a categorical\n",
    "    variable and we'd be loosing substantial information by dropping it. \n",
    " 2. Steps to make improvements:\n",
    "    a) Add back time in the following format\n",
    "      - Is the ride on a WEEKDAY or WEEKEND. Weekday, is rush-hour commute for the most part and\n",
    "          probably from home to work. Weekend could be a longer, more casual ride and have higher variability.\n",
    "      - Is the ride in the MORNING, AFTERNOON, EVENING, or NIGHT. The exact timing will be based on the \n",
    "          difference in trip duration based on time of day. Will have visuals below.\n",
    "      - What season is it?\n",
    "        - December - Feb. = Winter\n",
    "        - March - May = Spring\n",
    "        - June - Aug. = Summer\n",
    "        - Sept. - Nov. = Fall\n",
    " 3. Evaluate model and check correlation to ensure against collinearity and identify what's going on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Start Coordinates</th>\n",
       "      <th>End Coordinates</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>(40.734785818, -74.050443636)</td>\n",
       "      <td>(40.7376037, -74.05247829999998)</td>\n",
       "      <td>0.221838</td>\n",
       "      <td>58</td>\n",
       "      <td>28.25</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>...</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>(40.7287448, -74.03210820000002)</td>\n",
       "      <td>(40.72112360000001, -74.03805095)</td>\n",
       "      <td>0.611454</td>\n",
       "      <td>43</td>\n",
       "      <td>7.50</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7112423, -74.0557013)</td>\n",
       "      <td>1.217823</td>\n",
       "      <td>34</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>365</td>\n",
       "      <td>2015-10-01 00:41:46</td>\n",
       "      <td>2015-10-01 00:47:51</td>\n",
       "      <td>3198</td>\n",
       "      <td>Heights Elevator</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-74.040443</td>\n",
       "      <td>3215</td>\n",
       "      <td>Central Ave</td>\n",
       "      <td>40.746730</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>(40.748715945380226, -74.04044330120088)</td>\n",
       "      <td>(40.7467299, -74.0492509)</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>34</td>\n",
       "      <td>12.62</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>139</td>\n",
       "      <td>2015-10-01 00:43:44</td>\n",
       "      <td>2015-10-01 00:46:03</td>\n",
       "      <td>3206</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>40.731169</td>\n",
       "      <td>-74.057574</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>...</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>(40.7311689, -74.0575736)</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>30</td>\n",
       "      <td>7.08</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717653</td>\n",
       "      <td>239</td>\n",
       "      <td>2017-03-31 21:26:40</td>\n",
       "      <td>2017-03-31 21:30:39</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>-74.063784</td>\n",
       "      <td>3201</td>\n",
       "      <td>Dey St</td>\n",
       "      <td>40.737711</td>\n",
       "      <td>...</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>(40.737711, -74.066921)</td>\n",
       "      <td>0.508248</td>\n",
       "      <td>48</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717654</td>\n",
       "      <td>192</td>\n",
       "      <td>2017-03-31 21:46:57</td>\n",
       "      <td>2017-03-31 21:50:09</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3267</td>\n",
       "      <td>Morris Canal</td>\n",
       "      <td>40.712419</td>\n",
       "      <td>...</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7124188237569, -74.03852552175522)</td>\n",
       "      <td>0.374889</td>\n",
       "      <td>42</td>\n",
       "      <td>8.54</td>\n",
       "      <td>7.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717655</td>\n",
       "      <td>186</td>\n",
       "      <td>2017-03-31 22:03:56</td>\n",
       "      <td>2017-03-31 22:07:02</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>...</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.71277420000001, -74.03648570000001)</td>\n",
       "      <td>0.287536</td>\n",
       "      <td>62</td>\n",
       "      <td>10.78</td>\n",
       "      <td>5.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717656</td>\n",
       "      <td>618</td>\n",
       "      <td>2017-03-31 23:20:14</td>\n",
       "      <td>2017-03-31 23:30:32</td>\n",
       "      <td>3272</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "      <td>40.723332</td>\n",
       "      <td>-74.045953</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>(40.723331586464354, -74.04595255851744)</td>\n",
       "      <td>(40.71346382669195, -74.06285852193831)</td>\n",
       "      <td>1.118670</td>\n",
       "      <td>34</td>\n",
       "      <td>9.21</td>\n",
       "      <td>6.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717657</td>\n",
       "      <td>649</td>\n",
       "      <td>2017-03-31 23:25:34</td>\n",
       "      <td>2017-03-31 23:36:23</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3190</td>\n",
       "      <td>Garfield Ave Station</td>\n",
       "      <td>40.710467</td>\n",
       "      <td>...</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>10.816667</td>\n",
       "      <td>(40.7177325, -74.04384499999998)</td>\n",
       "      <td>(40.7104670233797, -74.07003879547119)</td>\n",
       "      <td>1.463817</td>\n",
       "      <td>30</td>\n",
       "      <td>7.39</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654640 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trip Duration          Start Time           Stop Time  \\\n",
       "0                 376 2015-10-01 00:16:26 2015-10-01 00:22:42   \n",
       "3                 275 2015-10-01 00:34:31 2015-10-01 00:39:06   \n",
       "4                 561 2015-10-01 00:40:12 2015-10-01 00:49:33   \n",
       "5                 365 2015-10-01 00:41:46 2015-10-01 00:47:51   \n",
       "6                 139 2015-10-01 00:43:44 2015-10-01 00:46:03   \n",
       "...               ...                 ...                 ...   \n",
       "717653            239 2017-03-31 21:26:40 2017-03-31 21:30:39   \n",
       "717654            192 2017-03-31 21:46:57 2017-03-31 21:50:09   \n",
       "717655            186 2017-03-31 22:03:56 2017-03-31 22:07:02   \n",
       "717656            618 2017-03-31 23:20:14 2017-03-31 23:30:32   \n",
       "717657            649 2017-03-31 23:25:34 2017-03-31 23:36:23   \n",
       "\n",
       "        Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0                   3212    Christ Hospital               40.734786   \n",
       "3                   3199       Newport Pkwy               40.728745   \n",
       "4                   3183     Exchange Place               40.716247   \n",
       "5                   3198   Heights Elevator               40.748716   \n",
       "6                   3206            Hilltop               40.731169   \n",
       "...                  ...                ...                     ...   \n",
       "717653              3195            Sip Ave               40.730743   \n",
       "717654              3183     Exchange Place               40.716247   \n",
       "717655              3183     Exchange Place               40.716247   \n",
       "717656              3272       Jersey & 3rd               40.723332   \n",
       "717657              3185          City Hall               40.717732   \n",
       "\n",
       "        Start Station Longitude  End Station ID      End Station Name  \\\n",
       "0                    -74.050444            3207           Oakland Ave   \n",
       "3                    -74.032108            3187             Warren St   \n",
       "4                    -74.033459            3192    Liberty Light Rail   \n",
       "5                    -74.040443            3215           Central Ave   \n",
       "6                    -74.057574            3195               Sip Ave   \n",
       "...                         ...             ...                   ...   \n",
       "717653               -74.063784            3201                Dey St   \n",
       "717654               -74.033459            3267          Morris Canal   \n",
       "717655               -74.033459            3214      Essex Light Rail   \n",
       "717656               -74.045953            3268        Lafayette Park   \n",
       "717657               -74.043845            3190  Garfield Ave Station   \n",
       "\n",
       "        End Station Latitude  ...  Birth Year  Gender Trip_Duration_in_min  \\\n",
       "0                  40.737604  ...      1960.0       1                    6   \n",
       "3                  40.721124  ...      1975.0       1                    5   \n",
       "4                  40.711242  ...      1984.0       0                    9   \n",
       "5                  40.746730  ...      1984.0       0                    6   \n",
       "6                  40.730743  ...      1988.0       1                    2   \n",
       "...                      ...  ...         ...     ...                  ...   \n",
       "717653             40.737711  ...      1970.0       1                    4   \n",
       "717654             40.712419  ...      1976.0       1                    3   \n",
       "717655             40.712774  ...      1956.0       1                    3   \n",
       "717656             40.713464  ...      1984.0       1                   10   \n",
       "717657             40.710467  ...      1988.0       1                   11   \n",
       "\n",
       "          Minutes                         Start Coordinates  \\\n",
       "0        6.266667             (40.734785818, -74.050443636)   \n",
       "3        4.583333          (40.7287448, -74.03210820000002)   \n",
       "4        9.350000                 (40.7162469, -74.0334588)   \n",
       "5        6.083333  (40.748715945380226, -74.04044330120088)   \n",
       "6        2.316667                 (40.7311689, -74.0575736)   \n",
       "...           ...                                       ...   \n",
       "717653   3.983333  (40.730742625306576, -74.06378388404845)   \n",
       "717654   3.200000                 (40.7162469, -74.0334588)   \n",
       "717655   3.100000                 (40.7162469, -74.0334588)   \n",
       "717656  10.300000  (40.723331586464354, -74.04595255851744)   \n",
       "717657  10.816667          (40.7177325, -74.04384499999998)   \n",
       "\n",
       "                                 End Coordinates  Distance Age min_mile  \\\n",
       "0               (40.7376037, -74.05247829999998)  0.221838  58    28.25   \n",
       "3              (40.72112360000001, -74.03805095)  0.611454  43     7.50   \n",
       "4                      (40.7112423, -74.0557013)  1.217823  34     7.68   \n",
       "5                      (40.7467299, -74.0492509)  0.482094  34    12.62   \n",
       "6       (40.730742625306576, -74.06378388404845)  0.327309  30     7.08   \n",
       "...                                          ...       ...  ..      ...   \n",
       "717653                   (40.737711, -74.066921)  0.508248  48     7.84   \n",
       "717654    (40.7124188237569, -74.03852552175522)  0.374889  42     8.54   \n",
       "717655   (40.71277420000001, -74.03648570000001)  0.287536  62    10.78   \n",
       "717656   (40.71346382669195, -74.06285852193831)  1.118670  34     9.21   \n",
       "717657    (40.7104670233797, -74.07003879547119)  1.463817  30     7.39   \n",
       "\n",
       "        mile_hour  \n",
       "0            2.12  \n",
       "3            8.00  \n",
       "4            7.81  \n",
       "5            4.75  \n",
       "6            8.48  \n",
       "...           ...  \n",
       "717653       7.66  \n",
       "717654       7.03  \n",
       "717655       5.57  \n",
       "717656       6.52  \n",
       "717657       8.12  \n",
       "\n",
       "[654640 rows x 23 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The following code was used to create get_date_info\n",
    "'''\n",
    "#Weekday or Weekend: 1 = Weekday, 0 = Weekend\n",
    "df_model1['d_week'] = (df_model1['d_week']<5).astype(int)\n",
    "\n",
    "#Season of the year: Winter = 0, Spring = 1, Summer = 2, Fall = 3\n",
    "df_model1['m_yr'] = df_model1['m_yr'].replace(to_replace=[12,1,2], value = 0)\n",
    "df_model1['m_yr'] = df_model1['m_yr'].replace(to_replace=[3,4,5], value = 1)\n",
    "df_model1['m_yr'] = df_model1['m_yr'].replace(to_replace=[6,7,8], value = 2)\n",
    "df_model1['m_yr'] = df_model1['m_yr'].replace(to_replace=[9,10,11], value = 3)\n",
    "\n",
    "#Visualize difference in ridership based on time of day\n",
    "#Ploted both mean and media, similar pattern, using median due to the high variance in the data\n",
    "fig3, ax10 = plt.subplots(figsize=(15,7))\n",
    "df_model1.groupby('ToD').mean()['Minutes'].plot(ax=ax10)\n",
    "#df_model1.groupby('ToD').median()['Minutes'].plot(ax=ax10)\n",
    "plt.ylabel('Median Minutes per Trip')\n",
    "plt.xlabel('Hour of the Day')\n",
    "plt.title('Median Minutes per Trip by Time of Day')\n",
    "plt.show()\n",
    "\n",
    "#Based on the visual above and distribution of ridership: NIGHT = 20-5, MORNING = 5-9, AFTERNON = 9-14, EVENING = 14-20\n",
    "df_model1['ToD'] = pd.cut(df_model1['ToD'], bins=[-1, 5, 9, 14, 20, 25], labels=['Night','Morning','Afternoon','Evening','Night1'])\n",
    "df_model1['ToD'] = df_model1['ToD'].replace(to_replace='Night1', value = 'Night')\n",
    "df_model1['ToD'] = df_model1['ToD'].cat.remove_unused_categories()\n",
    "\n",
    "#Convert m_yr to categorical\n",
    "df_model1['m_yr'] = df_model1['m_yr'].astype('category')\n",
    "\n",
    "\n",
    "#Dummify categorical data and avoid \n",
    "df_model1 = pd.get_dummies(df_model1, drop_first = True)\n",
    "'''\n",
    "def get_date_info(df):\n",
    "    df['d_week'] = df['Start Time'].dt.dayofweek\n",
    "    df['m_yr'] = df['Start Time'].dt.month\n",
    "    df['ToD'] = df['Start Time'].dt.hour\n",
    "\n",
    "    df['d_week'] = (df['d_week']<5).astype(int)\n",
    "\n",
    "    df['m_yr'] = df['m_yr'].replace(to_replace=[12,1,2], value = 0)\n",
    "    df['m_yr'] = df['m_yr'].replace(to_replace=[3,4,5], value = 1)\n",
    "    df['m_yr'] = df['m_yr'].replace(to_replace=[6,7,8], value = 2)\n",
    "    df['m_yr'] = df['m_yr'].replace(to_replace=[9,10,11], value = 3)\n",
    "    \n",
    "    df['ToD'] = pd.cut(df['ToD'], bins=[-1, 5, 9, 14, 20, 25], labels=['Night','Morning','Afternoon','Evening','Night1'])\n",
    "    df['ToD'] = df['ToD'].replace(to_replace='Night1', value = 'Night')\n",
    "    df['ToD'] = df['ToD'].cat.remove_unused_categories()\n",
    "    \n",
    "    df['m_yr'] = df['m_yr'].astype('category')\n",
    "    df['d_week'] = df['d_week'].astype('category')\n",
    "\n",
    "    return(df)\n",
    "df_women2 = df.copy()\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>User Type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Distance</th>\n",
       "      <th>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>615711</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.516667</td>\n",
       "      <td>0.292686</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>360706</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>0.585072</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>509455</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.683333</td>\n",
       "      <td>0.287536</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37055</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.529657</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>251923</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>11.533333</td>\n",
       "      <td>1.100923</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>241289</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.256328</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>654496</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>0.376587</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>410536</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>1.456659</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550893</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>0.298241</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122106</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>1.141633</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65464 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User Type Gender  Trip_Duration_in_min    Minutes  Distance d_week  \\\n",
       "615711  Subscriber      1                     3   2.516667  0.292686      1   \n",
       "360706  Subscriber      1                     5   4.750000  0.585072      1   \n",
       "509455  Subscriber      1                     3   2.683333  0.287536      1   \n",
       "37055   Subscriber      2                     4   4.000000  0.529657      0   \n",
       "251923  Subscriber      2                    12  11.533333  1.100923      0   \n",
       "...            ...    ...                   ...        ...       ...    ...   \n",
       "241289  Subscriber      1                     8   7.650000  0.256328      1   \n",
       "654496  Subscriber      2                     6   5.650000  0.376587      1   \n",
       "410536  Subscriber      1                    16  15.550000  1.456659      0   \n",
       "550893  Subscriber      1                     3   3.350000  0.298241      1   \n",
       "122106  Subscriber      1                    11  10.650000  1.141633      0   \n",
       "\n",
       "       m_yr        ToD  \n",
       "615711    3      Night  \n",
       "360706    1    Morning  \n",
       "509455    2    Morning  \n",
       "37055     3  Afternoon  \n",
       "251923    3  Afternoon  \n",
       "...     ...        ...  \n",
       "241289    3    Evening  \n",
       "654496    0    Evening  \n",
       "410536    0    Evening  \n",
       "550893    2    Morning  \n",
       "122106    1    Evening  \n",
       "\n",
       "[65464 rows x 8 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataset for the second model\n",
    "df_model1 = drop_data(df_sample)\n",
    "df_model1 = get_date_info(df_model1)\n",
    "df_model1 = df_model1.drop('Start Time', axis =1)\n",
    "df_model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_model1 = pd.get_dummies(df_model1, drop_first = True)\n",
    "#df_model1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "X = df_model1.iloc[:,1:]\n",
    "#Target variable\n",
    "y = df_model1.iloc[:,0]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9971643529059063"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Trip_Duration_in_min</td> <th>  R-squared:         </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>    <th>  F-statistic:       </th> <td>1.565e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Sun, 10 Nov 2019</td>   <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>01:45:30</td>       <th>  Log-Likelihood:    </th> <td> -9294.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td> 52371</td>        <th>  AIC:               </th> <td>1.862e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td> 52358</td>        <th>  BIC:               </th> <td>1.873e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>    12</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td>   -0.0038</td> <td>    0.008</td> <td>   -0.445</td> <td> 0.656</td> <td>   -0.020</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Minutes</th>              <td>    1.0000</td> <td>    0.000</td> <td> 2492.197</td> <td> 0.000</td> <td>    0.999</td> <td>    1.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    0.0021</td> <td>    0.005</td> <td>    0.442</td> <td> 0.658</td> <td>   -0.007</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>    0.0098</td> <td>    0.012</td> <td>    0.834</td> <td> 0.404</td> <td>   -0.013</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.0072</td> <td>    0.010</td> <td>   -0.727</td> <td> 0.467</td> <td>   -0.027</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>   -0.0005</td> <td>    0.010</td> <td>   -0.044</td> <td> 0.965</td> <td>   -0.020</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>   -0.0045</td> <td>    0.003</td> <td>   -1.360</td> <td> 0.174</td> <td>   -0.011</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>    0.0083</td> <td>    0.004</td> <td>    2.081</td> <td> 0.037</td> <td>    0.000</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>    0.0019</td> <td>    0.004</td> <td>    0.476</td> <td> 0.634</td> <td>   -0.006</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>    0.0013</td> <td>    0.004</td> <td>    0.369</td> <td> 0.712</td> <td>   -0.006</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.0063</td> <td>    0.005</td> <td>   -1.386</td> <td> 0.166</td> <td>   -0.015</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>   -0.0045</td> <td>    0.005</td> <td>   -0.932</td> <td> 0.352</td> <td>   -0.014</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>    0.0015</td> <td>    0.004</td> <td>    0.346</td> <td> 0.730</td> <td>   -0.007</td> <td>    0.010</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>46875.349</td> <th>  Durbin-Watson:     </th> <td>   1.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>3148.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.001</td>   <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.799</td>   <th>  Cond. No.          </th> <td>    133.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                             \n",
       "================================================================================\n",
       "Dep. Variable:     Trip_Duration_in_min   R-squared:                       0.997\n",
       "Model:                              OLS   Adj. R-squared:                  0.997\n",
       "Method:                   Least Squares   F-statistic:                 1.565e+06\n",
       "Date:                  Sun, 10 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                          01:45:30   Log-Likelihood:                -9294.6\n",
       "No. Observations:                 52371   AIC:                         1.862e+04\n",
       "Df Residuals:                     52358   BIC:                         1.873e+04\n",
       "Df Model:                            12                                         \n",
       "Covariance Type:              nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                   -0.0038      0.008     -0.445      0.656      -0.020       0.013\n",
       "Minutes                  1.0000      0.000   2492.197      0.000       0.999       1.001\n",
       "Distance                 0.0021      0.005      0.442      0.658      -0.007       0.011\n",
       "User Type_Subscriber     0.0098      0.012      0.834      0.404      -0.013       0.033\n",
       "Gender_1                -0.0072      0.010     -0.727      0.467      -0.027       0.012\n",
       "Gender_2                -0.0005      0.010     -0.044      0.965      -0.020       0.020\n",
       "d_week_1                -0.0045      0.003     -1.360      0.174      -0.011       0.002\n",
       "m_yr_1                   0.0083      0.004      2.081      0.037       0.000       0.016\n",
       "m_yr_2                   0.0019      0.004      0.476      0.634      -0.006       0.010\n",
       "m_yr_3                   0.0013      0.004      0.369      0.712      -0.006       0.008\n",
       "ToD_Morning             -0.0063      0.005     -1.386      0.166      -0.015       0.003\n",
       "ToD_Afternoon           -0.0045      0.005     -0.932      0.352      -0.014       0.005\n",
       "ToD_Evening              0.0015      0.004      0.346      0.730      -0.007       0.010\n",
       "==============================================================================\n",
       "Omnibus:                    46875.349   Durbin-Watson:                   1.997\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3148.728\n",
       "Skew:                           0.001   Prob(JB):                         0.00\n",
       "Kurtosis:                       1.799   Cond. No.                         133.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using Statsmodel because it has the summary function.\n",
    "import statsmodels.api as sm\n",
    "X_train = sm.add_constant(X_train)\n",
    "X_test = sm.add_constant(X_test)\n",
    "regressor_OLS = sm.OLS(y_train, X_train).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trip_Duration_in_min    0.998603\n",
       "Minutes                 1.000000\n",
       "Distance                0.790130\n",
       "User Type_Subscriber   -0.243266\n",
       "Gender_1               -0.110553\n",
       "Gender_2                0.002184\n",
       "d_week_1               -0.099507\n",
       "m_yr_1                 -0.012380\n",
       "m_yr_2                  0.078114\n",
       "m_yr_3                  0.006514\n",
       "ToD_Morning            -0.090699\n",
       "ToD_Afternoon           0.071367\n",
       "ToD_Evening             0.051769\n",
       "Name: Minutes, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model1.corr().loc[:,'Minutes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6.3: Predictive Model - Improving Model 1\n",
    "Model 1: Negligible improvement in R^2: 67% (depending on random_state used)\n",
    "\n",
    "   - Date seems to not have as big of an impact. May be more useful to split time and day differently, but\n",
    "    acording to the graphs, there is no indication of a better split. Next steps will be to factor in speed\n",
    "    and distance based on Gender and Trip. By not being able to encode start and end stations (due to the\n",
    "    sheer number of points), we are losing crucial information on the trip itself. We need another proxy for\n",
    "    those measures. \n",
    "   - Another change I could make is to bin age into buckets. However, the data indicates that age has no correlation\n",
    "    or effect on the trip duration. This is counter-intuitive, however, I don't have a good reason to refute the data.\n",
    "\n",
    "Next Steps:\n",
    " 1. Include Average Speed based on: Trip and User Type\n",
    "    - Reason for Trip: \n",
    "      - Some trips are up hill, others are down hill. Some routes, such as one through times square involve heavy trafic, based on intuition.\n",
    "     - Reason for User Type: \n",
    "        - Tourists (Customers), will usually ride more slowly with frequent stops than a Subscriber, according to the data.\n",
    " 2. Include average duration for each trip based on: Trip and User Type\n",
    "    - Reason, for each, same as above.\n",
    "      - Using seconds instead of minutes for granularity in information.\n",
    "\n",
    " 3. A bit of inexperience here:\n",
    "    - The imputed columns are \"dependent\" variables in the since speed is derived from trip duration and distance. \n",
    "      - There may be a better way to do this, however, I would need to consult someone. Would appreciate feedback on this.\n",
    "      - I don't think it should be a big issue that they're \"dependent\" since they serve more as anchor points. But I'd appreciate feeback."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code below used to create get_speed_distance\n",
    "'''\n",
    "df_model2 = df_sample\n",
    "\n",
    "df_model2['Start Station Name'] = df_model2['Start Station Name'].astype(str)\n",
    "df_model2['End Station Name'] = df_model2['End Station Name'].astype(str)\n",
    "df_model2['Trip'] = df_model2['Start Station Name'] + ' to ' + df_model2['End Station Name']\n",
    "df_model2['Trip'] = df_model2['Trip'].astype('category')\n",
    "\n",
    "df_model2['avg_speed'] = df_model2.groupby(['Trip','User Type'])['mile_hour'].transform('mean')\n",
    "df_model2['avg_duration'] = df_model2.groupby(['Trip','User Type'])['Trip Duration'].transform('median')\n",
    "#Code to make sure transfrom function works appropriately.\n",
    "#df_model2[df_model2['avg_speed']==df_model2.loc[258881]['avg_speed']]\n",
    "'''\n",
    "def get_speed_distance(df):\n",
    "\n",
    "    df['Start Station Name'] = df['Start Station Name'].astype(str)\n",
    "    df['End Station Name'] = df['End Station Name'].astype(str)\n",
    "    df['Trip'] = df['Start Station Name'] + ' to ' + df['End Station Name']\n",
    "    df['Trip'] = df['Trip'].astype('category')\n",
    "    \n",
    "    df['avg_speed'] = df.groupby(['Trip','User Type'])['mile_hour'].transform('mean')\n",
    "    df['avg_duration'] = df.groupby(['Trip','User Type'])['Trip Duration'].transform('median')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Irrelevant data, updated with Trip\n",
    "def drop_data_trip(df):\n",
    "    df = df.drop(['Trip Duration','Stop Time','Start Station ID','Start Station Latitude','Start Station Longitude',\n",
    "                  'Start Coordinates','End Station ID', 'End Station Latitude', 'End Station Longitude', \n",
    "                  'End Coordinates','Bike ID', 'Start Station Name','Birth Year','End Station Name','min_mile', \n",
    "                  'mile_hour', 'Age', 'Trip'], axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_model2 = get_speed_distance(df_sample)\n",
    "df_model2 = drop_data_trip(df_model2)\n",
    "df_model2 = get_date_info(df_model2)\n",
    "df_model2 = df_model2.drop('Start Time', axis =1)\n",
    "df_model2=df_model2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_model2 = pd.get_dummies(df_model2, drop_first = True)\n",
    "#df_model2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "X = df_model2.iloc[:,1:]\n",
    "#Target variable\n",
    "y = df_model2.iloc[:,0]\n",
    "#Split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973379568487214"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Trip_Duration_in_min</td> <th>  R-squared:         </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>    <th>  F-statistic:       </th> <td>8.430e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Sun, 10 Nov 2019</td>   <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>01:45:30</td>       <th>  Log-Likelihood:    </th> <td> -5602.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td> 31302</td>        <th>  AIC:               </th> <td>1.124e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td> 31287</td>        <th>  BIC:               </th> <td>1.136e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>    14</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td>    0.0037</td> <td>    0.013</td> <td>    0.279</td> <td> 0.780</td> <td>   -0.022</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Minutes</th>              <td>    1.0005</td> <td>    0.000</td> <td> 2043.887</td> <td> 0.000</td> <td>    1.000</td> <td>    1.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>   -0.0050</td> <td>    0.006</td> <td>   -0.868</td> <td> 0.385</td> <td>   -0.016</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_speed</th>            <td>   -0.0008</td> <td>    0.001</td> <td>   -0.701</td> <td> 0.484</td> <td>   -0.003</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_duration</th>         <td>-1.214e-06</td> <td> 3.63e-06</td> <td>   -0.335</td> <td> 0.738</td> <td>-8.32e-06</td> <td> 5.89e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>    0.0077</td> <td>    0.015</td> <td>    0.505</td> <td> 0.613</td> <td>   -0.022</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.0071</td> <td>    0.013</td> <td>   -0.536</td> <td> 0.592</td> <td>   -0.033</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>   -0.0043</td> <td>    0.013</td> <td>   -0.318</td> <td> 0.751</td> <td>   -0.031</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>    0.0018</td> <td>    0.004</td> <td>    0.431</td> <td> 0.666</td> <td>   -0.006</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>    0.0063</td> <td>    0.005</td> <td>    1.205</td> <td> 0.228</td> <td>   -0.004</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>    0.0007</td> <td>    0.005</td> <td>    0.140</td> <td> 0.889</td> <td>   -0.009</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>    0.0022</td> <td>    0.005</td> <td>    0.487</td> <td> 0.626</td> <td>   -0.007</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.0078</td> <td>    0.006</td> <td>   -1.300</td> <td> 0.194</td> <td>   -0.020</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>   -0.0050</td> <td>    0.006</td> <td>   -0.791</td> <td> 0.429</td> <td>   -0.017</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>   -0.0008</td> <td>    0.006</td> <td>   -0.143</td> <td> 0.886</td> <td>   -0.012</td> <td>    0.010</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>28898.569</td> <th>  Durbin-Watson:     </th> <td>   1.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1893.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.008</td>   <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.795</td>   <th>  Cond. No.          </th> <td>1.28e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.28e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                             \n",
       "================================================================================\n",
       "Dep. Variable:     Trip_Duration_in_min   R-squared:                       0.997\n",
       "Model:                              OLS   Adj. R-squared:                  0.997\n",
       "Method:                   Least Squares   F-statistic:                 8.430e+05\n",
       "Date:                  Sun, 10 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                          01:45:30   Log-Likelihood:                -5602.6\n",
       "No. Observations:                 31302   AIC:                         1.124e+04\n",
       "Df Residuals:                     31287   BIC:                         1.136e+04\n",
       "Df Model:                            14                                         \n",
       "Covariance Type:              nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                    0.0037      0.013      0.279      0.780      -0.022       0.030\n",
       "Minutes                  1.0005      0.000   2043.887      0.000       1.000       1.001\n",
       "Distance                -0.0050      0.006     -0.868      0.385      -0.016       0.006\n",
       "avg_speed               -0.0008      0.001     -0.701      0.484      -0.003       0.001\n",
       "avg_duration         -1.214e-06   3.63e-06     -0.335      0.738   -8.32e-06    5.89e-06\n",
       "User Type_Subscriber     0.0077      0.015      0.505      0.613      -0.022       0.038\n",
       "Gender_1                -0.0071      0.013     -0.536      0.592      -0.033       0.019\n",
       "Gender_2                -0.0043      0.013     -0.318      0.751      -0.031       0.022\n",
       "d_week_1                 0.0018      0.004      0.431      0.666      -0.006       0.010\n",
       "m_yr_1                   0.0063      0.005      1.205      0.228      -0.004       0.017\n",
       "m_yr_2                   0.0007      0.005      0.140      0.889      -0.009       0.011\n",
       "m_yr_3                   0.0022      0.005      0.487      0.626      -0.007       0.011\n",
       "ToD_Morning             -0.0078      0.006     -1.300      0.194      -0.020       0.004\n",
       "ToD_Afternoon           -0.0050      0.006     -0.791      0.429      -0.017       0.007\n",
       "ToD_Evening             -0.0008      0.006     -0.143      0.886      -0.012       0.010\n",
       "==============================================================================\n",
       "Omnibus:                    28898.569   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1893.582\n",
       "Skew:                          -0.008   Prob(JB):                         0.00\n",
       "Kurtosis:                       1.795   Cond. No.                     1.28e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.28e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using Statsmodel because it has the summary function.\n",
    "import statsmodels.api as sm\n",
    "X_train = sm.add_constant(X_train)\n",
    "X_test = sm.add_constant(X_test)\n",
    "regressor_OLS = sm.OLS(y_train, X_train).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Part 6.4: Predictive Model - Improving Model 2 \n",
    "Model 2 is VERY VERY good.\n",
    "\n",
    "The model above is extremely good. But can I make it even better?\n",
    "\n",
    "- One of the grading requirements for the project is to include an additional data source. Weather and traffic make the most sense. I could include pedestrian traffic data from citibike's website, however, I don't think it'll be too helpful due to the fact that there are now bikelanes in NYC. I could use weather data as well.\n",
    "    - Concerns with using weather data:\n",
    "        - Weather dictates whether or not a rider will bike, not how long they will bike. For weather to determine\n",
    "        how long one will bike, it would have to dictate where they go. However, since grading deends on my ability\n",
    "        to use external data, I'm going to test this hypothesis. If weather is not a strong indicatior, I will remove\n",
    "        it in the next model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load and clean weather data from 2017 (Min Temp, Max Temp, Precipitation)\n",
    "df_weather = pd.read_csv('Files/weather2017.csv')\n",
    "df_weather = df_weather.iloc[:,1:5]\n",
    "df_weather['DATE'] = to_datetime(df_weather['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Following code was used to create get_weather()\n",
    "'''\n",
    "df_model3['DATE'] = to_datetime(df_model3['Start Time'].dt.date)\n",
    "\n",
    "\n",
    "df_w = df_weather\n",
    "df_mod = df_model3    \n",
    "\n",
    "df_w.head()\n",
    "df_mod.dtypes\n",
    "test = df_mod.merge(df_w, on = 'DATE', how = 'left')\n",
    "test.head()\n",
    "\n",
    "df = df_model3.merge(df_weather, on = 'DATE', how = 'left')\n",
    "'''\n",
    "\n",
    "def get_weather(df):\n",
    "    df['DATE'] = to_datetime(df['Start Time'].dt.date)\n",
    "    df = df.merge(df_weather, on = 'DATE', how = 'left')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        User Type Gender  Trip_Duration_in_min    Minutes  Distance  \\\n",
       "1      Subscriber      1                     5   4.750000  0.585072   \n",
       "5      Subscriber      1                    19  19.316667  1.552934   \n",
       "30     Subscriber      1                    17  16.783333  1.668429   \n",
       "47     Subscriber      0                     2   2.233333  0.253726   \n",
       "62     Subscriber      1                     9   8.583333  0.966462   \n",
       "...           ...    ...                   ...        ...       ...   \n",
       "65412  Subscriber      1                     3   2.916667  0.416431   \n",
       "65414  Subscriber      1                     9   8.783333  0.694166   \n",
       "65419  Subscriber      1                     3   3.200000  0.423763   \n",
       "65422  Subscriber      1                     6   5.933333  0.759130   \n",
       "65431  Subscriber      2                     3   2.666667  0.253551   \n",
       "\n",
       "       avg_speed  avg_duration d_week m_yr      ToD  PRCP  TMAX  TMIN  \n",
       "1       2.360000         326.0      1    1  Morning  0.72  46.0  41.0  \n",
       "5       4.257500         240.0      1    1    Night  0.00  39.0  24.0  \n",
       "30      5.013333         495.0      1    1  Morning  0.00  45.0  26.0  \n",
       "47      4.818333        1545.5      0    0  Evening  0.05  48.0  43.0  \n",
       "62      5.920000        1274.0      1    1  Evening  0.00  39.0  24.0  \n",
       "...          ...           ...    ...  ...      ...   ...   ...   ...  \n",
       "65412   4.665000        1513.0      1    1    Night  0.00  42.0  23.0  \n",
       "65414   3.550000         653.0      1    1  Morning  0.72  46.0  41.0  \n",
       "65419   4.330000         480.0      1    0  Evening  0.00  49.0  39.0  \n",
       "65422   5.737500         670.0      1    1  Morning  0.00  64.0  35.0  \n",
       "65431   4.703333         430.0      1    1  Morning  0.00  50.0  24.0  \n",
       "\n",
       "[5795 rows x 13 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create dataset to test the model on\n",
    "df_model3 = get_speed_distance(df_sample)\n",
    "df_model3 = drop_data(df_model3)\n",
    "df_model3 = get_date_info(df_model3)\n",
    "df_model3 = get_weather(df_model3)\n",
    "df_model3 = df_model3.drop(['Start Time','Trip','DATE'], axis =1)\n",
    "df_model3=df_model3.dropna()\n",
    "df_model3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "      <td>65422</td>\n",
       "      <td>6</td>\n",
       "      <td>5.933333</td>\n",
       "      <td>0.759130</td>\n",
       "      <td>5.737500</td>\n",
       "      <td>-0.249599</td>\n",
       "      <td>-0.379517</td>\n",
       "      <td>1.304939</td>\n",
       "      <td>-0.040115</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65431</td>\n",
       "      <td>3</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.253551</td>\n",
       "      <td>4.703333</td>\n",
       "      <td>-0.770757</td>\n",
       "      <td>-0.379517</td>\n",
       "      <td>0.019925</td>\n",
       "      <td>-1.208809</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5795 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip_Duration_in_min    Minutes  Distance  avg_speed  avg_duration  \\\n",
       "1                         5   4.750000  0.585072   2.360000     -0.996593   \n",
       "5                        19  19.316667  1.552934   4.257500     -1.183341   \n",
       "30                       17  16.783333  1.668429   5.013333     -0.629610   \n",
       "47                        2   2.233333  0.253726   4.818333      1.651543   \n",
       "62                        9   8.583333  0.966462   5.920000      1.061983   \n",
       "...                     ...        ...       ...        ...           ...   \n",
       "65412                     3   2.916667  0.416431   4.665000      1.580970   \n",
       "65414                     9   8.783333  0.694166   3.550000     -0.286514   \n",
       "65419                     3   3.200000  0.423763   4.330000     -0.662183   \n",
       "65422                     6   5.933333  0.759130   5.737500     -0.249599   \n",
       "65431                     3   2.666667  0.253551   4.703333     -0.770757   \n",
       "\n",
       "           PRCP      TMAX      TMIN  User Type_Subscriber  Gender_1  Gender_2  \\\n",
       "1      3.179858 -0.347221  0.597355                     1         1         0   \n",
       "5     -0.379517 -0.989728 -1.208809                     1         1         0   \n",
       "30    -0.379517 -0.439008 -0.996319                     1         1         0   \n",
       "47    -0.132338 -0.163648  0.809845                     1         0         0   \n",
       "62    -0.379517 -0.989728 -1.208809                     1         1         0   \n",
       "...         ...       ...       ...                   ...       ...       ...   \n",
       "65412 -0.379517 -0.714368 -1.315054                     1         1         0   \n",
       "65414  3.179858 -0.347221  0.597355                     1         1         0   \n",
       "65419 -0.379517 -0.071861  0.384865                     1         1         0   \n",
       "65422 -0.379517  1.304939 -0.040115                     1         1         0   \n",
       "65431 -0.379517  0.019925 -1.208809                     1         0         1   \n",
       "\n",
       "       d_week_1  m_yr_1  m_yr_2  m_yr_3  ToD_Morning  ToD_Afternoon  \\\n",
       "1             1       1       0       0            1              0   \n",
       "5             1       1       0       0            0              0   \n",
       "30            1       1       0       0            1              0   \n",
       "47            0       0       0       0            0              0   \n",
       "62            1       1       0       0            0              0   \n",
       "...         ...     ...     ...     ...          ...            ...   \n",
       "65412         1       1       0       0            0              0   \n",
       "65414         1       1       0       0            1              0   \n",
       "65419         1       0       0       0            0              0   \n",
       "65422         1       1       0       0            1              0   \n",
       "65431         1       1       0       0            1              0   \n",
       "\n",
       "       ToD_Evening  \n",
       "1                0  \n",
       "5                0  \n",
       "30               0  \n",
       "47               1  \n",
       "62               1  \n",
       "...            ...  \n",
       "65412            0  \n",
       "65414            0  \n",
       "65419            1  \n",
       "65422            0  \n",
       "65431            0  \n",
       "\n",
       "[5795 rows x 18 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_model3 = pd.get_dummies(df_model3, drop_first = True)\n",
    "\n",
    "#Scale Continuous variables\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_m1 = StandardScaler()\n",
    "df_model3['avg_duration'] = sc_m1.fit_transform(df_model3[['avg_duration']])\n",
    "sc_m2 = StandardScaler()\n",
    "df_model3['TMIN'] = sc_m2.fit_transform(df_model3[['TMIN']])\n",
    "sc_m3 = StandardScaler()\n",
    "df_model3['TMAX'] = sc_m3.fit_transform(df_model3[['TMAX']])\n",
    "sc_m4 = StandardScaler()\n",
    "df_model3['PRCP'] = sc_m4.fit_transform(df_model3[['PRCP']])\n",
    "df_model3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "X = df_model3.iloc[:,1:]\n",
    "#Target variable\n",
    "y = df_model3.iloc[:,0]\n",
    "#Split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9954400797769715"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Trip_Duration_in_min</td> <th>  R-squared:         </th> <td>   0.996</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.996</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>    <th>  F-statistic:       </th> <td>7.241e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Sun, 10 Nov 2019</td>   <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>01:45:30</td>       <th>  Log-Likelihood:    </th> <td> -814.01</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>  4636</td>        <th>  AIC:               </th> <td>   1660.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>  4620</td>        <th>  BIC:               </th> <td>   1763.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>    15</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                <td>    0.0315</td> <td>    0.047</td> <td>    0.672</td> <td> 0.502</td> <td>   -0.060</td> <td>    0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Minutes</th>              <td>    0.9993</td> <td>    0.002</td> <td>  586.552</td> <td> 0.000</td> <td>    0.996</td> <td>    1.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>   -0.0148</td> <td>    0.018</td> <td>   -0.824</td> <td> 0.410</td> <td>   -0.050</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_speed</th>            <td>   -0.0052</td> <td>    0.003</td> <td>   -1.780</td> <td> 0.075</td> <td>   -0.011</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_duration</th>         <td>   -0.0023</td> <td>    0.004</td> <td>   -0.523</td> <td> 0.601</td> <td>   -0.011</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PRCP</th>                 <td>    0.0026</td> <td>    0.004</td> <td>    0.578</td> <td> 0.563</td> <td>   -0.006</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TMAX</th>                 <td>   -0.0047</td> <td>    0.008</td> <td>   -0.561</td> <td> 0.575</td> <td>   -0.021</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TMIN</th>                 <td>    0.0113</td> <td>    0.008</td> <td>    1.381</td> <td> 0.167</td> <td>   -0.005</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>   -0.0583</td> <td>    0.048</td> <td>   -1.214</td> <td> 0.225</td> <td>   -0.153</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>    0.0596</td> <td>    0.026</td> <td>    2.291</td> <td> 0.022</td> <td>    0.009</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>    0.0375</td> <td>    0.027</td> <td>    1.374</td> <td> 0.169</td> <td>   -0.016</td> <td>    0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>    0.0015</td> <td>    0.012</td> <td>    0.126</td> <td> 0.900</td> <td>   -0.022</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>    0.0102</td> <td>    0.009</td> <td>    1.116</td> <td> 0.265</td> <td>   -0.008</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>-6.215e-19</td> <td> 2.94e-17</td> <td>   -0.021</td> <td> 0.983</td> <td>-5.82e-17</td> <td>  5.7e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>-1.406e-18</td> <td> 2.97e-18</td> <td>   -0.473</td> <td> 0.636</td> <td>-7.24e-18</td> <td> 4.42e-18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.0016</td> <td>    0.016</td> <td>   -0.099</td> <td> 0.921</td> <td>   -0.034</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>    0.0063</td> <td>    0.018</td> <td>    0.355</td> <td> 0.722</td> <td>   -0.028</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>    0.0064</td> <td>    0.016</td> <td>    0.407</td> <td> 0.684</td> <td>   -0.025</td> <td>    0.037</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3787.166</td> <th>  Durbin-Watson:     </th> <td>   1.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 276.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.022</td>  <th>  Prob(JB):          </th> <td>8.98e-61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.804</td>  <th>  Cond. No.          </th> <td>2.53e+18</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 6.45e-32. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                             \n",
       "================================================================================\n",
       "Dep. Variable:     Trip_Duration_in_min   R-squared:                       0.996\n",
       "Model:                              OLS   Adj. R-squared:                  0.996\n",
       "Method:                   Least Squares   F-statistic:                 7.241e+04\n",
       "Date:                  Sun, 10 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                          01:45:30   Log-Likelihood:                -814.01\n",
       "No. Observations:                  4636   AIC:                             1660.\n",
       "Df Residuals:                      4620   BIC:                             1763.\n",
       "Df Model:                            15                                         \n",
       "Covariance Type:              nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                    0.0315      0.047      0.672      0.502      -0.060       0.123\n",
       "Minutes                  0.9993      0.002    586.552      0.000       0.996       1.003\n",
       "Distance                -0.0148      0.018     -0.824      0.410      -0.050       0.020\n",
       "avg_speed               -0.0052      0.003     -1.780      0.075      -0.011       0.001\n",
       "avg_duration            -0.0023      0.004     -0.523      0.601      -0.011       0.006\n",
       "PRCP                     0.0026      0.004      0.578      0.563      -0.006       0.011\n",
       "TMAX                    -0.0047      0.008     -0.561      0.575      -0.021       0.012\n",
       "TMIN                     0.0113      0.008      1.381      0.167      -0.005       0.027\n",
       "User Type_Subscriber    -0.0583      0.048     -1.214      0.225      -0.153       0.036\n",
       "Gender_1                 0.0596      0.026      2.291      0.022       0.009       0.111\n",
       "Gender_2                 0.0375      0.027      1.374      0.169      -0.016       0.091\n",
       "d_week_1                 0.0015      0.012      0.126      0.900      -0.022       0.025\n",
       "m_yr_1                   0.0102      0.009      1.116      0.265      -0.008       0.028\n",
       "m_yr_2               -6.215e-19   2.94e-17     -0.021      0.983   -5.82e-17     5.7e-17\n",
       "m_yr_3               -1.406e-18   2.97e-18     -0.473      0.636   -7.24e-18    4.42e-18\n",
       "ToD_Morning             -0.0016      0.016     -0.099      0.921      -0.034       0.031\n",
       "ToD_Afternoon            0.0063      0.018      0.355      0.722      -0.028       0.041\n",
       "ToD_Evening              0.0064      0.016      0.407      0.684      -0.025       0.037\n",
       "==============================================================================\n",
       "Omnibus:                     3787.166   Durbin-Watson:                   1.977\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              276.526\n",
       "Skew:                           0.022   Prob(JB):                     8.98e-61\n",
       "Kurtosis:                       1.804   Cond. No.                     2.53e+18\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 6.45e-32. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using Statsmodel because it has the summary function.\n",
    "import statsmodels.api as sm\n",
    "X_train = sm.add_constant(X_train)\n",
    "X_test = sm.add_constant(X_test)\n",
    "regressor_OLS = sm.OLS(y_train, X_train).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy: 0.996 +/- 0.000\n"
     ]
    }
   ],
   "source": [
    "#Ensure model accuracy with cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(regressor, X_train, y_train, cv=10)\n",
    "print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Removing unnecessary variables based on correlation: ToD, m_yr, PRCP, TMAX,TMIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataset to test the model on\n",
    "df_model4 = get_speed_distance(df_sample)\n",
    "df_model4 = drop_data(df_model4)\n",
    "df_model4 = get_date_info(df_model4)\n",
    "df_model4 = get_weather(df_model4)\n",
    "df_model4 = df_model4.drop(['Start Time','Trip','DATE','PRCP','TMAX','TMIN'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_model4 = pd.get_dummies(df_model4, drop_first = True)\n",
    "\n",
    "#Scale Continuous variables\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_m1 = StandardScaler()\n",
    "df_model4['avg_duration'] = sc_m1.fit_transform(df_model4[['avg_duration']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "df_model4=df_model4.replace([np.inf, -np.inf], np.nan)\n",
    "df_model4=df_model4.dropna()\n",
    "X = df_model4.iloc[:,1:]\n",
    "#Target variable\n",
    "y = df_model4.iloc[:,0]\n",
    "#Split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973379568487214"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assesing the viability of a different model such as RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9646421541294867"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ensemble algorithm, to see if there's a drastic difference, n_est. and min_samples_leaf determined after testing\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "regressor_rf = RandomForestRegressor(n_estimators = 80, random_state = 0, min_samples_leaf = 600)\n",
    "regressor_rf.fit(X_train,y_train)\n",
    "regressor_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 6.5: Predictive Model - Final Model\n",
    "- As I thought, weather is not a major contributor to the model. Thus, I'll be taking out weather. Additionally, I'll be taking out date attributes as mentioned earlier. \n",
    "    1. The ensemble algorithm, Random Forest takes much longer to run (even with a low n_estimators). If the run\n",
    "    time is 5 minutes on ~1.5 million rows, I don't want to take the risk on ~15 million rows of data. \n",
    "    2. I could've used XGboost and other fancy algorithms, however, for a dataset of this size, it would take too\n",
    "    long to run and the gains wouldn't be worth it if there are any. \n",
    "    3. Final Model:\n",
    "        - Linear Regression:\n",
    "            - Predictors: Distance, Gender, Average Duration based on Trip and Gender, User Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prep_data(df):\n",
    "    df = get_speed_distance(df)\n",
    "    df = drop_data(df)\n",
    "    df = get_date_info(df)\n",
    "    df = df.drop(['Start Time','Trip','m_yr',], axis =1)\n",
    "    return df\n",
    "\n",
    "df_final = prep_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_final = pd.get_dummies(df_final, drop_first = True)\n",
    "\n",
    "#Scale Continuous variables\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_m1 = StandardScaler()\n",
    "df_final['avg_duration'] = sc_m1.fit_transform(df_final[['avg_duration']])\n",
    "sc_m1 = StandardScaler()\n",
    "df_final['avg_speed'] = sc_m1.fit_transform(df_final[['avg_speed']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "df_final=df_final.dropna()\n",
    "X = df_final.iloc[:,1:]\n",
    "#Target variable\n",
    "y = df_final.iloc[:,0]\n",
    "#Split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "#X_test=X_test.dropna()\n",
    "#X_test\n",
    "#X_train=X_train.dropna()\n",
    "#X_train \n",
    "#y_train=y_train.dropna()\n",
    "#y_train\n",
    "#y_test=y_test.dropna()\n",
    "#y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9971457785527679"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My Work is Located at this Location (: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Distance</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>avg_duration</th>\n",
       "      <th>User Type_Subscriber</th>\n",
       "      <th>Gender_1</th>\n",
       "      <th>Gender_2</th>\n",
       "      <th>d_week_1</th>\n",
       "      <th>ToD_Morning</th>\n",
       "      <th>ToD_Afternoon</th>\n",
       "      <th>ToD_Evening</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>0.221838</td>\n",
       "      <td>-0.379213</td>\n",
       "      <td>0.771455</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>0.092689</td>\n",
       "      <td>1.712533</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>-0.307933</td>\n",
       "      <td>0.735680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>10.783333</td>\n",
       "      <td>1.295671</td>\n",
       "      <td>0.441783</td>\n",
       "      <td>-1.206101</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>0.467110</td>\n",
       "      <td>0.057576</td>\n",
       "      <td>-0.054349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Trip_Duration_in_min    Minutes  Distance  avg_speed  avg_duration  \\\n",
       "0                      6   6.266667  0.221838  -0.379213      0.771455   \n",
       "5                      6   6.083333  0.482094   0.092689      1.712533   \n",
       "6                      2   2.316667  0.327309  -0.307933      0.735680   \n",
       "8                     11  10.783333  1.295671   0.441783     -1.206101   \n",
       "10                     6   5.866667  0.467110   0.057576     -0.054349   \n",
       "\n",
       "    User Type_Subscriber  Gender_1  Gender_2  d_week_1  ToD_Morning  \\\n",
       "0                      1         1         0         1            0   \n",
       "5                      0         0         0         1            0   \n",
       "6                      1         1         0         1            0   \n",
       "8                      1         1         0         1            0   \n",
       "10                     0         0         0         1            0   \n",
       "\n",
       "    ToD_Afternoon  ToD_Evening  \n",
       "0               0            0  \n",
       "5               0            0  \n",
       "6               0            0  \n",
       "8               0            0  \n",
       "10              0            0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "X = df_final.loc[:,df_final.columns != 'Gender_1']\n",
    "#Target variable\n",
    "y = df_final.iloc[:,6]\n",
    "#Split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "df_final=df_final.dropna()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#X_test=X_test.dropna()\n",
    "#X_test\n",
    "#X_train=X_train.dropna()\n",
    "#X_train \n",
    "#y_train=y_train.dropna()\n",
    "#y_train\n",
    "#y_test=y_test.dropna()\n",
    "#y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.915507047330862"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Predicted_Rounded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>570104</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978777</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>266187</td>\n",
       "      <td>1</td>\n",
       "      <td>0.981780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>225316</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002709</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>388815</td>\n",
       "      <td>1</td>\n",
       "      <td>0.975131</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>353451</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979788</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual  Predicted  Predicted_Rounded\n",
       "570104       1   0.978777                  1\n",
       "266187       1   0.981780                  1\n",
       "225316       0   0.002709                  0\n",
       "388815       1   0.975131                  1\n",
       "353451       1   0.979788                  1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "y_round = y_pred.round(decimals=0).astype(int) \n",
    "df_preddd = pandas.DataFrame({\"Actual\": y_test, \"Predicted\": y_pred, \"Predicted_Rounded\": y_round}) \n",
    "df_preddd.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.915507047330862"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THIS IS WOMEN POPULARITY SECTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trip Duration          Start Time           Stop Time  Start Station ID  \\\n",
       "0            376 2015-10-01 00:16:26 2015-10-01 00:22:42              3212   \n",
       "1            739 2015-10-01 00:27:12 2015-10-01 00:39:32              3207   \n",
       "2           2714 2015-10-01 00:32:46 2015-10-01 01:18:01              3193   \n",
       "3            275 2015-10-01 00:34:31 2015-10-01 00:39:06              3199   \n",
       "4            561 2015-10-01 00:40:12 2015-10-01 00:49:33              3183   \n",
       "\n",
       "  Start Station Name  Start Station Latitude  Start Station Longitude  \\\n",
       "0    Christ Hospital               40.734786               -74.050444   \n",
       "1        Oakland Ave               40.737604               -74.052478   \n",
       "2       Lincoln Park               40.724605               -74.078406   \n",
       "3       Newport Pkwy               40.728745               -74.032108   \n",
       "4     Exchange Place               40.716247               -74.033459   \n",
       "\n",
       "   End Station ID    End Station Name  End Station Latitude  \\\n",
       "0            3207         Oakland Ave             40.737604   \n",
       "1            3212     Christ Hospital             40.734786   \n",
       "2            3193        Lincoln Park             40.724605   \n",
       "3            3187           Warren St             40.721124   \n",
       "4            3192  Liberty Light Rail             40.711242   \n",
       "\n",
       "   End Station Longitude  Bike ID   User Type  Birth Year Gender  \\\n",
       "0             -74.052478    24470  Subscriber      1960.0      1   \n",
       "1             -74.050444    24481  Subscriber      1960.0      1   \n",
       "2             -74.078406    24628  Subscriber      1983.0      1   \n",
       "3             -74.038051    24613  Subscriber      1975.0      1   \n",
       "4             -74.055701    24668    Customer      1984.0      0   \n",
       "\n",
       "   Trip_Duration_in_min  \n",
       "0                     6  \n",
       "1                    12  \n",
       "2                    45  \n",
       "3                     5  \n",
       "4                     9  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_women= pd.read_csv(\"Files/NYC-BikeShare-2015-2017-combined.csv\")\n",
    "#Ensure data is formatted correctly to avoid errors in the visuals\n",
    "df_women['Start Time'] = to_datetime(df_women['Start Time'])\n",
    "df_women['Stop Time'] = to_datetime(df_women['Stop Time'])\n",
    "df_women['Start Station Name'] = df_women['Start Station Name'].astype('category')\n",
    "df_women['End Station Name'] = df_women['End Station Name'].astype('category')\n",
    "df_women['User Type'] = df_women['User Type'].astype('category')\n",
    "df_women['Gender'] = df_women['Gender'].astype('category')\n",
    "round(df.describe(),2)\n",
    "df_women = df_women.drop(['Unnamed: 0'], axis = 1)\n",
    "df_women.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Start Coordinates</th>\n",
       "      <th>End Coordinates</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>(40.734785818, -74.050443636)</td>\n",
       "      <td>(40.7376037, -74.05247829999998)</td>\n",
       "      <td>0.221838</td>\n",
       "      <td>58</td>\n",
       "      <td>28.25</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>...</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>(40.7287448, -74.03210820000002)</td>\n",
       "      <td>(40.72112360000001, -74.03805095)</td>\n",
       "      <td>0.611454</td>\n",
       "      <td>43</td>\n",
       "      <td>7.50</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7112423, -74.0557013)</td>\n",
       "      <td>1.217823</td>\n",
       "      <td>34</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>365</td>\n",
       "      <td>2015-10-01 00:41:46</td>\n",
       "      <td>2015-10-01 00:47:51</td>\n",
       "      <td>3198</td>\n",
       "      <td>Heights Elevator</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-74.040443</td>\n",
       "      <td>3215</td>\n",
       "      <td>Central Ave</td>\n",
       "      <td>40.746730</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>(40.748715945380226, -74.04044330120088)</td>\n",
       "      <td>(40.7467299, -74.0492509)</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>34</td>\n",
       "      <td>12.62</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>139</td>\n",
       "      <td>2015-10-01 00:43:44</td>\n",
       "      <td>2015-10-01 00:46:03</td>\n",
       "      <td>3206</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>40.731169</td>\n",
       "      <td>-74.057574</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>...</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>(40.7311689, -74.0575736)</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>30</td>\n",
       "      <td>7.08</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717653</td>\n",
       "      <td>239</td>\n",
       "      <td>2017-03-31 21:26:40</td>\n",
       "      <td>2017-03-31 21:30:39</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>-74.063784</td>\n",
       "      <td>3201</td>\n",
       "      <td>Dey St</td>\n",
       "      <td>40.737711</td>\n",
       "      <td>...</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>(40.737711, -74.066921)</td>\n",
       "      <td>0.508248</td>\n",
       "      <td>48</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717654</td>\n",
       "      <td>192</td>\n",
       "      <td>2017-03-31 21:46:57</td>\n",
       "      <td>2017-03-31 21:50:09</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3267</td>\n",
       "      <td>Morris Canal</td>\n",
       "      <td>40.712419</td>\n",
       "      <td>...</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7124188237569, -74.03852552175522)</td>\n",
       "      <td>0.374889</td>\n",
       "      <td>42</td>\n",
       "      <td>8.54</td>\n",
       "      <td>7.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717655</td>\n",
       "      <td>186</td>\n",
       "      <td>2017-03-31 22:03:56</td>\n",
       "      <td>2017-03-31 22:07:02</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>...</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.71277420000001, -74.03648570000001)</td>\n",
       "      <td>0.287536</td>\n",
       "      <td>62</td>\n",
       "      <td>10.78</td>\n",
       "      <td>5.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717656</td>\n",
       "      <td>618</td>\n",
       "      <td>2017-03-31 23:20:14</td>\n",
       "      <td>2017-03-31 23:30:32</td>\n",
       "      <td>3272</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "      <td>40.723332</td>\n",
       "      <td>-74.045953</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>(40.723331586464354, -74.04595255851744)</td>\n",
       "      <td>(40.71346382669195, -74.06285852193831)</td>\n",
       "      <td>1.118670</td>\n",
       "      <td>34</td>\n",
       "      <td>9.21</td>\n",
       "      <td>6.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717657</td>\n",
       "      <td>649</td>\n",
       "      <td>2017-03-31 23:25:34</td>\n",
       "      <td>2017-03-31 23:36:23</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3190</td>\n",
       "      <td>Garfield Ave Station</td>\n",
       "      <td>40.710467</td>\n",
       "      <td>...</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>10.816667</td>\n",
       "      <td>(40.7177325, -74.04384499999998)</td>\n",
       "      <td>(40.7104670233797, -74.07003879547119)</td>\n",
       "      <td>1.463817</td>\n",
       "      <td>30</td>\n",
       "      <td>7.39</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654640 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trip Duration          Start Time           Stop Time  \\\n",
       "0                 376 2015-10-01 00:16:26 2015-10-01 00:22:42   \n",
       "3                 275 2015-10-01 00:34:31 2015-10-01 00:39:06   \n",
       "4                 561 2015-10-01 00:40:12 2015-10-01 00:49:33   \n",
       "5                 365 2015-10-01 00:41:46 2015-10-01 00:47:51   \n",
       "6                 139 2015-10-01 00:43:44 2015-10-01 00:46:03   \n",
       "...               ...                 ...                 ...   \n",
       "717653            239 2017-03-31 21:26:40 2017-03-31 21:30:39   \n",
       "717654            192 2017-03-31 21:46:57 2017-03-31 21:50:09   \n",
       "717655            186 2017-03-31 22:03:56 2017-03-31 22:07:02   \n",
       "717656            618 2017-03-31 23:20:14 2017-03-31 23:30:32   \n",
       "717657            649 2017-03-31 23:25:34 2017-03-31 23:36:23   \n",
       "\n",
       "        Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0                   3212    Christ Hospital               40.734786   \n",
       "3                   3199       Newport Pkwy               40.728745   \n",
       "4                   3183     Exchange Place               40.716247   \n",
       "5                   3198   Heights Elevator               40.748716   \n",
       "6                   3206            Hilltop               40.731169   \n",
       "...                  ...                ...                     ...   \n",
       "717653              3195            Sip Ave               40.730743   \n",
       "717654              3183     Exchange Place               40.716247   \n",
       "717655              3183     Exchange Place               40.716247   \n",
       "717656              3272       Jersey & 3rd               40.723332   \n",
       "717657              3185          City Hall               40.717732   \n",
       "\n",
       "        Start Station Longitude  End Station ID      End Station Name  \\\n",
       "0                    -74.050444            3207           Oakland Ave   \n",
       "3                    -74.032108            3187             Warren St   \n",
       "4                    -74.033459            3192    Liberty Light Rail   \n",
       "5                    -74.040443            3215           Central Ave   \n",
       "6                    -74.057574            3195               Sip Ave   \n",
       "...                         ...             ...                   ...   \n",
       "717653               -74.063784            3201                Dey St   \n",
       "717654               -74.033459            3267          Morris Canal   \n",
       "717655               -74.033459            3214      Essex Light Rail   \n",
       "717656               -74.045953            3268        Lafayette Park   \n",
       "717657               -74.043845            3190  Garfield Ave Station   \n",
       "\n",
       "        End Station Latitude  ...  Birth Year  Gender Trip_Duration_in_min  \\\n",
       "0                  40.737604  ...      1960.0       1                    6   \n",
       "3                  40.721124  ...      1975.0       1                    5   \n",
       "4                  40.711242  ...      1984.0       0                    9   \n",
       "5                  40.746730  ...      1984.0       0                    6   \n",
       "6                  40.730743  ...      1988.0       1                    2   \n",
       "...                      ...  ...         ...     ...                  ...   \n",
       "717653             40.737711  ...      1970.0       1                    4   \n",
       "717654             40.712419  ...      1976.0       1                    3   \n",
       "717655             40.712774  ...      1956.0       1                    3   \n",
       "717656             40.713464  ...      1984.0       1                   10   \n",
       "717657             40.710467  ...      1988.0       1                   11   \n",
       "\n",
       "          Minutes                         Start Coordinates  \\\n",
       "0        6.266667             (40.734785818, -74.050443636)   \n",
       "3        4.583333          (40.7287448, -74.03210820000002)   \n",
       "4        9.350000                 (40.7162469, -74.0334588)   \n",
       "5        6.083333  (40.748715945380226, -74.04044330120088)   \n",
       "6        2.316667                 (40.7311689, -74.0575736)   \n",
       "...           ...                                       ...   \n",
       "717653   3.983333  (40.730742625306576, -74.06378388404845)   \n",
       "717654   3.200000                 (40.7162469, -74.0334588)   \n",
       "717655   3.100000                 (40.7162469, -74.0334588)   \n",
       "717656  10.300000  (40.723331586464354, -74.04595255851744)   \n",
       "717657  10.816667          (40.7177325, -74.04384499999998)   \n",
       "\n",
       "                                 End Coordinates  Distance Age min_mile  \\\n",
       "0               (40.7376037, -74.05247829999998)  0.221838  58    28.25   \n",
       "3              (40.72112360000001, -74.03805095)  0.611454  43     7.50   \n",
       "4                      (40.7112423, -74.0557013)  1.217823  34     7.68   \n",
       "5                      (40.7467299, -74.0492509)  0.482094  34    12.62   \n",
       "6       (40.730742625306576, -74.06378388404845)  0.327309  30     7.08   \n",
       "...                                          ...       ...  ..      ...   \n",
       "717653                   (40.737711, -74.066921)  0.508248  48     7.84   \n",
       "717654    (40.7124188237569, -74.03852552175522)  0.374889  42     8.54   \n",
       "717655   (40.71277420000001, -74.03648570000001)  0.287536  62    10.78   \n",
       "717656   (40.71346382669195, -74.06285852193831)  1.118670  34     9.21   \n",
       "717657    (40.7104670233797, -74.07003879547119)  1.463817  30     7.39   \n",
       "\n",
       "        mile_hour  \n",
       "0            2.12  \n",
       "3            8.00  \n",
       "4            7.81  \n",
       "5            4.75  \n",
       "6            8.48  \n",
       "...           ...  \n",
       "717653       7.66  \n",
       "717654       7.03  \n",
       "717655       5.57  \n",
       "717656       6.52  \n",
       "717657       8.12  \n",
       "\n",
       "[654640 rows x 23 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_women2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>735497</td>\n",
       "      <td>239</td>\n",
       "      <td>2017-03-31 21:26:40</td>\n",
       "      <td>2017-03-31 21:30:39</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>-74.063784</td>\n",
       "      <td>3201</td>\n",
       "      <td>Dey St</td>\n",
       "      <td>40.737711</td>\n",
       "      <td>-74.066921</td>\n",
       "      <td>29276</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735498</td>\n",
       "      <td>192</td>\n",
       "      <td>2017-03-31 21:46:57</td>\n",
       "      <td>2017-03-31 21:50:09</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3267</td>\n",
       "      <td>Morris Canal</td>\n",
       "      <td>40.712419</td>\n",
       "      <td>-74.038526</td>\n",
       "      <td>26177</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735499</td>\n",
       "      <td>186</td>\n",
       "      <td>2017-03-31 22:03:56</td>\n",
       "      <td>2017-03-31 22:07:02</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>-74.036486</td>\n",
       "      <td>26240</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735500</td>\n",
       "      <td>618</td>\n",
       "      <td>2017-03-31 23:20:14</td>\n",
       "      <td>2017-03-31 23:30:32</td>\n",
       "      <td>3272</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "      <td>40.723332</td>\n",
       "      <td>-74.045953</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>-74.062859</td>\n",
       "      <td>24398</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735501</td>\n",
       "      <td>649</td>\n",
       "      <td>2017-03-31 23:25:34</td>\n",
       "      <td>2017-03-31 23:36:23</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3190</td>\n",
       "      <td>Garfield Ave Station</td>\n",
       "      <td>40.710467</td>\n",
       "      <td>-74.070039</td>\n",
       "      <td>26200</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>735502 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trip Duration          Start Time           Stop Time  \\\n",
       "0                 376 2015-10-01 00:16:26 2015-10-01 00:22:42   \n",
       "1                 739 2015-10-01 00:27:12 2015-10-01 00:39:32   \n",
       "2                2714 2015-10-01 00:32:46 2015-10-01 01:18:01   \n",
       "3                 275 2015-10-01 00:34:31 2015-10-01 00:39:06   \n",
       "4                 561 2015-10-01 00:40:12 2015-10-01 00:49:33   \n",
       "...               ...                 ...                 ...   \n",
       "735497            239 2017-03-31 21:26:40 2017-03-31 21:30:39   \n",
       "735498            192 2017-03-31 21:46:57 2017-03-31 21:50:09   \n",
       "735499            186 2017-03-31 22:03:56 2017-03-31 22:07:02   \n",
       "735500            618 2017-03-31 23:20:14 2017-03-31 23:30:32   \n",
       "735501            649 2017-03-31 23:25:34 2017-03-31 23:36:23   \n",
       "\n",
       "        Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0                   3212    Christ Hospital               40.734786   \n",
       "1                   3207        Oakland Ave               40.737604   \n",
       "2                   3193       Lincoln Park               40.724605   \n",
       "3                   3199       Newport Pkwy               40.728745   \n",
       "4                   3183     Exchange Place               40.716247   \n",
       "...                  ...                ...                     ...   \n",
       "735497              3195            Sip Ave               40.730743   \n",
       "735498              3183     Exchange Place               40.716247   \n",
       "735499              3183     Exchange Place               40.716247   \n",
       "735500              3272       Jersey & 3rd               40.723332   \n",
       "735501              3185          City Hall               40.717732   \n",
       "\n",
       "        Start Station Longitude  End Station ID      End Station Name  \\\n",
       "0                    -74.050444            3207           Oakland Ave   \n",
       "1                    -74.052478            3212       Christ Hospital   \n",
       "2                    -74.078406            3193          Lincoln Park   \n",
       "3                    -74.032108            3187             Warren St   \n",
       "4                    -74.033459            3192    Liberty Light Rail   \n",
       "...                         ...             ...                   ...   \n",
       "735497               -74.063784            3201                Dey St   \n",
       "735498               -74.033459            3267          Morris Canal   \n",
       "735499               -74.033459            3214      Essex Light Rail   \n",
       "735500               -74.045953            3268        Lafayette Park   \n",
       "735501               -74.043845            3190  Garfield Ave Station   \n",
       "\n",
       "        End Station Latitude  End Station Longitude  Bike ID   User Type  \\\n",
       "0                  40.737604             -74.052478    24470  Subscriber   \n",
       "1                  40.734786             -74.050444    24481  Subscriber   \n",
       "2                  40.724605             -74.078406    24628  Subscriber   \n",
       "3                  40.721124             -74.038051    24613  Subscriber   \n",
       "4                  40.711242             -74.055701    24668    Customer   \n",
       "...                      ...                    ...      ...         ...   \n",
       "735497             40.737711             -74.066921    29276  Subscriber   \n",
       "735498             40.712419             -74.038526    26177  Subscriber   \n",
       "735499             40.712774             -74.036486    26240  Subscriber   \n",
       "735500             40.713464             -74.062859    24398  Subscriber   \n",
       "735501             40.710467             -74.070039    26200  Subscriber   \n",
       "\n",
       "        Birth Year Gender  Trip_Duration_in_min d_week m_yr    ToD  \n",
       "0           1960.0      1                     6      1    3  Night  \n",
       "1           1960.0      1                    12      1    3  Night  \n",
       "2           1983.0      1                    45      1    3  Night  \n",
       "3           1975.0      1                     5      1    3  Night  \n",
       "4           1984.0      0                     9      1    3  Night  \n",
       "...            ...    ...                   ...    ...  ...    ...  \n",
       "735497      1970.0      1                     4      1    1  Night  \n",
       "735498      1976.0      1                     3      1    1  Night  \n",
       "735499      1956.0      1                     3      1    1  Night  \n",
       "735500      1984.0      1                    10      1    1  Night  \n",
       "735501      1988.0      1                    11      1    1  Night  \n",
       "\n",
       "[735502 rows x 19 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_women = get_date_info(df_women)\n",
    "df_women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
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       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
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       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
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       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
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       "      <td>3187</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
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       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735497</td>\n",
       "      <td>239</td>\n",
       "      <td>2017-03-31 21:26:40</td>\n",
       "      <td>2017-03-31 21:30:39</td>\n",
       "      <td>3195</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>-74.063784</td>\n",
       "      <td>3201</td>\n",
       "      <td>40.737711</td>\n",
       "      <td>-74.066921</td>\n",
       "      <td>29276</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735498</td>\n",
       "      <td>192</td>\n",
       "      <td>2017-03-31 21:46:57</td>\n",
       "      <td>2017-03-31 21:50:09</td>\n",
       "      <td>3183</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3267</td>\n",
       "      <td>40.712419</td>\n",
       "      <td>-74.038526</td>\n",
       "      <td>26177</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735499</td>\n",
       "      <td>186</td>\n",
       "      <td>2017-03-31 22:03:56</td>\n",
       "      <td>2017-03-31 22:07:02</td>\n",
       "      <td>3183</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>-74.036486</td>\n",
       "      <td>26240</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735500</td>\n",
       "      <td>618</td>\n",
       "      <td>2017-03-31 23:20:14</td>\n",
       "      <td>2017-03-31 23:30:32</td>\n",
       "      <td>3272</td>\n",
       "      <td>40.723332</td>\n",
       "      <td>-74.045953</td>\n",
       "      <td>3268</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>-74.062859</td>\n",
       "      <td>24398</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>735501</td>\n",
       "      <td>649</td>\n",
       "      <td>2017-03-31 23:25:34</td>\n",
       "      <td>2017-03-31 23:36:23</td>\n",
       "      <td>3185</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3190</td>\n",
       "      <td>40.710467</td>\n",
       "      <td>-74.070039</td>\n",
       "      <td>26200</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>735502 rows × 185 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trip Duration          Start Time           Stop Time  \\\n",
       "0                 376 2015-10-01 00:16:26 2015-10-01 00:22:42   \n",
       "1                 739 2015-10-01 00:27:12 2015-10-01 00:39:32   \n",
       "2                2714 2015-10-01 00:32:46 2015-10-01 01:18:01   \n",
       "3                 275 2015-10-01 00:34:31 2015-10-01 00:39:06   \n",
       "4                 561 2015-10-01 00:40:12 2015-10-01 00:49:33   \n",
       "...               ...                 ...                 ...   \n",
       "735497            239 2017-03-31 21:26:40 2017-03-31 21:30:39   \n",
       "735498            192 2017-03-31 21:46:57 2017-03-31 21:50:09   \n",
       "735499            186 2017-03-31 22:03:56 2017-03-31 22:07:02   \n",
       "735500            618 2017-03-31 23:20:14 2017-03-31 23:30:32   \n",
       "735501            649 2017-03-31 23:25:34 2017-03-31 23:36:23   \n",
       "\n",
       "        Start Station ID  Start Station Latitude  Start Station Longitude  \\\n",
       "0                   3212               40.734786               -74.050444   \n",
       "1                   3207               40.737604               -74.052478   \n",
       "2                   3193               40.724605               -74.078406   \n",
       "3                   3199               40.728745               -74.032108   \n",
       "4                   3183               40.716247               -74.033459   \n",
       "...                  ...                     ...                      ...   \n",
       "735497              3195               40.730743               -74.063784   \n",
       "735498              3183               40.716247               -74.033459   \n",
       "735499              3183               40.716247               -74.033459   \n",
       "735500              3272               40.723332               -74.045953   \n",
       "735501              3185               40.717732               -74.043845   \n",
       "\n",
       "        End Station ID  End Station Latitude  End Station Longitude  Bike ID  \\\n",
       "0                 3207             40.737604             -74.052478    24470   \n",
       "1                 3212             40.734786             -74.050444    24481   \n",
       "2                 3193             40.724605             -74.078406    24628   \n",
       "3                 3187             40.721124             -74.038051    24613   \n",
       "4                 3192             40.711242             -74.055701    24668   \n",
       "...                ...                   ...                    ...      ...   \n",
       "735497            3201             40.737711             -74.066921    29276   \n",
       "735498            3267             40.712419             -74.038526    26177   \n",
       "735499            3214             40.712774             -74.036486    26240   \n",
       "735500            3268             40.713464             -74.062859    24398   \n",
       "735501            3190             40.710467             -74.070039    26200   \n",
       "\n",
       "        ...  User Type_Subscriber  Gender_1  Gender_2  d_week_1  m_yr_1  \\\n",
       "0       ...                     1         1         0         1       0   \n",
       "1       ...                     1         1         0         1       0   \n",
       "2       ...                     1         1         0         1       0   \n",
       "3       ...                     1         1         0         1       0   \n",
       "4       ...                     0         0         0         1       0   \n",
       "...     ...                   ...       ...       ...       ...     ...   \n",
       "735497  ...                     1         1         0         1       1   \n",
       "735498  ...                     1         1         0         1       1   \n",
       "735499  ...                     1         1         0         1       1   \n",
       "735500  ...                     1         1         0         1       1   \n",
       "735501  ...                     1         1         0         1       1   \n",
       "\n",
       "        m_yr_2  m_yr_3  ToD_Morning  ToD_Afternoon  ToD_Evening  \n",
       "0            0       1            0              0            0  \n",
       "1            0       1            0              0            0  \n",
       "2            0       1            0              0            0  \n",
       "3            0       1            0              0            0  \n",
       "4            0       1            0              0            0  \n",
       "...        ...     ...          ...            ...          ...  \n",
       "735497       0       0            0              0            0  \n",
       "735498       0       0            0              0            0  \n",
       "735499       0       0            0              0            0  \n",
       "735500       0       0            0              0            0  \n",
       "735501       0       0            0              0            0  \n",
       "\n",
       "[735502 rows x 185 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dummify categorical data and avoid dummy variable trap\n",
    "df_women = pd.get_dummies(df_women, drop_first = True)\n",
    "df_women\n",
    "#df_model1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\n",
    "X = df_women.loc[:,['ToD_Morning', 'ToD_Afternoon', 'ToD_Evening', 'Start Station ID']] \n",
    "#Target variable\n",
    "y = df_women.loc[:,\"Gender_1\"]\n",
    "#Split\n",
    "#from sklearn.cross_validation import train_test_split\n",
    "df_women=df_women.dropna()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "#X_test=X_test.dropna()\n",
    "#X_test\n",
    "#X_train=X_train.dropna()\n",
    "#X_train \n",
    "#y_train=y_train.dropna()\n",
    "#y_train\n",
    "#y_test=y_test.dropna()\n",
    "#y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Start Station ID\n",
      "0                   3212\n",
      "1                   3207\n",
      "2                   3193\n",
      "3                   3199\n",
      "4                   3183\n",
      "...                  ...\n",
      "735497              3195\n",
      "735498              3183\n",
      "735499              3183\n",
      "735500              3272\n",
      "735501              3185\n",
      "\n",
      "[735502 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_women.loc[:,['Start Station ID']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005487625233618232"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>186315</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>611388</td>\n",
       "      <td>1</td>\n",
       "      <td>0.720856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>634502</td>\n",
       "      <td>1</td>\n",
       "      <td>0.712678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>728901</td>\n",
       "      <td>0</td>\n",
       "      <td>0.665825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>285966</td>\n",
       "      <td>0</td>\n",
       "      <td>0.746617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Actual  Predicted\n",
       "186315       0   0.666568\n",
       "611388       1   0.720856\n",
       "634502       1   0.712678\n",
       "728901       0   0.665825\n",
       "285966       0   0.746617"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "#y_round = y_pred.round(decimals=0).astype(int) \n",
    "df_preddd2 = pandas.DataFrame({\"Actual\": y_test, \"Predicted\": y_pred}) \n",
    "df_preddd2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005487625233618232"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit Linear Regression and check accuracy using sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Start Coordinates</th>\n",
       "      <th>End Coordinates</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>(40.734785818, -74.050443636)</td>\n",
       "      <td>(40.7376037, -74.05247829999998)</td>\n",
       "      <td>0.221838</td>\n",
       "      <td>58</td>\n",
       "      <td>28.25</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>...</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>(40.7287448, -74.03210820000002)</td>\n",
       "      <td>(40.72112360000001, -74.03805095)</td>\n",
       "      <td>0.611454</td>\n",
       "      <td>43</td>\n",
       "      <td>7.50</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7112423, -74.0557013)</td>\n",
       "      <td>1.217823</td>\n",
       "      <td>34</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>365</td>\n",
       "      <td>2015-10-01 00:41:46</td>\n",
       "      <td>2015-10-01 00:47:51</td>\n",
       "      <td>3198</td>\n",
       "      <td>Heights Elevator</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-74.040443</td>\n",
       "      <td>3215</td>\n",
       "      <td>Central Ave</td>\n",
       "      <td>40.746730</td>\n",
       "      <td>...</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>(40.748715945380226, -74.04044330120088)</td>\n",
       "      <td>(40.7467299, -74.0492509)</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>34</td>\n",
       "      <td>12.62</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>139</td>\n",
       "      <td>2015-10-01 00:43:44</td>\n",
       "      <td>2015-10-01 00:46:03</td>\n",
       "      <td>3206</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>40.731169</td>\n",
       "      <td>-74.057574</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>...</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>(40.7311689, -74.0575736)</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>30</td>\n",
       "      <td>7.08</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trip Duration          Start Time           Stop Time  Start Station ID  \\\n",
       "0            376 2015-10-01 00:16:26 2015-10-01 00:22:42              3212   \n",
       "3            275 2015-10-01 00:34:31 2015-10-01 00:39:06              3199   \n",
       "4            561 2015-10-01 00:40:12 2015-10-01 00:49:33              3183   \n",
       "5            365 2015-10-01 00:41:46 2015-10-01 00:47:51              3198   \n",
       "6            139 2015-10-01 00:43:44 2015-10-01 00:46:03              3206   \n",
       "\n",
       "  Start Station Name  Start Station Latitude  Start Station Longitude  \\\n",
       "0    Christ Hospital               40.734786               -74.050444   \n",
       "3       Newport Pkwy               40.728745               -74.032108   \n",
       "4     Exchange Place               40.716247               -74.033459   \n",
       "5   Heights Elevator               40.748716               -74.040443   \n",
       "6            Hilltop               40.731169               -74.057574   \n",
       "\n",
       "   End Station ID    End Station Name  End Station Latitude  ...  Birth Year  \\\n",
       "0            3207         Oakland Ave             40.737604  ...      1960.0   \n",
       "3            3187           Warren St             40.721124  ...      1975.0   \n",
       "4            3192  Liberty Light Rail             40.711242  ...      1984.0   \n",
       "5            3215         Central Ave             40.746730  ...      1984.0   \n",
       "6            3195             Sip Ave             40.730743  ...      1988.0   \n",
       "\n",
       "   Gender Trip_Duration_in_min   Minutes  \\\n",
       "0       1                    6  6.266667   \n",
       "3       1                    5  4.583333   \n",
       "4       0                    9  9.350000   \n",
       "5       0                    6  6.083333   \n",
       "6       1                    2  2.316667   \n",
       "\n",
       "                          Start Coordinates  \\\n",
       "0             (40.734785818, -74.050443636)   \n",
       "3          (40.7287448, -74.03210820000002)   \n",
       "4                 (40.7162469, -74.0334588)   \n",
       "5  (40.748715945380226, -74.04044330120088)   \n",
       "6                 (40.7311689, -74.0575736)   \n",
       "\n",
       "                            End Coordinates  Distance Age min_mile  mile_hour  \n",
       "0          (40.7376037, -74.05247829999998)  0.221838  58    28.25       2.12  \n",
       "3         (40.72112360000001, -74.03805095)  0.611454  43     7.50       8.00  \n",
       "4                 (40.7112423, -74.0557013)  1.217823  34     7.68       7.81  \n",
       "5                 (40.7467299, -74.0492509)  0.482094  34    12.62       4.75  \n",
       "6  (40.730742625306576, -74.06378388404845)  0.327309  30     7.08       8.48  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = df2.copy()   \n",
    "# pd.read_csv(\"Files/NYC-BikeShare-2015-2017-combined.csv\")\n",
    "#Ensure data is formatted correctly to avoid errors in the visuals\n",
    "pop_df['Start Station Name'] = pop_df['Start Station Name'].astype(str)\n",
    "\n",
    "''' \n",
    "pop_df['Start Time'] = to_datetime(df_women['Start Time'])\n",
    "pop_df['Stop Time'] = to_datetime(df_women['Stop Time'])\n",
    "pop_df['Start Station Name'] = pop_df['Start Station Name'].astype('category')\n",
    "pop_df['End Station Name'] = pop_df['End Station Name'].astype('category')\n",
    "pop_df['User Type'] = pop_df['User Type'].astype('category')\n",
    "pop_df['Gender'] = pop_df['Gender'].astype('category')\n",
    "pop_df = pop_df.drop(['Unnamed: 0'], axis = 1)\n",
    "round(pop_df.describe(),2)\n",
    "pop_df = pop_df.drop(pop_df.index[(pop_df['Trip Duration'] < 90) & \n",
    "                          (pop_df['Start Station Latitude'] == pop_df['End Station Latitude'])])\n",
    "pop_df = pop_df.drop(pop_df.index[(pop_df['Trip Duration'] > 7200)])\n",
    "pop_df = pop_df.drop(pop_df.index[(pop_df['Birth Year'] < 1956)])\n",
    "pop_df['Birth Year'] = pop_df.groupby(['Gender','Start Station ID'])['Birth Year'].transform(lambda x: x.fillna(x.median()))\n",
    "pop_df = pop_df.dropna(subset=['Birth Year'])\n",
    "pop_df['Age'] = 2018 - pop_df['Birth Year']\n",
    "pop_df['Age'] = pop_df['Age'].astype(int)\n",
    "pop_df = pop_df.drop(pop_df.index[(pop_df['Distance'] == 0)])\n",
    "'''\n",
    "pop_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gets date without categorizing \n",
    "def get_date_info2(df):   \n",
    "    df['d_week'] = df['Start Time'].dt.dayofweek\n",
    "    df['m_yr'] = df['Start Time'].dt.month\n",
    "    df['ToD'] = df['Start Time'].dt.hour\n",
    "    \n",
    "    df['m_yr'] = df['m_yr'].astype('category')\n",
    "    df['d_week'] = df['d_week'].astype('category')\n",
    "\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Start Coordinates</th>\n",
       "      <th>End Coordinates</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "      <th>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>...</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>(40.734785818, -74.050443636)</td>\n",
       "      <td>(40.7376037, -74.05247829999998)</td>\n",
       "      <td>0.221838</td>\n",
       "      <td>58</td>\n",
       "      <td>28.25</td>\n",
       "      <td>2.12</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>...</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>(40.7287448, -74.03210820000002)</td>\n",
       "      <td>(40.72112360000001, -74.03805095)</td>\n",
       "      <td>0.611454</td>\n",
       "      <td>43</td>\n",
       "      <td>7.50</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>...</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7112423, -74.0557013)</td>\n",
       "      <td>1.217823</td>\n",
       "      <td>34</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.81</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>365</td>\n",
       "      <td>2015-10-01 00:41:46</td>\n",
       "      <td>2015-10-01 00:47:51</td>\n",
       "      <td>3198</td>\n",
       "      <td>Heights Elevator</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-74.040443</td>\n",
       "      <td>3215</td>\n",
       "      <td>Central Ave</td>\n",
       "      <td>40.746730</td>\n",
       "      <td>...</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>(40.748715945380226, -74.04044330120088)</td>\n",
       "      <td>(40.7467299, -74.0492509)</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>34</td>\n",
       "      <td>12.62</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>139</td>\n",
       "      <td>2015-10-01 00:43:44</td>\n",
       "      <td>2015-10-01 00:46:03</td>\n",
       "      <td>3206</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>40.731169</td>\n",
       "      <td>-74.057574</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>...</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>(40.7311689, -74.0575736)</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>30</td>\n",
       "      <td>7.08</td>\n",
       "      <td>8.48</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717653</td>\n",
       "      <td>239</td>\n",
       "      <td>2017-03-31 21:26:40</td>\n",
       "      <td>2017-03-31 21:30:39</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>-74.063784</td>\n",
       "      <td>3201</td>\n",
       "      <td>Dey St</td>\n",
       "      <td>40.737711</td>\n",
       "      <td>...</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>(40.737711, -74.066921)</td>\n",
       "      <td>0.508248</td>\n",
       "      <td>48</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.66</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717654</td>\n",
       "      <td>192</td>\n",
       "      <td>2017-03-31 21:46:57</td>\n",
       "      <td>2017-03-31 21:50:09</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3267</td>\n",
       "      <td>Morris Canal</td>\n",
       "      <td>40.712419</td>\n",
       "      <td>...</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7124188237569, -74.03852552175522)</td>\n",
       "      <td>0.374889</td>\n",
       "      <td>42</td>\n",
       "      <td>8.54</td>\n",
       "      <td>7.03</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717655</td>\n",
       "      <td>186</td>\n",
       "      <td>2017-03-31 22:03:56</td>\n",
       "      <td>2017-03-31 22:07:02</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>...</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.71277420000001, -74.03648570000001)</td>\n",
       "      <td>0.287536</td>\n",
       "      <td>62</td>\n",
       "      <td>10.78</td>\n",
       "      <td>5.57</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717656</td>\n",
       "      <td>618</td>\n",
       "      <td>2017-03-31 23:20:14</td>\n",
       "      <td>2017-03-31 23:30:32</td>\n",
       "      <td>3272</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "      <td>40.723332</td>\n",
       "      <td>-74.045953</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>...</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>(40.723331586464354, -74.04595255851744)</td>\n",
       "      <td>(40.71346382669195, -74.06285852193831)</td>\n",
       "      <td>1.118670</td>\n",
       "      <td>34</td>\n",
       "      <td>9.21</td>\n",
       "      <td>6.52</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717657</td>\n",
       "      <td>649</td>\n",
       "      <td>2017-03-31 23:25:34</td>\n",
       "      <td>2017-03-31 23:36:23</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3190</td>\n",
       "      <td>Garfield Ave Station</td>\n",
       "      <td>40.710467</td>\n",
       "      <td>...</td>\n",
       "      <td>10.816667</td>\n",
       "      <td>(40.7177325, -74.04384499999998)</td>\n",
       "      <td>(40.7104670233797, -74.07003879547119)</td>\n",
       "      <td>1.463817</td>\n",
       "      <td>30</td>\n",
       "      <td>7.39</td>\n",
       "      <td>8.12</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654640 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trip Duration          Start Time           Stop Time  \\\n",
       "0                 376 2015-10-01 00:16:26 2015-10-01 00:22:42   \n",
       "3                 275 2015-10-01 00:34:31 2015-10-01 00:39:06   \n",
       "4                 561 2015-10-01 00:40:12 2015-10-01 00:49:33   \n",
       "5                 365 2015-10-01 00:41:46 2015-10-01 00:47:51   \n",
       "6                 139 2015-10-01 00:43:44 2015-10-01 00:46:03   \n",
       "...               ...                 ...                 ...   \n",
       "717653            239 2017-03-31 21:26:40 2017-03-31 21:30:39   \n",
       "717654            192 2017-03-31 21:46:57 2017-03-31 21:50:09   \n",
       "717655            186 2017-03-31 22:03:56 2017-03-31 22:07:02   \n",
       "717656            618 2017-03-31 23:20:14 2017-03-31 23:30:32   \n",
       "717657            649 2017-03-31 23:25:34 2017-03-31 23:36:23   \n",
       "\n",
       "        Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0                   3212    Christ Hospital               40.734786   \n",
       "3                   3199       Newport Pkwy               40.728745   \n",
       "4                   3183     Exchange Place               40.716247   \n",
       "5                   3198   Heights Elevator               40.748716   \n",
       "6                   3206            Hilltop               40.731169   \n",
       "...                  ...                ...                     ...   \n",
       "717653              3195            Sip Ave               40.730743   \n",
       "717654              3183     Exchange Place               40.716247   \n",
       "717655              3183     Exchange Place               40.716247   \n",
       "717656              3272       Jersey & 3rd               40.723332   \n",
       "717657              3185          City Hall               40.717732   \n",
       "\n",
       "        Start Station Longitude  End Station ID      End Station Name  \\\n",
       "0                    -74.050444            3207           Oakland Ave   \n",
       "3                    -74.032108            3187             Warren St   \n",
       "4                    -74.033459            3192    Liberty Light Rail   \n",
       "5                    -74.040443            3215           Central Ave   \n",
       "6                    -74.057574            3195               Sip Ave   \n",
       "...                         ...             ...                   ...   \n",
       "717653               -74.063784            3201                Dey St   \n",
       "717654               -74.033459            3267          Morris Canal   \n",
       "717655               -74.033459            3214      Essex Light Rail   \n",
       "717656               -74.045953            3268        Lafayette Park   \n",
       "717657               -74.043845            3190  Garfield Ave Station   \n",
       "\n",
       "        End Station Latitude  ...    Minutes  \\\n",
       "0                  40.737604  ...   6.266667   \n",
       "3                  40.721124  ...   4.583333   \n",
       "4                  40.711242  ...   9.350000   \n",
       "5                  40.746730  ...   6.083333   \n",
       "6                  40.730743  ...   2.316667   \n",
       "...                      ...  ...        ...   \n",
       "717653             40.737711  ...   3.983333   \n",
       "717654             40.712419  ...   3.200000   \n",
       "717655             40.712774  ...   3.100000   \n",
       "717656             40.713464  ...  10.300000   \n",
       "717657             40.710467  ...  10.816667   \n",
       "\n",
       "                               Start Coordinates  \\\n",
       "0                  (40.734785818, -74.050443636)   \n",
       "3               (40.7287448, -74.03210820000002)   \n",
       "4                      (40.7162469, -74.0334588)   \n",
       "5       (40.748715945380226, -74.04044330120088)   \n",
       "6                      (40.7311689, -74.0575736)   \n",
       "...                                          ...   \n",
       "717653  (40.730742625306576, -74.06378388404845)   \n",
       "717654                 (40.7162469, -74.0334588)   \n",
       "717655                 (40.7162469, -74.0334588)   \n",
       "717656  (40.723331586464354, -74.04595255851744)   \n",
       "717657          (40.7177325, -74.04384499999998)   \n",
       "\n",
       "                                 End Coordinates  Distance Age  min_mile  \\\n",
       "0               (40.7376037, -74.05247829999998)  0.221838  58     28.25   \n",
       "3              (40.72112360000001, -74.03805095)  0.611454  43      7.50   \n",
       "4                      (40.7112423, -74.0557013)  1.217823  34      7.68   \n",
       "5                      (40.7467299, -74.0492509)  0.482094  34     12.62   \n",
       "6       (40.730742625306576, -74.06378388404845)  0.327309  30      7.08   \n",
       "...                                          ...       ...  ..       ...   \n",
       "717653                   (40.737711, -74.066921)  0.508248  48      7.84   \n",
       "717654    (40.7124188237569, -74.03852552175522)  0.374889  42      8.54   \n",
       "717655   (40.71277420000001, -74.03648570000001)  0.287536  62     10.78   \n",
       "717656   (40.71346382669195, -74.06285852193831)  1.118670  34      9.21   \n",
       "717657    (40.7104670233797, -74.07003879547119)  1.463817  30      7.39   \n",
       "\n",
       "        mile_hour d_week m_yr  ToD  \n",
       "0            2.12      3   10    0  \n",
       "3            8.00      3   10    0  \n",
       "4            7.81      3   10    0  \n",
       "5            4.75      3   10    0  \n",
       "6            8.48      3   10    0  \n",
       "...           ...    ...  ...  ...  \n",
       "717653       7.66      4    3   21  \n",
       "717654       7.03      4    3   21  \n",
       "717655       5.57      4    3   22  \n",
       "717656       6.52      4    3   23  \n",
       "717657       8.12      4    3   23  \n",
       "\n",
       "[654640 rows x 26 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = get_date_info2(pop_df)\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start_ID</th>\n",
       "      <th>Start_Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Start Coordinates</th>\n",
       "      <th>End Coordinates</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Age</th>\n",
       "      <th>min_mile</th>\n",
       "      <th>mile_hour</th>\n",
       "      <th>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>...</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>(40.734785818, -74.050443636)</td>\n",
       "      <td>(40.7376037, -74.05247829999998)</td>\n",
       "      <td>0.221838</td>\n",
       "      <td>58</td>\n",
       "      <td>28.25</td>\n",
       "      <td>2.12</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>...</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>(40.7287448, -74.03210820000002)</td>\n",
       "      <td>(40.72112360000001, -74.03805095)</td>\n",
       "      <td>0.611454</td>\n",
       "      <td>43</td>\n",
       "      <td>7.50</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>...</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7112423, -74.0557013)</td>\n",
       "      <td>1.217823</td>\n",
       "      <td>34</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.81</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>365</td>\n",
       "      <td>2015-10-01 00:41:46</td>\n",
       "      <td>2015-10-01 00:47:51</td>\n",
       "      <td>3198</td>\n",
       "      <td>Heights Elevator</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-74.040443</td>\n",
       "      <td>3215</td>\n",
       "      <td>Central Ave</td>\n",
       "      <td>40.746730</td>\n",
       "      <td>...</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>(40.748715945380226, -74.04044330120088)</td>\n",
       "      <td>(40.7467299, -74.0492509)</td>\n",
       "      <td>0.482094</td>\n",
       "      <td>34</td>\n",
       "      <td>12.62</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>139</td>\n",
       "      <td>2015-10-01 00:43:44</td>\n",
       "      <td>2015-10-01 00:46:03</td>\n",
       "      <td>3206</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>40.731169</td>\n",
       "      <td>-74.057574</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>...</td>\n",
       "      <td>2.316667</td>\n",
       "      <td>(40.7311689, -74.0575736)</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>30</td>\n",
       "      <td>7.08</td>\n",
       "      <td>8.48</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717653</td>\n",
       "      <td>239</td>\n",
       "      <td>2017-03-31 21:26:40</td>\n",
       "      <td>2017-03-31 21:30:39</td>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>40.730743</td>\n",
       "      <td>-74.063784</td>\n",
       "      <td>3201</td>\n",
       "      <td>Dey St</td>\n",
       "      <td>40.737711</td>\n",
       "      <td>...</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>(40.730742625306576, -74.06378388404845)</td>\n",
       "      <td>(40.737711, -74.066921)</td>\n",
       "      <td>0.508248</td>\n",
       "      <td>48</td>\n",
       "      <td>7.84</td>\n",
       "      <td>7.66</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717654</td>\n",
       "      <td>192</td>\n",
       "      <td>2017-03-31 21:46:57</td>\n",
       "      <td>2017-03-31 21:50:09</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3267</td>\n",
       "      <td>Morris Canal</td>\n",
       "      <td>40.712419</td>\n",
       "      <td>...</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.7124188237569, -74.03852552175522)</td>\n",
       "      <td>0.374889</td>\n",
       "      <td>42</td>\n",
       "      <td>8.54</td>\n",
       "      <td>7.03</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717655</td>\n",
       "      <td>186</td>\n",
       "      <td>2017-03-31 22:03:56</td>\n",
       "      <td>2017-03-31 22:07:02</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>...</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>(40.7162469, -74.0334588)</td>\n",
       "      <td>(40.71277420000001, -74.03648570000001)</td>\n",
       "      <td>0.287536</td>\n",
       "      <td>62</td>\n",
       "      <td>10.78</td>\n",
       "      <td>5.57</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717656</td>\n",
       "      <td>618</td>\n",
       "      <td>2017-03-31 23:20:14</td>\n",
       "      <td>2017-03-31 23:30:32</td>\n",
       "      <td>3272</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "      <td>40.723332</td>\n",
       "      <td>-74.045953</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>...</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>(40.723331586464354, -74.04595255851744)</td>\n",
       "      <td>(40.71346382669195, -74.06285852193831)</td>\n",
       "      <td>1.118670</td>\n",
       "      <td>34</td>\n",
       "      <td>9.21</td>\n",
       "      <td>6.52</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>717657</td>\n",
       "      <td>649</td>\n",
       "      <td>2017-03-31 23:25:34</td>\n",
       "      <td>2017-03-31 23:36:23</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717732</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3190</td>\n",
       "      <td>Garfield Ave Station</td>\n",
       "      <td>40.710467</td>\n",
       "      <td>...</td>\n",
       "      <td>10.816667</td>\n",
       "      <td>(40.7177325, -74.04384499999998)</td>\n",
       "      <td>(40.7104670233797, -74.07003879547119)</td>\n",
       "      <td>1.463817</td>\n",
       "      <td>30</td>\n",
       "      <td>7.39</td>\n",
       "      <td>8.12</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654640 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trip Duration          Start Time           Stop Time  Start_ID  \\\n",
       "0                 376 2015-10-01 00:16:26 2015-10-01 00:22:42      3212   \n",
       "3                 275 2015-10-01 00:34:31 2015-10-01 00:39:06      3199   \n",
       "4                 561 2015-10-01 00:40:12 2015-10-01 00:49:33      3183   \n",
       "5                 365 2015-10-01 00:41:46 2015-10-01 00:47:51      3198   \n",
       "6                 139 2015-10-01 00:43:44 2015-10-01 00:46:03      3206   \n",
       "...               ...                 ...                 ...       ...   \n",
       "717653            239 2017-03-31 21:26:40 2017-03-31 21:30:39      3195   \n",
       "717654            192 2017-03-31 21:46:57 2017-03-31 21:50:09      3183   \n",
       "717655            186 2017-03-31 22:03:56 2017-03-31 22:07:02      3183   \n",
       "717656            618 2017-03-31 23:20:14 2017-03-31 23:30:32      3272   \n",
       "717657            649 2017-03-31 23:25:34 2017-03-31 23:36:23      3185   \n",
       "\n",
       "              Start_Name  Start Station Latitude  Start Station Longitude  \\\n",
       "0        Christ Hospital               40.734786               -74.050444   \n",
       "3           Newport Pkwy               40.728745               -74.032108   \n",
       "4         Exchange Place               40.716247               -74.033459   \n",
       "5       Heights Elevator               40.748716               -74.040443   \n",
       "6                Hilltop               40.731169               -74.057574   \n",
       "...                  ...                     ...                      ...   \n",
       "717653           Sip Ave               40.730743               -74.063784   \n",
       "717654    Exchange Place               40.716247               -74.033459   \n",
       "717655    Exchange Place               40.716247               -74.033459   \n",
       "717656      Jersey & 3rd               40.723332               -74.045953   \n",
       "717657         City Hall               40.717732               -74.043845   \n",
       "\n",
       "        End Station ID      End Station Name  End Station Latitude  ...  \\\n",
       "0                 3207           Oakland Ave             40.737604  ...   \n",
       "3                 3187             Warren St             40.721124  ...   \n",
       "4                 3192    Liberty Light Rail             40.711242  ...   \n",
       "5                 3215           Central Ave             40.746730  ...   \n",
       "6                 3195               Sip Ave             40.730743  ...   \n",
       "...                ...                   ...                   ...  ...   \n",
       "717653            3201                Dey St             40.737711  ...   \n",
       "717654            3267          Morris Canal             40.712419  ...   \n",
       "717655            3214      Essex Light Rail             40.712774  ...   \n",
       "717656            3268        Lafayette Park             40.713464  ...   \n",
       "717657            3190  Garfield Ave Station             40.710467  ...   \n",
       "\n",
       "          Minutes                         Start Coordinates  \\\n",
       "0        6.266667             (40.734785818, -74.050443636)   \n",
       "3        4.583333          (40.7287448, -74.03210820000002)   \n",
       "4        9.350000                 (40.7162469, -74.0334588)   \n",
       "5        6.083333  (40.748715945380226, -74.04044330120088)   \n",
       "6        2.316667                 (40.7311689, -74.0575736)   \n",
       "...           ...                                       ...   \n",
       "717653   3.983333  (40.730742625306576, -74.06378388404845)   \n",
       "717654   3.200000                 (40.7162469, -74.0334588)   \n",
       "717655   3.100000                 (40.7162469, -74.0334588)   \n",
       "717656  10.300000  (40.723331586464354, -74.04595255851744)   \n",
       "717657  10.816667          (40.7177325, -74.04384499999998)   \n",
       "\n",
       "                                 End Coordinates  Distance Age  min_mile  \\\n",
       "0               (40.7376037, -74.05247829999998)  0.221838  58     28.25   \n",
       "3              (40.72112360000001, -74.03805095)  0.611454  43      7.50   \n",
       "4                      (40.7112423, -74.0557013)  1.217823  34      7.68   \n",
       "5                      (40.7467299, -74.0492509)  0.482094  34     12.62   \n",
       "6       (40.730742625306576, -74.06378388404845)  0.327309  30      7.08   \n",
       "...                                          ...       ...  ..       ...   \n",
       "717653                   (40.737711, -74.066921)  0.508248  48      7.84   \n",
       "717654    (40.7124188237569, -74.03852552175522)  0.374889  42      8.54   \n",
       "717655   (40.71277420000001, -74.03648570000001)  0.287536  62     10.78   \n",
       "717656   (40.71346382669195, -74.06285852193831)  1.118670  34      9.21   \n",
       "717657    (40.7104670233797, -74.07003879547119)  1.463817  30      7.39   \n",
       "\n",
       "        mile_hour d_week m_yr  ToD  \n",
       "0            2.12      3   10    0  \n",
       "3            8.00      3   10    0  \n",
       "4            7.81      3   10    0  \n",
       "5            4.75      3   10    0  \n",
       "6            8.48      3   10    0  \n",
       "...           ...    ...  ...  ...  \n",
       "717653       7.66      4    3   21  \n",
       "717654       7.03      4    3   21  \n",
       "717655       5.57      4    3   22  \n",
       "717656       6.52      4    3   23  \n",
       "717657       8.12      4    3   23  \n",
       "\n",
       "[654640 rows x 26 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = pop_df.rename(columns={\"Start Station ID\": \"Start_ID\"})\n",
    "pop_df = pop_df.rename(columns={\"Start Station Name\": \"Start_Name\"})\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Start_Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3184</td>\n",
       "      <td>Paulus Hook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3188</td>\n",
       "      <td>NJCU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3189</td>\n",
       "      <td>West Side Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3190</td>\n",
       "      <td>Garfield Ave Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3191</td>\n",
       "      <td>Union St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3194</td>\n",
       "      <td>McGinley Square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3195</td>\n",
       "      <td>Sip Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3196</td>\n",
       "      <td>Riverview Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3197</td>\n",
       "      <td>North St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3198</td>\n",
       "      <td>Heights Elevator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3200</td>\n",
       "      <td>MLK Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3201</td>\n",
       "      <td>Dey St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3202</td>\n",
       "      <td>Newport PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3203</td>\n",
       "      <td>Hamilton Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3205</td>\n",
       "      <td>JC Medical Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3206</td>\n",
       "      <td>Hilltop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3209</td>\n",
       "      <td>Brunswick St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3210</td>\n",
       "      <td>Pershing Field</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3211</td>\n",
       "      <td>Newark Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3215</td>\n",
       "      <td>Central Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3216</td>\n",
       "      <td>Columbia Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3217</td>\n",
       "      <td>Bayside Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3220</td>\n",
       "      <td>5 Corners Library</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3225</td>\n",
       "      <td>Baldwin at Montgomery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3267</td>\n",
       "      <td>Morris Canal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3269</td>\n",
       "      <td>Brunswick &amp; 6th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3270</td>\n",
       "      <td>Jersey &amp; 6th St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3271</td>\n",
       "      <td>Danforth Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3272</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3273</td>\n",
       "      <td>Manila &amp; 1st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3274</td>\n",
       "      <td>Bethune Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3275</td>\n",
       "      <td>Columbus Drive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3276</td>\n",
       "      <td>Marin Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3277</td>\n",
       "      <td>Communipaw &amp; Berry Lane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3278</td>\n",
       "      <td>Monmouth and 6th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3279</td>\n",
       "      <td>Dixon Mills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3280</td>\n",
       "      <td>Astor Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3281</td>\n",
       "      <td>Leonard Gordon Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3426</td>\n",
       "      <td>JCBS Depot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Start_Name\n",
       "Start_ID                         \n",
       "3183               Exchange Place\n",
       "3184                  Paulus Hook\n",
       "3185                    City Hall\n",
       "3186                Grove St PATH\n",
       "3187                    Warren St\n",
       "3188                         NJCU\n",
       "3189         West Side Light Rail\n",
       "3190         Garfield Ave Station\n",
       "3191                     Union St\n",
       "3192           Liberty Light Rail\n",
       "3193                 Lincoln Park\n",
       "3194              McGinley Square\n",
       "3195                      Sip Ave\n",
       "3196               Riverview Park\n",
       "3197                     North St\n",
       "3198             Heights Elevator\n",
       "3199                 Newport Pkwy\n",
       "3200               MLK Light Rail\n",
       "3201                       Dey St\n",
       "3202                 Newport PATH\n",
       "3203                Hamilton Park\n",
       "3205            JC Medical Center\n",
       "3206                      Hilltop\n",
       "3207                  Oakland Ave\n",
       "3209                 Brunswick St\n",
       "3210               Pershing Field\n",
       "3211                   Newark Ave\n",
       "3212              Christ Hospital\n",
       "3213               Van Vorst Park\n",
       "3214             Essex Light Rail\n",
       "3215                  Central Ave\n",
       "3216                Columbia Park\n",
       "3217                 Bayside Park\n",
       "3220            5 Corners Library\n",
       "3225        Baldwin at Montgomery\n",
       "3267                 Morris Canal\n",
       "3268               Lafayette Park\n",
       "3269              Brunswick & 6th\n",
       "3270              Jersey & 6th St\n",
       "3271          Danforth Light Rail\n",
       "3272                 Jersey & 3rd\n",
       "3273                 Manila & 1st\n",
       "3274               Bethune Center\n",
       "3275               Columbus Drive\n",
       "3276             Marin Light Rail\n",
       "3277      Communipaw & Berry Lane\n",
       "3278             Monmouth and 6th\n",
       "3279                  Dixon Mills\n",
       "3280                  Astor Place\n",
       "3281          Leonard Gordon Park\n",
       "3426                   JCBS Depot"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify the 10 most popular trips\n",
    "pop_df_2=pop_df\n",
    "final_station=pop_df_2.groupby(['Start_ID', 'Start_Name']).size().reset_index(name = 'Count') \n",
    "final_station=final_station.drop([\"Count\"], axis=1)\n",
    "final_station=final_station.set_index('Start_ID')\n",
    "final_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "      <th>Popular_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3185.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3183.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  d_week m_yr  ToD  Popular_id\n",
       "0      0    1    0      3186.0\n",
       "1      0    1    1      3211.0\n",
       "2      0    1    2      3185.0\n",
       "3      0    1    3         NaN\n",
       "4      0    1    4      3183.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify the 10 most popular trips\n",
    "pop_df_2=pop_df\n",
    "pop_df_2= pop_df_2.groupby(['d_week','m_yr', 'ToD']).Start_ID.apply(lambda x : x.mode()[0]).reset_index(name = 'Popular_id')\n",
    "#pop_df_2['Popular_id'] = pop_df_2['Popular_id'].astype('int')\n",
    "pop_df_2.head()\n",
    "#trips_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Exchange Place'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_station.get_value(3183,'Start_Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "      <th>Popular_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3211.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3183.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  d_week m_yr  ToD  Popular_id\n",
       "0      0    1    0      3186.0\n",
       "1      0    1    1      3211.0\n",
       "2      0    1    2      3185.0\n",
       "3      0    1    3         NaN\n",
       "4      0    1    4      3183.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df_2=pop_df_2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "pop_df_2 = pop_df_2.apply(le.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "      <th>Popular_id</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      d_week  m_yr  ToD  Popular_id\n",
       "0          0     0    0           3\n",
       "1          0     0    1          21\n",
       "2          0     0    2           2\n",
       "4          0     0    4           0\n",
       "5          0     0    5          18\n",
       "...      ...   ...  ...         ...\n",
       "2011       6    11   19           3\n",
       "2012       6    11   20           3\n",
       "2013       6    11   21           3\n",
       "2014       6    11   22           3\n",
       "2015       6    11   23           3\n",
       "\n",
       "[2011 rows x 4 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pop_df_2.iloc[:,0:3]\n",
    "y = pop_df_2.iloc[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>984</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>522</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1976</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1825</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>839</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1221</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1658</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>688</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1608 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      d_week  m_yr  ToD\n",
       "984        3     5    0\n",
       "522        1     9   18\n",
       "1976       6    10    8\n",
       "361        1     3    1\n",
       "1825       6     4    1\n",
       "...      ...   ...  ...\n",
       "839        2    10   23\n",
       "1221       4     2   21\n",
       "1658       5     9    2\n",
       "560        1    11    8\n",
       "688        2     4   16\n",
       "\n",
       "[1608 rows x 3 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36476426799007444\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train, y_train)\n",
    "print(classifier.score(X_test,y_test))\n",
    "\n",
    "\n",
    "#clf.predict_proba(X[:2, :]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      d_week  m_yr  ToD\n",
      "984        3     5    0\n",
      "522        1     9   18\n",
      "1976       6    10    8\n",
      "361        1     3    1\n",
      "1825       6     4    1\n",
      "...      ...   ...  ...\n",
      "839        2    10   23\n",
      "1221       4     2   21\n",
      "1658       5     9    2\n",
      "560        1    11    8\n",
      "688        2     4   16\n",
      "\n",
      "[1608 rows x 3 columns]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.22      0.05      0.08        42\n",
      "           1       0.00      0.00      0.00         3\n",
      "           2       0.23      0.21      0.22        14\n",
      "           3       0.60      0.97      0.74       152\n",
      "           4       0.00      0.00      0.00         6\n",
      "           6       0.00      0.00      0.00         1\n",
      "           7       0.00      0.00      0.00         3\n",
      "           8       0.00      0.00      0.00         1\n",
      "           9       0.00      0.00      0.00         9\n",
      "          10       0.39      0.19      0.26        36\n",
      "          11       0.00      0.00      0.00         1\n",
      "          13       0.00      0.00      0.00         8\n",
      "          14       0.00      0.00      0.00        12\n",
      "          15       0.52      0.85      0.65        48\n",
      "          17       0.00      0.00      0.00         1\n",
      "          18       0.29      0.62      0.40         8\n",
      "          19       0.56      0.36      0.43        14\n",
      "          21       0.00      0.00      0.00        14\n",
      "          22       0.00      0.00      0.00         9\n",
      "          23       0.00      0.00      0.00         7\n",
      "          24       0.33      0.67      0.44         3\n",
      "          26       0.00      0.00      0.00         2\n",
      "          27       0.00      0.00      0.00         5\n",
      "          29       0.00      0.00      0.00         1\n",
      "          31       0.00      0.00      0.00         1\n",
      "          33       0.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.53       403\n",
      "   macro avg       0.12      0.15      0.12       403\n",
      "weighted avg       0.38      0.53      0.42       403\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(11, 16), random_state=1)\n",
    "clf.fit(X_train, y_train) \n",
    "\n",
    "print(X_train)\n",
    "\n",
    "predict_train = clf.predict(X_train)\n",
    "predict_test = clf.predict(X_test)\n",
    "#print(predict_test)\n",
    "\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "#print(confusion_matrix(y_train,predict_train))\n",
    "#print(classification_report(y_train,predict_train))\n",
    "\n",
    "#print(confusion_matrix(y_test,predict_test))\n",
    "print(classification_report(y_test,predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test_tran = scaler.transform(X_test)\n",
    "\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 42)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.28      0.31      0.30        42\n",
      "           1       0.00      0.00      0.00         3\n",
      "           2       0.15      0.14      0.15        14\n",
      "           3       0.66      0.79      0.72       152\n",
      "           4       0.00      0.00      0.00         6\n",
      "           5       0.00      0.00      0.00         0\n",
      "           6       0.00      0.00      0.00         1\n",
      "           7       0.00      0.00      0.00         3\n",
      "           8       0.00      0.00      0.00         1\n",
      "           9       0.20      0.11      0.14         9\n",
      "          10       0.17      0.11      0.14        36\n",
      "          11       0.00      0.00      0.00         1\n",
      "          13       0.33      0.12      0.18         8\n",
      "          14       0.14      0.08      0.11        12\n",
      "          15       0.58      0.75      0.65        48\n",
      "          17       0.00      0.00      0.00         1\n",
      "          18       0.22      0.25      0.24         8\n",
      "          19       0.38      0.21      0.27        14\n",
      "          21       0.00      0.00      0.00        14\n",
      "          22       0.00      0.00      0.00         9\n",
      "          23       0.25      0.14      0.18         7\n",
      "          24       0.00      0.00      0.00         3\n",
      "          25       0.00      0.00      0.00         0\n",
      "          26       0.00      0.00      0.00         2\n",
      "          27       0.40      0.40      0.40         5\n",
      "          29       0.00      0.00      0.00         1\n",
      "          30       0.00      0.00      0.00         0\n",
      "          31       0.00      0.00      0.00         1\n",
      "          33       0.00      0.00      0.00         2\n",
      "\n",
      "    accuracy                           0.46       403\n",
      "   macro avg       0.13      0.12      0.12       403\n",
      "weighted avg       0.41      0.46      0.43       403\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "y_pred_tran = classifier.predict(X_test_tran)\n",
    "print(classification_report(y_test, y_pred_tran))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3186., 3195., 3186., 3267., 3186., 3203., 3186., 3186., 3267.,\n",
       "       3203., 3186., 3186., 3203., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3186., 3186., 3217., 3186., 3185., 3186., 3203., 3186., 3186.,\n",
       "       3186., 3186., 3186., 3209., 3183., 3186., 3186., 3203., 3186.,\n",
       "       3186., 3203., 3186., 3186., 3186., 3186., 3203., 3185., 3186.,\n",
       "       3186., 3186., 3186., 3203., 3195., 3186., 3185., 3203., 3186.,\n",
       "       3186., 3186., 3186., 3203., 3186., 3186., 3195., 3186., 3186.,\n",
       "       3186., 3186., 3203., 3186., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3214., 3195., 3183., 3186., 3185., 3211., 3186., 3209., 3185.,\n",
       "       3186., 3186., 3203., 3186., 3186., 3186., 3195., 3195., 3186.,\n",
       "       3186., 3203., 3186., 3207., 3186., 3185., 3186., 3217., 3186.,\n",
       "       3186., 3186., 3183., 3186., 3186., 3203., 3186., 3186., 3203.,\n",
       "       3207., 3186., 3186., 3186., 3203., 3203., 3186., 3186., 3203.,\n",
       "       3186., 3203., 3203., 3186., 3186., 3186., 3207., 3186., 3209.,\n",
       "       3186., 3186., 3186., 3195., 3186., 3186., 3203., 3203., 3203.,\n",
       "       3195., 3203., 3186., 3186., 3217., 3203., 3186., 3186., 3207.,\n",
       "       3207., 3186., 3186., 3186., 3186., 3195., 3186., 3186., 3203.,\n",
       "       3183., 3185., 3186., 3186., 3207., 3186., 3186., 3183., 3186.,\n",
       "       3186., 3186., 3186., 3203., 3186., 3203., 3186., 3203., 3186.,\n",
       "       3203., 3207., 3186., 3203., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3186., 3203., 3195., 3186., 3186., 3209., 3203., 3186., 3207.,\n",
       "       3185., 3203., 3186., 3186., 3186., 3186., 3186., 3217., 3195.,\n",
       "       3186., 3203., 3186., 3183., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3186., 3186., 3209., 3186., 3203., 3186., 3186., 3186., 3185.,\n",
       "       3186., 3203., 3195., 3186., 3203., 3203., 3186., 3186., 3186.,\n",
       "       3207., 3186., 3186., 3203., 3207., 3195., 3203., 3186., 3186.,\n",
       "       3195., 3186., 3203., 3186., 3186., 3209., 3186., 3186., 3209.,\n",
       "       3186., 3203., 3203., 3186., 3195., 3186., 3186., 3186., 3185.,\n",
       "       3186., 3194., 3186., 3186., 3203., 3186., 3186., 3186., 3203.,\n",
       "       3186., 3186., 3183., 3186., 3186., 3203., 3186., 3185., 3186.,\n",
       "       3186., 3186., 3185., 3186., 3203., 3203., 3194., 3207., 3186.,\n",
       "       3186., 3203., 3186., 3203., 3203., 3183., 3209., 3186., 3203.,\n",
       "       3203., 3203., 3186., 3203., 3203., 3203., 3186., 3186., 3186.,\n",
       "       3186., 3186., 3203., 3186., 3186., 3203., 3186., 3203., 3186.,\n",
       "       3186., 3186., 3203., 3186., 3186., 3186., 3186., 3185., 3207.,\n",
       "       3186., 3207., 3183., 3186., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3186., 3186., 3186., 3186., 3186., 3186., 3186., 3217., 3203.,\n",
       "       3203., 3186., 3186., 3207., 3186., 3186., 3186., 3195., 3186.,\n",
       "       3186., 3186., 3186., 3186., 3186., 3186., 3186., 3186., 3209.,\n",
       "       3203., 3186., 3203., 3203., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3203., 3186., 3207., 3186., 3195., 3186., 3186., 3207., 3186.,\n",
       "       3203., 3186., 3186., 3195., 3203., 3186., 3186., 3203., 3186.,\n",
       "       3186., 3186., 3186., 3186., 3203., 3186., 3186., 3203., 3186.,\n",
       "       3186., 3186., 3217., 3186., 3203., 3186., 3203., 3186., 3186.,\n",
       "       3186., 3203., 3207., 3186., 3203., 3203., 3203.])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred_inverse = le.inverse_transform(predict_test)\n",
    "ypred_inverse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3186., 3183., 3195., 3209., 3202., 3203., 3186., 3186., 3190.,\n",
       "       3194., 3207., 3186., 3199., 3186., 3186., 3186., 3186., 3183.,\n",
       "       3183., 3186., 3217., 3183., 3185., 3186., 3187., 3186., 3186.,\n",
       "       3186., 3186., 3186., 3207., 3183., 3186., 3186., 3267., 3186.,\n",
       "       3186., 3203., 3186., 3186., 3186., 3186., 3202., 3185., 3186.,\n",
       "       3195., 3195., 3186., 3203., 3195., 3186., 3186., 3203., 3186.,\n",
       "       3202., 3186., 3186., 3186., 3186., 3186., 3195., 3185., 3186.,\n",
       "       3186., 3213., 3214., 3186., 3183., 3186., 3185., 3186., 3187.,\n",
       "       3211., 3185., 3186., 3186., 3211., 3195., 3202., 3192., 3213.,\n",
       "       3183., 3186., 3203., 3183., 3186., 3186., 3183., 3183., 3186.,\n",
       "       3186., 3194., 3185., 3207., 3183., 3194., 3186., 3199., 3185.,\n",
       "       3186., 3186., 3206., 3186., 3186., 3203., 3186., 3186., 3203.,\n",
       "       3207., 3207., 3186., 3183., 3213., 3203., 3195., 3186., 3209.,\n",
       "       3186., 3184., 3202., 3183., 3186., 3186., 3225., 3186., 3209.,\n",
       "       3186., 3186., 3203., 3195., 3183., 3186., 3203., 3209., 3214.,\n",
       "       3194., 3203., 3183., 3186., 3211., 3209., 3186., 3184., 3203.,\n",
       "       3213., 3186., 3186., 3186., 3183., 3183., 3195., 3199., 3209.,\n",
       "       3203., 3194., 3186., 3186., 3199., 3186., 3186., 3195., 3186.,\n",
       "       3186., 3186., 3186., 3203., 3199., 3203., 3186., 3203., 3186.,\n",
       "       3203., 3207., 3186., 3203., 3211., 3186., 3186., 3186., 3186.,\n",
       "       3186., 3214., 3202., 3195., 3186., 3193., 3203., 3195., 3209.,\n",
       "       3211., 3279., 3211., 3183., 3186., 3192., 3195., 3187., 3185.,\n",
       "       3186., 3214., 3186., 3186., 3186., 3184., 3183., 3183., 3183.,\n",
       "       3186., 3195., 3209., 3202., 3186., 3195., 3186., 3186., 3211.,\n",
       "       3186., 3203., 3217., 3186., 3203., 3203., 3186., 3186., 3185.,\n",
       "       3197., 3183., 3186., 3199., 3209., 3185., 3214., 3185., 3186.,\n",
       "       3195., 3186., 3203., 3186., 3183., 3203., 3183., 3186., 3209.,\n",
       "       3186., 3209., 3203., 3183., 3195., 3186., 3186., 3195., 3211.,\n",
       "       3186., 3276., 3183., 3186., 3203., 3186., 3186., 3183., 3214.,\n",
       "       3186., 3183., 3183., 3186., 3186., 3203., 3186., 3183., 3183.,\n",
       "       3203., 3203., 3202., 3183., 3203., 3203., 3279., 3267., 3183.,\n",
       "       3186., 3203., 3186., 3203., 3187., 3272., 3209., 3195., 3203.,\n",
       "       3187., 3203., 3186., 3203., 3211., 3213., 3183., 3186., 3203.,\n",
       "       3186., 3199., 3203., 3195., 3202., 3203., 3195., 3203., 3186.,\n",
       "       3186., 3202., 3203., 3186., 3195., 3195., 3186., 3185., 3194.,\n",
       "       3195., 3267., 3195., 3195., 3186., 3186., 3186., 3186., 3186.,\n",
       "       3183., 3183., 3186., 3186., 3186., 3194., 3186., 3217., 3213.,\n",
       "       3213., 3183., 3199., 3225., 3186., 3185., 3186., 3211., 3194.,\n",
       "       3186., 3195., 3186., 3195., 3213., 3186., 3186., 3186., 3209.,\n",
       "       3203., 3186., 3209., 3203., 3183., 3195., 3186., 3186., 3183.,\n",
       "       3203., 3186., 3207., 3202., 3195., 3186., 3186., 3267., 3211.,\n",
       "       3203., 3185., 3183., 3195., 3267., 3183., 3186., 3202., 3186.,\n",
       "       3211., 3186., 3186., 3186., 3211., 3195., 3195., 3194., 3186.,\n",
       "       3195., 3195., 3192., 3186., 3203., 3211., 3203., 3195., 3186.,\n",
       "       3186., 3213., 3207., 3183., 3203., 3214., 3187.])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest_inverse = le.inverse_transform(y_test)\n",
    "ytest_inverse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Morris Canal',\n",
       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Morris Canal',\n",
       " 'Hamilton Park',\n",
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       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Bayside Park',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
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       " 'Hamilton Park',\n",
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       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
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       " 'Hamilton Park',\n",
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       " 'Sip Ave',\n",
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       " 'Grove St PATH',\n",
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       " 'Brunswick St',\n",
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       " 'Grove St PATH',\n",
       " 'Oakland Ave',\n",
       " 'City Hall',\n",
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       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
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       " 'Hamilton Park',\n",
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       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
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       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
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       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'McGinley Square',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
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       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
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       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
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       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
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       " 'Hamilton Park',\n",
       " 'McGinley Square',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
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       " 'Hamilton Park',\n",
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       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
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       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
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       " 'City Hall',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Oakland Ave',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Bayside Park',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Bayside Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park']"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_station\n",
    "ypred_inverse_final = []\n",
    "\n",
    "for i in ypred_inverse: \n",
    "    ypred_inverse_final.append(final_station.get_value(i,'Start_Name'))\n",
    "    \n",
    "ypred_inverse_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Sip Ave',\n",
       " 'Brunswick St',\n",
       " 'Newport PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Garfield Ave Station',\n",
       " 'McGinley Square',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Newport Pkwy',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
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       " 'Grove St PATH',\n",
       " 'Bayside Park',\n",
       " 'Exchange Place',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Warren St',\n",
       " 'Grove St PATH',\n",
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       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
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       " 'Morris Canal',\n",
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       " 'Newport PATH',\n",
       " 'City Hall',\n",
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       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
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       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Warren St',\n",
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       " 'Exchange Place',\n",
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       " 'Grove St PATH',\n",
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       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'McGinley Square',\n",
       " 'City Hall',\n",
       " 'Oakland Ave',\n",
       " 'Exchange Place',\n",
       " 'McGinley Square',\n",
       " 'Grove St PATH',\n",
       " 'Newport Pkwy',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hilltop',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Oakland Ave',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Van Vorst Park',\n",
       " 'Hamilton Park',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Grove St PATH',\n",
       " 'Paulus Hook',\n",
       " 'Newport PATH',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Baldwin at Montgomery',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Sip Ave',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Brunswick St',\n",
       " 'Essex Light Rail',\n",
       " 'McGinley Square',\n",
       " 'Hamilton Park',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Newark Ave',\n",
       " 'Brunswick St',\n",
       " 'Grove St PATH',\n",
       " 'Paulus Hook',\n",
       " 'Hamilton Park',\n",
       " 'Van Vorst Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Exchange Place',\n",
       " 'Sip Ave',\n",
       " 'Newport Pkwy',\n",
       " 'Brunswick St',\n",
       " 'Hamilton Park',\n",
       " 'McGinley Square',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Newport Pkwy',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Newport Pkwy',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Oakland Ave',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Newark Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Essex Light Rail',\n",
       " 'Newport PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Lincoln Park',\n",
       " 'Hamilton Park',\n",
       " 'Sip Ave',\n",
       " 'Brunswick St',\n",
       " 'Newark Ave',\n",
       " 'Dixon Mills',\n",
       " 'Newark Ave',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Liberty Light Rail',\n",
       " 'Sip Ave',\n",
       " 'Warren St',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Essex Light Rail',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Paulus Hook',\n",
       " 'Exchange Place',\n",
       " 'Exchange Place',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Brunswick St',\n",
       " 'Newport PATH',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Newark Ave',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Bayside Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'North St',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Newport Pkwy',\n",
       " 'Brunswick St',\n",
       " 'City Hall',\n",
       " 'Essex Light Rail',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Hamilton Park',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Hamilton Park',\n",
       " 'Exchange Place',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Newark Ave',\n",
       " 'Grove St PATH',\n",
       " 'Marin Light Rail',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Essex Light Rail',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Exchange Place',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Newport PATH',\n",
       " 'Exchange Place',\n",
       " 'Hamilton Park',\n",
       " 'Hamilton Park',\n",
       " 'Dixon Mills',\n",
       " 'Morris Canal',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Warren St',\n",
       " 'Jersey & 3rd',\n",
       " 'Brunswick St',\n",
       " 'Sip Ave',\n",
       " 'Hamilton Park',\n",
       " 'Warren St',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Newark Ave',\n",
       " 'Van Vorst Park',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Newport Pkwy',\n",
       " 'Hamilton Park',\n",
       " 'Sip Ave',\n",
       " 'Newport PATH',\n",
       " 'Hamilton Park',\n",
       " 'Sip Ave',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Newport PATH',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'McGinley Square',\n",
       " 'Sip Ave',\n",
       " 'Morris Canal',\n",
       " 'Sip Ave',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'McGinley Square',\n",
       " 'Grove St PATH',\n",
       " 'Bayside Park',\n",
       " 'Van Vorst Park',\n",
       " 'Van Vorst Park',\n",
       " 'Exchange Place',\n",
       " 'Newport Pkwy',\n",
       " 'Baldwin at Montgomery',\n",
       " 'Grove St PATH',\n",
       " 'City Hall',\n",
       " 'Grove St PATH',\n",
       " 'Newark Ave',\n",
       " 'McGinley Square',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Van Vorst Park',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Brunswick St',\n",
       " 'Hamilton Park',\n",
       " 'Exchange Place',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Exchange Place',\n",
       " 'Hamilton Park',\n",
       " 'Grove St PATH',\n",
       " 'Oakland Ave',\n",
       " 'Newport PATH',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Morris Canal',\n",
       " 'Newark Ave',\n",
       " 'Hamilton Park',\n",
       " 'City Hall',\n",
       " 'Exchange Place',\n",
       " 'Sip Ave',\n",
       " 'Morris Canal',\n",
       " 'Exchange Place',\n",
       " 'Grove St PATH',\n",
       " 'Newport PATH',\n",
       " 'Grove St PATH',\n",
       " 'Newark Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Newark Ave',\n",
       " 'Sip Ave',\n",
       " 'Sip Ave',\n",
       " 'McGinley Square',\n",
       " 'Grove St PATH',\n",
       " 'Sip Ave',\n",
       " 'Sip Ave',\n",
       " 'Liberty Light Rail',\n",
       " 'Grove St PATH',\n",
       " 'Hamilton Park',\n",
       " 'Newark Ave',\n",
       " 'Hamilton Park',\n",
       " 'Sip Ave',\n",
       " 'Grove St PATH',\n",
       " 'Grove St PATH',\n",
       " 'Van Vorst Park',\n",
       " 'Oakland Ave',\n",
       " 'Exchange Place',\n",
       " 'Hamilton Park',\n",
       " 'Essex Light Rail',\n",
       " 'Warren St']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_station\n",
    "ytest_inverse_final = []\n",
    "\n",
    "for i in ytest_inverse: \n",
    "    ytest_inverse_final.append(final_station.get_value(i,'Start_Name'))\n",
    "    \n",
    "ytest_inverse_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>Grove St PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Sip Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>Grove St PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Morris Canal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>Grove St PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>Oakland Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>Grove St PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Hamilton Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Hamilton Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>402</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Hamilton Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     d_week  m_yr  ToD         y_pred\n",
       "0         0     9   14  Grove St PATH\n",
       "1         4     3    1        Sip Ave\n",
       "2         6     3   23  Grove St PATH\n",
       "3         1     6    5   Morris Canal\n",
       "4         2    10   13  Grove St PATH\n",
       "..      ...   ...  ...            ...\n",
       "398       2     9    5    Oakland Ave\n",
       "399       4     3   16  Grove St PATH\n",
       "400       1     0   11  Hamilton Park\n",
       "401       0     2    5  Hamilton Park\n",
       "402       5     7    9  Hamilton Park\n",
       "\n",
       "[403 rows x 4 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ypred = X_test.copy()\n",
    "df_ypred['y_pred'] = ypred_inverse_final\n",
    "df_ypred = df_ypred.reset_index()\n",
    "df_ypred = df_ypred.drop('index',axis=1)\n",
    "df_ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>d_week</th>\n",
       "      <th>m_yr</th>\n",
       "      <th>ToD</th>\n",
       "      <th>y_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>Grove St PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Exchange Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>Sip Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Brunswick St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>Newport PATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>Oakland Ave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>Exchange Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Hamilton Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>401</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>402</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Warren St</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     d_week  m_yr  ToD            y_test\n",
       "0         0     9   14     Grove St PATH\n",
       "1         4     3    1    Exchange Place\n",
       "2         6     3   23           Sip Ave\n",
       "3         1     6    5      Brunswick St\n",
       "4         2    10   13      Newport PATH\n",
       "..      ...   ...  ...               ...\n",
       "398       2     9    5       Oakland Ave\n",
       "399       4     3   16    Exchange Place\n",
       "400       1     0   11     Hamilton Park\n",
       "401       0     2    5  Essex Light Rail\n",
       "402       5     7    9         Warren St\n",
       "\n",
       "[403 rows x 4 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_ytest = pd.DataFrame({\"col1\":X_test[:, 0], \"col2\":X_test[:,1], \"col3\":X_test[:,2]})\n",
    "df_ytest = pd.DataFrame(X_test)\n",
    "df_ytest['y_test'] = ytest_inverse_final\n",
    "df_ytest = df_ytest.reset_index()\n",
    "df_ytest = df_ytest.drop('index',axis=1)\n",
    "df_ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a download=\"data_ytest.csv\" href=\"data:text/csv;base64,,d_week,m_yr,ToD,y_pred
0,0,9,14,Grove St PATH
1,4,3,1,Sip Ave
2,6,3,23,Grove St PATH
3,1,6,5,Morris Canal
4,2,10,13,Grove St PATH
5,1,11,7,Hamilton Park
6,3,7,22,Grove St PATH
7,4,2,18,Grove St PATH
8,0,6,5,Morris Canal
9,1,8,10,Hamilton Park
10,6,11,5,Grove St PATH
11,3,7,23,Grove St PATH
12,5,5,11,Hamilton Park
13,2,10,17,Grove St PATH
14,6,10,1,Grove St PATH
15,3,3,12,Grove St PATH
16,3,0,22,Grove St PATH
17,3,1,16,Grove St PATH
18,6,0,5,Grove St PATH
19,6,10,2,Grove St PATH
20,1,9,3,Bayside Park
21,5,2,1,Grove St PATH
22,4,8,2,City Hall
23,5,9,4,Grove St PATH
24,3,6,10,Hamilton Park
25,0,7,20,Grove St PATH
26,6,0,2,Grove St PATH
27,4,0,17,Grove St PATH
28,0,0,19,Grove St PATH
29,6,1,2,Grove St PATH
30,0,3,5,Brunswick St
31,0,6,2,Exchange Place
32,5,10,4,Grove St PATH
33,6,1,16,Grove St PATH
34,1,8,6,Hamilton Park
35,3,1,17,Grove St PATH
36,2,8,17,Grove St PATH
37,4,6,10,Hamilton Park
38,1,2,18,Grove St PATH
39,6,10,3,Grove St PATH
40,6,2,19,Grove St PATH
41,0,6,18,Grove St PATH
42,2,6,10,Hamilton Park
43,1,10,2,City Hall
44,4,1,21,Grove St PATH
45,1,1,22,Grove St PATH
46,0,9,0,Grove St PATH
47,4,8,12,Grove St PATH
48,0,7,8,Hamilton Park
49,3,0,1,Sip Ave
50,3,11,14,Grove St PATH
51,3,10,2,City Hall
52,3,5,7,Hamilton Park
53,6,9,17,Grove St PATH
54,3,8,13,Grove St PATH
55,1,0,15,Grove St PATH
56,1,10,19,Grove St PATH
57,0,1,4,Hamilton Park
58,5,1,16,Grove St PATH
59,5,10,19,Grove St PATH
60,2,2,0,Sip Ave
61,6,5,2,Grove St PATH
62,5,8,23,Grove St PATH
63,0,0,18,Grove St PATH
64,5,5,2,Grove St PATH
65,1,1,6,Hamilton Park
66,4,4,21,Grove St PATH
67,3,10,15,Grove St PATH
68,3,6,20,Grove St PATH
69,1,0,2,Grove St PATH
70,0,10,20,Grove St PATH
71,6,1,12,Grove St PATH
72,6,5,8,Essex Light Rail
73,4,4,3,Sip Ave
74,0,10,1,Exchange Place
75,5,0,14,Grove St PATH
76,3,11,0,City Hall
77,4,11,3,Newark Ave
78,0,2,0,Grove St PATH
79,3,3,5,Brunswick St
80,2,10,2,City Hall
81,4,1,13,Grove St PATH
82,6,8,0,Grove St PATH
83,3,1,8,Hamilton Park
84,3,7,12,Grove St PATH
85,5,4,12,Grove St PATH
86,5,10,22,Grove St PATH
87,3,1,1,Sip Ave
88,3,5,2,Sip Ave
89,4,8,16,Grove St PATH
90,6,1,23,Grove St PATH
91,2,8,9,Hamilton Park
92,5,9,3,Grove St PATH
93,0,9,5,Oakland Ave
94,3,2,14,Grove St PATH
95,4,11,2,City Hall
96,1,9,14,Grove St PATH
97,1,4,3,Bayside Park
98,5,2,3,Grove St PATH
99,1,4,16,Grove St PATH
100,0,8,0,Grove St PATH
101,1,3,2,Exchange Place
102,5,9,18,Grove St PATH
103,3,6,15,Grove St PATH
104,1,9,9,Hamilton Park
105,2,7,18,Grove St PATH
106,3,2,16,Grove St PATH
107,0,2,6,Hamilton Park
108,3,5,5,Oakland Ave
109,6,10,5,Grove St PATH
110,6,11,17,Grove St PATH
111,3,5,17,Grove St PATH
112,3,5,9,Hamilton Park
113,4,0,7,Hamilton Park
114,2,4,18,Grove St PATH
115,0,7,15,Grove St PATH
116,0,3,9,Hamilton Park
117,6,5,21,Grove St PATH
118,5,3,11,Hamilton Park
119,6,6,11,Hamilton Park
120,5,4,16,Grove St PATH
121,4,7,19,Grove St PATH
122,6,7,16,Grove St PATH
123,5,8,6,Oakland Ave
124,0,1,14,Grove St PATH
125,4,7,4,Brunswick St
126,0,4,12,Grove St PATH
127,2,1,18,Grove St PATH
128,5,10,14,Grove St PATH
129,2,5,0,Sip Ave
130,0,5,16,Grove St PATH
131,0,10,21,Grove St PATH
132,0,0,10,Hamilton Park
133,4,3,9,Hamilton Park
134,0,3,6,Hamilton Park
135,3,0,2,Sip Ave
136,0,0,7,Hamilton Park
137,4,11,13,Grove St PATH
138,5,0,23,Grove St PATH
139,3,5,3,Bayside Park
140,4,1,9,Hamilton Park
141,1,4,22,Grove St PATH
142,0,6,13,Grove St PATH
143,1,11,6,Oakland Ave
144,2,7,4,Oakland Ave
145,2,3,21,Grove St PATH
146,1,7,20,Grove St PATH
147,5,8,16,Grove St PATH
148,5,7,3,Grove St PATH
149,5,7,1,Sip Ave
150,0,10,23,Grove St PATH
151,6,6,9,Grove St PATH
152,2,9,6,Hamilton Park
153,1,10,11,Exchange Place
154,2,11,2,City Hall
155,0,3,14,Grove St PATH
156,4,5,12,Grove St PATH
157,5,6,6,Oakland Ave
158,0,4,23,Grove St PATH
159,3,0,19,Grove St PATH
160,1,4,2,Exchange Place
161,6,7,4,Grove St PATH
162,5,8,2,Grove St PATH
163,6,9,19,Grove St PATH
164,1,4,19,Grove St PATH
165,0,1,7,Hamilton Park
166,2,3,13,Grove St PATH
167,2,9,8,Hamilton Park
168,0,11,1,Grove St PATH
169,0,10,10,Hamilton Park
170,4,9,13,Grove St PATH
171,3,10,9,Hamilton Park
172,2,10,5,Oakland Ave
173,5,7,17,Grove St PATH
174,1,4,7,Hamilton Park
175,0,10,13,Grove St PATH
176,6,8,14,Grove St PATH
177,0,11,16,Grove St PATH
178,4,0,22,Grove St PATH
179,6,6,1,Grove St PATH
180,0,10,15,Grove St PATH
181,4,5,6,Hamilton Park
182,4,0,3,Sip Ave
183,2,4,14,Grove St PATH
184,5,5,12,Grove St PATH
185,0,5,5,Brunswick St
186,0,8,9,Hamilton Park
187,1,4,1,Grove St PATH
188,1,7,4,Oakland Ave
189,3,10,0,City Hall
190,3,2,6,Hamilton Park
191,6,3,1,Grove St PATH
192,3,5,12,Grove St PATH
193,6,2,22,Grove St PATH
194,6,7,6,Grove St PATH
195,1,8,22,Grove St PATH
196,3,9,3,Bayside Park
197,5,8,1,Sip Ave
198,3,7,18,Grove St PATH
199,2,4,6,Hamilton Park
200,1,9,19,Grove St PATH
201,4,11,12,Exchange Place
202,1,9,16,Grove St PATH
203,1,6,15,Grove St PATH
204,2,8,11,Grove St PATH
205,6,4,5,Grove St PATH
206,1,11,13,Grove St PATH
207,2,6,15,Grove St PATH
208,3,6,22,Grove St PATH
209,3,3,4,Brunswick St
210,2,8,12,Grove St PATH
211,5,7,8,Hamilton Park
212,1,9,23,Grove St PATH
213,3,11,17,Grove St PATH
214,2,3,20,Grove St PATH
215,4,10,1,City Hall
216,6,4,20,Grove St PATH
217,1,2,7,Hamilton Park
218,5,6,3,Sip Ave
219,6,8,23,Grove St PATH
220,1,0,7,Hamilton Park
221,2,6,7,Hamilton Park
222,6,8,1,Grove St PATH
223,2,0,13,Grove St PATH
224,2,1,22,Grove St PATH
225,3,1,4,Oakland Ave
226,4,4,15,Grove St PATH
227,0,9,18,Grove St PATH
228,6,3,9,Hamilton Park
229,3,7,4,Oakland Ave
230,4,4,2,Sip Ave
231,5,4,11,Hamilton Park
232,5,7,13,Grove St PATH
233,1,0,16,Grove St PATH
234,3,4,0,Sip Ave
235,6,0,18,Grove St PATH
236,4,9,11,Hamilton Park
237,6,8,22,Grove St PATH
238,4,11,15,Grove St PATH
239,6,4,8,Brunswick St
240,4,2,15,Grove St PATH
241,6,3,12,Grove St PATH
242,4,3,4,Brunswick St
243,6,5,1,Grove St PATH
244,4,6,11,Hamilton Park
245,0,1,11,Hamilton Park
246,1,9,13,Grove St PATH
247,4,2,1,Sip Ave
248,4,7,21,Grove St PATH
249,1,5,20,Grove St PATH
250,5,6,23,Grove St PATH
251,3,8,0,City Hall
252,6,1,18,Grove St PATH
253,3,11,4,McGinley Square
254,1,3,17,Grove St PATH
255,1,9,15,Grove St PATH
256,1,7,10,Hamilton Park
257,2,1,21,Grove St PATH
258,5,0,19,Grove St PATH
259,3,5,22,Grove St PATH
260,5,6,9,Hamilton Park
261,5,1,14,Grove St PATH
262,0,11,13,Grove St PATH
263,3,11,12,Exchange Place
264,0,4,22,Grove St PATH
265,0,2,14,Grove St PATH
266,1,10,9,Hamilton Park
267,6,0,22,Grove St PATH
268,4,9,1,City Hall
269,0,9,12,Grove St PATH
270,6,4,13,Grove St PATH
271,0,0,15,Grove St PATH
272,1,7,2,City Hall
273,5,3,15,Grove St PATH
274,1,9,8,Hamilton Park
275,2,2,8,Hamilton Park
276,4,10,4,McGinley Square
277,2,8,4,Oakland Ave
278,1,0,23,Grove St PATH
279,6,2,23,Grove St PATH
280,2,0,8,Hamilton Park
281,2,7,19,Grove St PATH
282,1,8,8,Hamilton Park
283,1,4,10,Hamilton Park
284,1,11,2,Exchange Place
285,3,5,4,Brunswick St
286,5,3,23,Grove St PATH
287,0,9,8,Hamilton Park
288,5,10,10,Hamilton Park
289,1,6,9,Hamilton Park
290,5,2,17,Grove St PATH
291,3,1,10,Hamilton Park
292,3,3,11,Hamilton Park
293,0,6,9,Hamilton Park
294,2,9,22,Grove St PATH
295,6,4,18,Grove St PATH
296,5,5,16,Grove St PATH
297,2,4,20,Grove St PATH
298,6,7,15,Grove St PATH
299,0,2,8,Hamilton Park
300,0,5,22,Grove St PATH
301,5,9,1,Grove St PATH
302,3,9,8,Hamilton Park
303,0,8,11,Grove St PATH
304,5,3,8,Hamilton Park
305,1,6,21,Grove St PATH
306,2,4,19,Grove St PATH
307,5,4,14,Grove St PATH
308,2,2,11,Hamilton Park
309,2,1,19,Grove St PATH
310,5,1,0,Grove St PATH
311,1,9,0,Grove St PATH
312,4,5,18,Grove St PATH
313,3,8,1,City Hall
314,5,1,5,Oakland Ave
315,2,3,22,Grove St PATH
316,3,8,5,Oakland Ave
317,0,9,2,Exchange Place
318,5,8,4,Grove St PATH
319,5,11,22,Grove St PATH
320,0,1,16,Grove St PATH
321,3,8,22,Grove St PATH
322,0,2,19,Grove St PATH
323,3,7,20,Grove St PATH
324,1,6,12,Grove St PATH
325,3,5,16,Grove St PATH
326,0,8,15,Grove St PATH
327,4,5,19,Grove St PATH
328,4,4,16,Grove St PATH
329,5,11,4,Grove St PATH
330,4,8,18,Grove St PATH
331,1,10,3,Bayside Park
332,0,0,6,Hamilton Park
333,0,2,11,Hamilton Park
334,2,2,13,Grove St PATH
335,6,6,6,Grove St PATH
336,5,2,6,Oakland Ave
337,4,9,17,Grove St PATH
338,4,1,14,Grove St PATH
339,0,3,21,Grove St PATH
340,5,0,4,Sip Ave
341,2,7,0,Grove St PATH
342,1,5,18,Grove St PATH
343,3,1,22,Grove St PATH
344,3,5,18,Grove St PATH
345,0,11,15,Grove St PATH
346,4,7,11,Grove St PATH
347,0,5,14,Grove St PATH
348,3,7,17,Grove St PATH
349,0,6,1,Grove St PATH
350,4,6,4,Brunswick St
351,5,2,8,Hamilton Park
352,1,11,17,Grove St PATH
353,3,3,9,Hamilton Park
354,3,4,8,Hamilton Park
355,4,7,15,Grove St PATH
356,2,5,16,Grove St PATH
357,0,0,20,Grove St PATH
358,6,11,3,Grove St PATH
359,4,6,19,Grove St PATH
360,0,2,7,Hamilton Park
361,6,0,16,Grove St PATH
362,4,9,5,Oakland Ave
363,3,11,13,Grove St PATH
364,3,4,2,Sip Ave
365,5,11,16,Grove St PATH
366,4,5,21,Grove St PATH
367,2,8,5,Oakland Ave
368,0,0,1,Grove St PATH
369,1,7,6,Hamilton Park
370,3,5,23,Grove St PATH
371,0,4,18,Grove St PATH
372,3,1,0,Sip Ave
373,3,8,6,Hamilton Park
374,2,6,17,Grove St PATH
375,1,3,20,Grove St PATH
376,0,3,11,Hamilton Park
377,6,10,20,Grove St PATH
378,4,2,23,Grove St PATH
379,5,0,18,Grove St PATH
380,1,1,15,Grove St PATH
381,4,0,21,Grove St PATH
382,1,1,11,Hamilton Park
383,2,6,0,Grove St PATH
384,4,8,23,Grove St PATH
385,5,6,8,Hamilton Park
386,4,6,20,Grove St PATH
387,1,6,22,Grove St PATH
388,3,9,22,Grove St PATH
389,0,5,3,Bayside Park
390,5,3,2,Grove St PATH
391,3,2,9,Hamilton Park
392,0,2,1,Grove St PATH
393,4,2,8,Hamilton Park
394,2,4,21,Grove St PATH
395,5,1,17,Grove St PATH
396,3,10,17,Grove St PATH
397,3,9,9,Hamilton Park
398,2,9,5,Oakland Ave
399,4,3,16,Grove St PATH
400,1,0,11,Hamilton Park
401,0,2,5,Hamilton Park
402,5,7,9,Hamilton Park
\" target=\"_blank\">Download CSV file</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "import base64\n",
    "\n",
    "def create_download_link( df, title = \"Download CSV file\", filename = \"data_ytest.csv\"):  \n",
    "    csv = df.to_csv()\n",
    "    b64 = base64.b64encode(csv.encode())\n",
    "    payload = b64.decode()\n",
    "    html = '<a download=\"{filename}\" href=\"data:text/csv;base64,{payload}\" target=\"_blank\">{title}</a>'\n",
    "    html = html.format(payload=payload,title=title,filename=filename)\n",
    "    return HTML(html)\n",
    "\n",
    "create_download_link(df_ypred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a download=\"data_ytest.csv\" href=\"data:text/csv;base64,,d_week,m_yr,ToD,y_test
0,0,9,14,Grove St PATH
1,4,3,1,Exchange Place
2,6,3,23,Sip Ave
3,1,6,5,Brunswick St
4,2,10,13,Newport PATH
5,1,11,7,Hamilton Park
6,3,7,22,Grove St PATH
7,4,2,18,Grove St PATH
8,0,6,5,Garfield Ave Station
9,1,8,10,McGinley Square
10,6,11,5,Oakland Ave
11,3,7,23,Grove St PATH
12,5,5,11,Newport Pkwy
13,2,10,17,Grove St PATH
14,6,10,1,Grove St PATH
15,3,3,12,Grove St PATH
16,3,0,22,Grove St PATH
17,3,1,16,Exchange Place
18,6,0,5,Exchange Place
19,6,10,2,Grove St PATH
20,1,9,3,Bayside Park
21,5,2,1,Exchange Place
22,4,8,2,City Hall
23,5,9,4,Grove St PATH
24,3,6,10,Warren St
25,0,7,20,Grove St PATH
26,6,0,2,Grove St PATH
27,4,0,17,Grove St PATH
28,0,0,19,Grove St PATH
29,6,1,2,Grove St PATH
30,0,3,5,Oakland Ave
31,0,6,2,Exchange Place
32,5,10,4,Grove St PATH
33,6,1,16,Grove St PATH
34,1,8,6,Morris Canal
35,3,1,17,Grove St PATH
36,2,8,17,Grove St PATH
37,4,6,10,Hamilton Park
38,1,2,18,Grove St PATH
39,6,10,3,Grove St PATH
40,6,2,19,Grove St PATH
41,0,6,18,Grove St PATH
42,2,6,10,Newport PATH
43,1,10,2,City Hall
44,4,1,21,Grove St PATH
45,1,1,22,Sip Ave
46,0,9,0,Sip Ave
47,4,8,12,Grove St PATH
48,0,7,8,Hamilton Park
49,3,0,1,Sip Ave
50,3,11,14,Grove St PATH
51,3,10,2,Grove St PATH
52,3,5,7,Hamilton Park
53,6,9,17,Grove St PATH
54,3,8,13,Newport PATH
55,1,0,15,Grove St PATH
56,1,10,19,Grove St PATH
57,0,1,4,Grove St PATH
58,5,1,16,Grove St PATH
59,5,10,19,Grove St PATH
60,2,2,0,Sip Ave
61,6,5,2,City Hall
62,5,8,23,Grove St PATH
63,0,0,18,Grove St PATH
64,5,5,2,Van Vorst Park
65,1,1,6,Essex Light Rail
66,4,4,21,Grove St PATH
67,3,10,15,Exchange Place
68,3,6,20,Grove St PATH
69,1,0,2,City Hall
70,0,10,20,Grove St PATH
71,6,1,12,Warren St
72,6,5,8,Newark Ave
73,4,4,3,City Hall
74,0,10,1,Grove St PATH
75,5,0,14,Grove St PATH
76,3,11,0,Newark Ave
77,4,11,3,Sip Ave
78,0,2,0,Newport PATH
79,3,3,5,Liberty Light Rail
80,2,10,2,Van Vorst Park
81,4,1,13,Exchange Place
82,6,8,0,Grove St PATH
83,3,1,8,Hamilton Park
84,3,7,12,Exchange Place
85,5,4,12,Grove St PATH
86,5,10,22,Grove St PATH
87,3,1,1,Exchange Place
88,3,5,2,Exchange Place
89,4,8,16,Grove St PATH
90,6,1,23,Grove St PATH
91,2,8,9,McGinley Square
92,5,9,3,City Hall
93,0,9,5,Oakland Ave
94,3,2,14,Exchange Place
95,4,11,2,McGinley Square
96,1,9,14,Grove St PATH
97,1,4,3,Newport Pkwy
98,5,2,3,City Hall
99,1,4,16,Grove St PATH
100,0,8,0,Grove St PATH
101,1,3,2,Hilltop
102,5,9,18,Grove St PATH
103,3,6,15,Grove St PATH
104,1,9,9,Hamilton Park
105,2,7,18,Grove St PATH
106,3,2,16,Grove St PATH
107,0,2,6,Hamilton Park
108,3,5,5,Oakland Ave
109,6,10,5,Oakland Ave
110,6,11,17,Grove St PATH
111,3,5,17,Exchange Place
112,3,5,9,Van Vorst Park
113,4,0,7,Hamilton Park
114,2,4,18,Sip Ave
115,0,7,15,Grove St PATH
116,0,3,9,Brunswick St
117,6,5,21,Grove St PATH
118,5,3,11,Paulus Hook
119,6,6,11,Newport PATH
120,5,4,16,Exchange Place
121,4,7,19,Grove St PATH
122,6,7,16,Grove St PATH
123,5,8,6,Baldwin at Montgomery
124,0,1,14,Grove St PATH
125,4,7,4,Brunswick St
126,0,4,12,Grove St PATH
127,2,1,18,Grove St PATH
128,5,10,14,Hamilton Park
129,2,5,0,Sip Ave
130,0,5,16,Exchange Place
131,0,10,21,Grove St PATH
132,0,0,10,Hamilton Park
133,4,3,9,Brunswick St
134,0,3,6,Essex Light Rail
135,3,0,2,McGinley Square
136,0,0,7,Hamilton Park
137,4,11,13,Exchange Place
138,5,0,23,Grove St PATH
139,3,5,3,Newark Ave
140,4,1,9,Brunswick St
141,1,4,22,Grove St PATH
142,0,6,13,Paulus Hook
143,1,11,6,Hamilton Park
144,2,7,4,Van Vorst Park
145,2,3,21,Grove St PATH
146,1,7,20,Grove St PATH
147,5,8,16,Grove St PATH
148,5,7,3,Exchange Place
149,5,7,1,Exchange Place
150,0,10,23,Sip Ave
151,6,6,9,Newport Pkwy
152,2,9,6,Brunswick St
153,1,10,11,Hamilton Park
154,2,11,2,McGinley Square
155,0,3,14,Grove St PATH
156,4,5,12,Grove St PATH
157,5,6,6,Newport Pkwy
158,0,4,23,Grove St PATH
159,3,0,19,Grove St PATH
160,1,4,2,Sip Ave
161,6,7,4,Grove St PATH
162,5,8,2,Grove St PATH
163,6,9,19,Grove St PATH
164,1,4,19,Grove St PATH
165,0,1,7,Hamilton Park
166,2,3,13,Newport Pkwy
167,2,9,8,Hamilton Park
168,0,11,1,Grove St PATH
169,0,10,10,Hamilton Park
170,4,9,13,Grove St PATH
171,3,10,9,Hamilton Park
172,2,10,5,Oakland Ave
173,5,7,17,Grove St PATH
174,1,4,7,Hamilton Park
175,0,10,13,Newark Ave
176,6,8,14,Grove St PATH
177,0,11,16,Grove St PATH
178,4,0,22,Grove St PATH
179,6,6,1,Grove St PATH
180,0,10,15,Grove St PATH
181,4,5,6,Essex Light Rail
182,4,0,3,Newport PATH
183,2,4,14,Sip Ave
184,5,5,12,Grove St PATH
185,0,5,5,Lincoln Park
186,0,8,9,Hamilton Park
187,1,4,1,Sip Ave
188,1,7,4,Brunswick St
189,3,10,0,Newark Ave
190,3,2,6,Dixon Mills
191,6,3,1,Newark Ave
192,3,5,12,Exchange Place
193,6,2,22,Grove St PATH
194,6,7,6,Liberty Light Rail
195,1,8,22,Sip Ave
196,3,9,3,Warren St
197,5,8,1,City Hall
198,3,7,18,Grove St PATH
199,2,4,6,Essex Light Rail
200,1,9,19,Grove St PATH
201,4,11,12,Grove St PATH
202,1,9,16,Grove St PATH
203,1,6,15,Paulus Hook
204,2,8,11,Exchange Place
205,6,4,5,Exchange Place
206,1,11,13,Exchange Place
207,2,6,15,Grove St PATH
208,3,6,22,Sip Ave
209,3,3,4,Brunswick St
210,2,8,12,Newport PATH
211,5,7,8,Grove St PATH
212,1,9,23,Sip Ave
213,3,11,17,Grove St PATH
214,2,3,20,Grove St PATH
215,4,10,1,Newark Ave
216,6,4,20,Grove St PATH
217,1,2,7,Hamilton Park
218,5,6,3,Bayside Park
219,6,8,23,Grove St PATH
220,1,0,7,Hamilton Park
221,2,6,7,Hamilton Park
222,6,8,1,Grove St PATH
223,2,0,13,Grove St PATH
224,2,1,22,City Hall
225,3,1,4,North St
226,4,4,15,Exchange Place
227,0,9,18,Grove St PATH
228,6,3,9,Newport Pkwy
229,3,7,4,Brunswick St
230,4,4,2,City Hall
231,5,4,11,Essex Light Rail
232,5,7,13,City Hall
233,1,0,16,Grove St PATH
234,3,4,0,Sip Ave
235,6,0,18,Grove St PATH
236,4,9,11,Hamilton Park
237,6,8,22,Grove St PATH
238,4,11,15,Exchange Place
239,6,4,8,Hamilton Park
240,4,2,15,Exchange Place
241,6,3,12,Grove St PATH
242,4,3,4,Brunswick St
243,6,5,1,Grove St PATH
244,4,6,11,Brunswick St
245,0,1,11,Hamilton Park
246,1,9,13,Exchange Place
247,4,2,1,Sip Ave
248,4,7,21,Grove St PATH
249,1,5,20,Grove St PATH
250,5,6,23,Sip Ave
251,3,8,0,Newark Ave
252,6,1,18,Grove St PATH
253,3,11,4,Marin Light Rail
254,1,3,17,Exchange Place
255,1,9,15,Grove St PATH
256,1,7,10,Hamilton Park
257,2,1,21,Grove St PATH
258,5,0,19,Grove St PATH
259,3,5,22,Exchange Place
260,5,6,9,Essex Light Rail
261,5,1,14,Grove St PATH
262,0,11,13,Exchange Place
263,3,11,12,Exchange Place
264,0,4,22,Grove St PATH
265,0,2,14,Grove St PATH
266,1,10,9,Hamilton Park
267,6,0,22,Grove St PATH
268,4,9,1,Exchange Place
269,0,9,12,Exchange Place
270,6,4,13,Hamilton Park
271,0,0,15,Hamilton Park
272,1,7,2,Newport PATH
273,5,3,15,Exchange Place
274,1,9,8,Hamilton Park
275,2,2,8,Hamilton Park
276,4,10,4,Dixon Mills
277,2,8,4,Morris Canal
278,1,0,23,Exchange Place
279,6,2,23,Grove St PATH
280,2,0,8,Hamilton Park
281,2,7,19,Grove St PATH
282,1,8,8,Hamilton Park
283,1,4,10,Warren St
284,1,11,2,Jersey & 3rd
285,3,5,4,Brunswick St
286,5,3,23,Sip Ave
287,0,9,8,Hamilton Park
288,5,10,10,Warren St
289,1,6,9,Hamilton Park
290,5,2,17,Grove St PATH
291,3,1,10,Hamilton Park
292,3,3,11,Newark Ave
293,0,6,9,Van Vorst Park
294,2,9,22,Exchange Place
295,6,4,18,Grove St PATH
296,5,5,16,Hamilton Park
297,2,4,20,Grove St PATH
298,6,7,15,Newport Pkwy
299,0,2,8,Hamilton Park
300,0,5,22,Sip Ave
301,5,9,1,Newport PATH
302,3,9,8,Hamilton Park
303,0,8,11,Sip Ave
304,5,3,8,Hamilton Park
305,1,6,21,Grove St PATH
306,2,4,19,Grove St PATH
307,5,4,14,Newport PATH
308,2,2,11,Hamilton Park
309,2,1,19,Grove St PATH
310,5,1,0,Sip Ave
311,1,9,0,Sip Ave
312,4,5,18,Grove St PATH
313,3,8,1,City Hall
314,5,1,5,McGinley Square
315,2,3,22,Sip Ave
316,3,8,5,Morris Canal
317,0,9,2,Sip Ave
318,5,8,4,Sip Ave
319,5,11,22,Grove St PATH
320,0,1,16,Grove St PATH
321,3,8,22,Grove St PATH
322,0,2,19,Grove St PATH
323,3,7,20,Grove St PATH
324,1,6,12,Exchange Place
325,3,5,16,Exchange Place
326,0,8,15,Grove St PATH
327,4,5,19,Grove St PATH
328,4,4,16,Grove St PATH
329,5,11,4,McGinley Square
330,4,8,18,Grove St PATH
331,1,10,3,Bayside Park
332,0,0,6,Van Vorst Park
333,0,2,11,Van Vorst Park
334,2,2,13,Exchange Place
335,6,6,6,Newport Pkwy
336,5,2,6,Baldwin at Montgomery
337,4,9,17,Grove St PATH
338,4,1,14,City Hall
339,0,3,21,Grove St PATH
340,5,0,4,Newark Ave
341,2,7,0,McGinley Square
342,1,5,18,Grove St PATH
343,3,1,22,Sip Ave
344,3,5,18,Grove St PATH
345,0,11,15,Sip Ave
346,4,7,11,Van Vorst Park
347,0,5,14,Grove St PATH
348,3,7,17,Grove St PATH
349,0,6,1,Grove St PATH
350,4,6,4,Brunswick St
351,5,2,8,Hamilton Park
352,1,11,17,Grove St PATH
353,3,3,9,Brunswick St
354,3,4,8,Hamilton Park
355,4,7,15,Exchange Place
356,2,5,16,Sip Ave
357,0,0,20,Grove St PATH
358,6,11,3,Grove St PATH
359,4,6,19,Exchange Place
360,0,2,7,Hamilton Park
361,6,0,16,Grove St PATH
362,4,9,5,Oakland Ave
363,3,11,13,Newport PATH
364,3,4,2,Sip Ave
365,5,11,16,Grove St PATH
366,4,5,21,Grove St PATH
367,2,8,5,Morris Canal
368,0,0,1,Newark Ave
369,1,7,6,Hamilton Park
370,3,5,23,City Hall
371,0,4,18,Exchange Place
372,3,1,0,Sip Ave
373,3,8,6,Morris Canal
374,2,6,17,Exchange Place
375,1,3,20,Grove St PATH
376,0,3,11,Newport PATH
377,6,10,20,Grove St PATH
378,4,2,23,Newark Ave
379,5,0,18,Grove St PATH
380,1,1,15,Grove St PATH
381,4,0,21,Grove St PATH
382,1,1,11,Newark Ave
383,2,6,0,Sip Ave
384,4,8,23,Sip Ave
385,5,6,8,McGinley Square
386,4,6,20,Grove St PATH
387,1,6,22,Sip Ave
388,3,9,22,Sip Ave
389,0,5,3,Liberty Light Rail
390,5,3,2,Grove St PATH
391,3,2,9,Hamilton Park
392,0,2,1,Newark Ave
393,4,2,8,Hamilton Park
394,2,4,21,Sip Ave
395,5,1,17,Grove St PATH
396,3,10,17,Grove St PATH
397,3,9,9,Van Vorst Park
398,2,9,5,Oakland Ave
399,4,3,16,Exchange Place
400,1,0,11,Hamilton Park
401,0,2,5,Essex Light Rail
402,5,7,9,Warren St
\" target=\"_blank\">Download CSV file</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_download_link(df_ytest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy: 0.169 +/- 0.039\n"
     ]
    }
   ],
   "source": [
    "#Ensure model accuracy with cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(regressor, X_train, y_train, cv=5)\n",
    "print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))"
   ]
  }
 ],
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