VinitShah_Capstone_DataScience.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": 2,
   "metadata": {
    "collapsed": true
   },
   "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"
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "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",
    "#files = !ls *.csv #For Ipython only\n",
    "#df = concat([read_csv(f, header=None,low_memory=False) for f in files], keys=files) #header = None because some files don't have a header\n",
    "#****************************************************************************************\n",
    "\n",
    "#Let's see what we're working with\n",
    "#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('jan.csv')\n",
    "#df.columns = df2.columns\n",
    "#del[df2]\n",
    "#df = df[df[1] != 'starttime']#Could've just dropped duplicate rows here as well\n",
    "#df = df[df[1] != 'Start Time']\n",
    "#Saved csv to avoid running code above multiple times in the future\n",
    "#df.to_csv('2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195</td>\n",
       "      <td>2017-04-01 00:00:58</td>\n",
       "      <td>2017-04-01 00:04:14</td>\n",
       "      <td>217</td>\n",
       "      <td>Old Fulton St</td>\n",
       "      <td>40.702772</td>\n",
       "      <td>-73.993836</td>\n",
       "      <td>430</td>\n",
       "      <td>York St &amp; Jay St</td>\n",
       "      <td>40.701485</td>\n",
       "      <td>-73.986569</td>\n",
       "      <td>25454</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>585</td>\n",
       "      <td>2017-04-01 00:01:54</td>\n",
       "      <td>2017-04-01 00:11:40</td>\n",
       "      <td>490</td>\n",
       "      <td>8 Ave &amp; W 33 St</td>\n",
       "      <td>40.751551</td>\n",
       "      <td>-73.993934</td>\n",
       "      <td>3158</td>\n",
       "      <td>W 63 St &amp; Broadway</td>\n",
       "      <td>40.771639</td>\n",
       "      <td>-73.982614</td>\n",
       "      <td>25930</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>665</td>\n",
       "      <td>2017-04-01 00:01:56</td>\n",
       "      <td>2017-04-01 00:13:01</td>\n",
       "      <td>504</td>\n",
       "      <td>1 Ave &amp; E 16 St</td>\n",
       "      <td>40.732219</td>\n",
       "      <td>-73.981656</td>\n",
       "      <td>412</td>\n",
       "      <td>Forsyth St &amp; Canal St</td>\n",
       "      <td>40.715816</td>\n",
       "      <td>-73.994224</td>\n",
       "      <td>27110</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>262</td>\n",
       "      <td>2017-04-01 00:02:24</td>\n",
       "      <td>2017-04-01 00:06:47</td>\n",
       "      <td>445</td>\n",
       "      <td>E 10 St &amp; Avenue A</td>\n",
       "      <td>40.727408</td>\n",
       "      <td>-73.981420</td>\n",
       "      <td>411</td>\n",
       "      <td>E 6 St &amp; Avenue D</td>\n",
       "      <td>40.722281</td>\n",
       "      <td>-73.976687</td>\n",
       "      <td>28392</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>2017-04-01 00:02:36</td>\n",
       "      <td>2017-04-01 00:04:50</td>\n",
       "      <td>3423</td>\n",
       "      <td>West Drive &amp; Prospect Park West</td>\n",
       "      <td>40.661063</td>\n",
       "      <td>-73.979453</td>\n",
       "      <td>3300</td>\n",
       "      <td>Prospect Park West &amp; 8 St</td>\n",
       "      <td>40.665147</td>\n",
       "      <td>-73.976376</td>\n",
       "      <td>19316</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trip Duration           Start Time            Stop Time  Start Station ID  \\\n",
       "0            195  2017-04-01 00:00:58  2017-04-01 00:04:14               217   \n",
       "1            585  2017-04-01 00:01:54  2017-04-01 00:11:40               490   \n",
       "2            665  2017-04-01 00:01:56  2017-04-01 00:13:01               504   \n",
       "3            262  2017-04-01 00:02:24  2017-04-01 00:06:47               445   \n",
       "4            134  2017-04-01 00:02:36  2017-04-01 00:04:50              3423   \n",
       "\n",
       "                Start Station Name  Start Station Latitude  \\\n",
       "0                    Old Fulton St               40.702772   \n",
       "1                  8 Ave & W 33 St               40.751551   \n",
       "2                  1 Ave & E 16 St               40.732219   \n",
       "3               E 10 St & Avenue A               40.727408   \n",
       "4  West Drive & Prospect Park West               40.661063   \n",
       "\n",
       "   Start Station Longitude  End Station ID           End Station Name  \\\n",
       "0               -73.993836             430           York St & Jay St   \n",
       "1               -73.993934            3158         W 63 St & Broadway   \n",
       "2               -73.981656             412      Forsyth St & Canal St   \n",
       "3               -73.981420             411          E 6 St & Avenue D   \n",
       "4               -73.979453            3300  Prospect Park West & 8 St   \n",
       "\n",
       "   End Station Latitude  End Station Longitude  Bike ID   User Type  \\\n",
       "0             40.701485             -73.986569    25454  Subscriber   \n",
       "1             40.771639             -73.982614    25930  Subscriber   \n",
       "2             40.715816             -73.994224    27110  Subscriber   \n",
       "3             40.722281             -73.976687    28392  Subscriber   \n",
       "4             40.665147             -73.976376    19316  Subscriber   \n",
       "\n",
       "   Birth Year  Gender  \n",
       "0      1966.0       1  \n",
       "1      1995.0       1  \n",
       "2      1987.0       1  \n",
       "3      1961.0       1  \n",
       "4      1967.0       2  "
      ]
     },
     "execution_count": 3,
     "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 = read_csv('2017.csv')\n",
    "df = df.drop(['Unnamed: 0', 'Unnamed: 1'], axis = 1)\n",
    "df_copy = df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Birth Year</th>\n",
       "      <td>1630335</td>\n",
       "      <td>9.962537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User Type</th>\n",
       "      <td>15909</td>\n",
       "      <td>0.097216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>End Station Longitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>End Station Latitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>End Station Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>End Station ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start Station Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start Station ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stop Time</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start Time</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trip Duration</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Total   Percent\n",
       "Birth Year               1630335  9.962537\n",
       "User Type                  15909  0.097216\n",
       "Gender                         0  0.000000\n",
       "Bike ID                        0  0.000000\n",
       "End Station Longitude          0  0.000000\n",
       "End Station Latitude           0  0.000000\n",
       "End Station Name               0  0.000000\n",
       "End Station ID                 0  0.000000\n",
       "Start Station Longitude        0  0.000000\n",
       "Start Station Latitude         0  0.000000\n",
       "Start Station Name             0  0.000000\n",
       "Start Station ID               0  0.000000\n",
       "Stop Time                      0  0.000000\n",
       "Start Time                     0  0.000000\n",
       "Trip Duration                  0  0.000000"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>16364657.00</td>\n",
       "      <td>14734322.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>994.59</td>\n",
       "      <td>1357.13</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>1349.14</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>23754.63</td>\n",
       "      <td>1978.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16178.92</td>\n",
       "      <td>1346.90</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1344.69</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "      <td>5345.62</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-74.03</td>\n",
       "      <td>72.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-74.09</td>\n",
       "      <td>14529.00</td>\n",
       "      <td>1858.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>372.00</td>\n",
       "      <td>361.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>359.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>18653.00</td>\n",
       "      <td>1971.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>621.00</td>\n",
       "      <td>487.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>486.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>25464.00</td>\n",
       "      <td>1982.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1076.00</td>\n",
       "      <td>3142.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>3141.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>28114.00</td>\n",
       "      <td>1988.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9735948.00</td>\n",
       "      <td>3654.00</td>\n",
       "      <td>45.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3654.00</td>\n",
       "      <td>45.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33481.00</td>\n",
       "      <td>2001.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count    16364657.00       16364657.00             16364657.00   \n",
       "mean          994.59           1357.13                   40.74   \n",
       "std         16178.92           1346.90                    0.07   \n",
       "min            61.00             72.00                    0.00   \n",
       "25%           372.00            361.00                   40.72   \n",
       "50%           621.00            487.00                   40.74   \n",
       "75%          1076.00           3142.00                   40.76   \n",
       "max       9735948.00           3654.00                   45.51   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count              16364657.00     16364657.00           16364657.00   \n",
       "mean                    -73.98         1349.14                 40.74   \n",
       "std                       0.12         1344.69                  0.05   \n",
       "min                     -74.03           72.00                  0.00   \n",
       "25%                     -74.00          359.00                 40.72   \n",
       "50%                     -73.99          486.00                 40.74   \n",
       "75%                     -73.98         3141.00                 40.76   \n",
       "max                       0.00         3654.00                 45.51   \n",
       "\n",
       "       End Station Longitude      Bike ID   Birth Year  \n",
       "count            16364657.00  16364657.00  14734322.00  \n",
       "mean                  -73.98     23754.63      1978.90  \n",
       "std                     0.08      5345.62        11.83  \n",
       "min                   -74.09     14529.00      1858.00  \n",
       "25%                   -74.00     18653.00      1971.00  \n",
       "50%                   -73.99     25464.00      1982.00  \n",
       "75%                   -73.98     28114.00      1988.00  \n",
       "max                     0.00     33481.00      2001.00  "
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "collapsed": true
   },
   "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": 7,
   "metadata": {
    "collapsed": true
   },
   "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": 8,
   "metadata": {
    "collapsed": true
   },
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0atiEA9t15LWvPs3asWQacEokNQFeIUycNp+y6Zydc+W4//30X/R/fuuTjdLm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\npVKpVAaE/w9aq2aSTgIUBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a34dd2048>"
      ]
     },
     "metadata": {},
     "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": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del(top5)\n",
    "#Drop NA Usertype\n",
    "df = df.dropna(subset=['User Type']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wMwcAH6CYwUSSJElSAxoZ071nZl7dupKZfwTeUV1JkiRJUnNpJHTPKj8UZ3hE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\nGAocZftZSXMpm8tHUAaInQsMtH1NYwF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1751de48>"
      ]
     },
     "metadata": {},
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinit.shah1ibm.com/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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bahgJAAAAUEtCW41k/vz5vmTJkrAuDxyzQ4cOqaWlJewyAKAmcY9FtXniiSd+4+6/Vapd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\nhpKBqX6lXwV27mKjodxf8fskANSkoO6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1751d2b0>"
      ]
     },
     "metadata": {},
     "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": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Remove anomalies based on definition above\n",
    "df = df.drop(df.index[(df['Trip Duration'] > 7200)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinit.shah1ibm.com/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7WtrqX5Fk6fQdliRJkjR3TGak/E7gb6tqT2A/4MgkDwOOAs6pqj2Ac9oywEHA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\nvFGSfYFbNqAPxzEYLV8CfGS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f052e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5f0c88>"
      ]
     },
     "metadata": {},
     "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": 16,
   "metadata": {
    "collapsed": true
   },
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinit.shah1ibm.com/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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k27Zt6+3bt6+mFNhQnr8A68t5lkWx4k87qaqTM3sj5sP7a68LXZLk2Llut0py6crLAwAO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\nQf4fOc7XnY2oQSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1767a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y86ZpTiuiu1esLwDg+sUINnuN7t7pr6OeceYu7QcA7L0EbAAAGEjABgCAgQRsAAAYSMAG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113dfe080>"
      ]
     },
     "metadata": {},
     "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": 18,
   "metadata": {
    "collapsed": true
   },
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6yiR22HADtuqzLufcN6LFED31tbmML4d6vDxjFmuvskql+rdcf1222aAPe2w2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\nqsgQLUmSJFVkiJYkSZIq+v8VJZonewKDMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113df1160>"
      ]
     },
     "metadata": {},
     "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": 20,
   "metadata": {
    "collapsed": true
   },
   "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": 21,
   "metadata": {
    "collapsed": true
   },
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fue6MiJhQps+fA7cC/YC9c/uNJQ0o6K+9pKH5dRdJz0gaJKmvpNZF+94u93NJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5c1710>"
      ]
     },
     "metadata": {},
     "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": 23,
   "metadata": {
    "collapsed": true
   },
   "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",
    "df = df.drop(df.index[(df['Birth Year'] < 1956)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "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": 25,
   "metadata": {
    "collapsed": true
   },
   "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",
    "'''dist = []\n",
    "for i in range(len(df)):\n",
    "    dist.append(geopy.distance.vincenty(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",
    "dist = pd.read_csv('Distance.csv', header = None)\n",
    "dist = dist.drop([0], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "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": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Distance'] = dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "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": 29,
   "metadata": {
    "collapsed": true
   },
   "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": 30,
   "metadata": {
    "collapsed": true
   },
   "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": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(df.index[(df['Distance'] == 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "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": 33,
   "metadata": {
    "collapsed": true
   },
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>count</th>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "      <td>15432349.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>802.73</td>\n",
       "      <td>1351.79</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>1343.92</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>23770.68</td>\n",
       "      <td>1980.18</td>\n",
       "      <td>13.38</td>\n",
       "      <td>1.17</td>\n",
       "      <td>37.82</td>\n",
       "      <td>13.85</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>632.57</td>\n",
       "      <td>1345.08</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1342.96</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5345.42</td>\n",
       "      <td>10.07</td>\n",
       "      <td>10.54</td>\n",
       "      <td>9.54</td>\n",
       "      <td>10.07</td>\n",
       "      <td>664.18</td>\n",
       "      <td>158.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-74.03</td>\n",
       "      <td>72.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-74.07</td>\n",
       "      <td>14529.00</td>\n",
       "      <td>1956.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>371.00</td>\n",
       "      <td>361.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>359.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>18667.00</td>\n",
       "      <td>1973.00</td>\n",
       "      <td>6.18</td>\n",
       "      <td>0.55</td>\n",
       "      <td>30.00</td>\n",
       "      <td>8.70</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>615.00</td>\n",
       "      <td>487.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>486.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>25481.00</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>10.25</td>\n",
       "      <td>0.90</td>\n",
       "      <td>36.00</td>\n",
       "      <td>10.47</td>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1058.00</td>\n",
       "      <td>3141.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>3140.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>28124.00</td>\n",
       "      <td>1988.00</td>\n",
       "      <td>17.63</td>\n",
       "      <td>1.48</td>\n",
       "      <td>45.00</td>\n",
       "      <td>13.17</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7200.00</td>\n",
       "      <td>3654.00</td>\n",
       "      <td>40.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3654.00</td>\n",
       "      <td>40.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33481.00</td>\n",
       "      <td>2001.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>5389.14</td>\n",
       "      <td>62.00</td>\n",
       "      <td>2608057.83</td>\n",
       "      <td>255187.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count    15432349.00       15432349.00             15432349.00   \n",
       "mean          802.73           1351.79                   40.74   \n",
       "std           632.57           1345.08                    0.07   \n",
       "min            61.00             72.00                    0.00   \n",
       "25%           371.00            361.00                   40.72   \n",
       "50%           615.00            487.00                   40.74   \n",
       "75%          1058.00           3141.00                   40.76   \n",
       "max          7200.00           3654.00                   40.81   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count              15432349.00     15432349.00           15432349.00   \n",
       "mean                    -73.98         1343.92                 40.74   \n",
       "std                       0.11         1342.96                  0.05   \n",
       "min                     -74.03           72.00                  0.00   \n",
       "25%                     -74.00          359.00                 40.72   \n",
       "50%                     -73.99          486.00                 40.74   \n",
       "75%                     -73.98         3140.00                 40.76   \n",
       "max                       0.00         3654.00                 40.81   \n",
       "\n",
       "       End Station Longitude      Bike ID   Birth Year      Minutes  \\\n",
       "count            15432349.00  15432349.00  15432349.00  15432349.00   \n",
       "mean                  -73.98     23770.68      1980.18        13.38   \n",
       "std                     0.07      5345.42        10.07        10.54   \n",
       "min                   -74.07     14529.00      1956.00         1.02   \n",
       "25%                   -74.00     18667.00      1973.00         6.18   \n",
       "50%                   -73.99     25481.00      1982.00        10.25   \n",
       "75%                   -73.98     28124.00      1988.00        17.63   \n",
       "max                     0.00     33481.00      2001.00       120.00   \n",
       "\n",
       "          Distance          Age     min_mile    mile_hour  \n",
       "count  15432349.00  15432349.00  15432349.00  15432349.00  \n",
       "mean          1.17        37.82        13.85         5.89  \n",
       "std           9.54        10.07       664.18       158.59  \n",
       "min           0.00        17.00         0.00         0.00  \n",
       "25%           0.55        30.00         8.70         4.55  \n",
       "50%           0.90        36.00        10.47         5.73  \n",
       "75%           1.48        45.00        13.17         6.90  \n",
       "max        5389.14        62.00   2608057.83    255187.93  "
      ]
     },
     "execution_count": 34,
     "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": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[df['Distance'] < 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "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": 37,
   "metadata": {
    "collapsed": true
   },
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+fFrkv1AhHc965Eho95s5U4v6X3ut1i0NV5Ei2ho7apT2+DVooKWm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\n79BA6w8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e0fb38>"
      ]
     },
     "metadata": {},
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9IYzbMI4/D/2Z6Dwlcpf4twe+3Qs/rmd+fr/8dzRepVT20rFjR1599VWWLVvG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\nZvqYoQ4dEh2JiEh6q1jRl8+cP9/Hdw4Z4sOdbrnFZ1LfcIMP0XrlFR+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m9rmZzTWz2WZ2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5d95c0>"
      ]
     },
     "metadata": {},
     "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Chg0SLZXqAB8/70oyVWr2rfOffHikhaqeXPg009lql3jWa5elfyw06ZJ3xs2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\n16snAVHO5PHs3FkUQWdcPhx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pbXP+znnCBI7aOcrm/i7LujDTgEwOGXiuXiULFiRLlJCA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1be09c7da0>"
      ]
     },
     "metadata": {},
     "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": 41,
   "metadata": {
    "collapsed": true
   },
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYyJiLnBuPcy/CDg9IuZK6gs8A3TN2X5hRIzIHRAR36p6LukbwIHpy3nA4RFRLqk1MF7S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdac33ef0>"
      ]
     },
     "metadata": {},
     "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": 43,
   "metadata": {
    "collapsed": true
   },
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AMUPRMVvpN7NMM5tvZgvMbK6ZtffLq5jZGH+7n5m9fpTjn2Nmc8xskf9v16C6KWa2wt//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\nzImIiIjIqKGRfhERERGRKKekX0REREQkyinpFxERERGJckr6RURERES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MPzqX/QQsJfw/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\nAExO6AcAgMl9A9n747t0UDpiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e1da90>"
      ]
     },
     "metadata": {},
     "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>count</th>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "      <td>14875380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>759.94</td>\n",
       "      <td>1343.85</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>1334.48</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>23786.77</td>\n",
       "      <td>1980.18</td>\n",
       "      <td>12.67</td>\n",
       "      <td>1.18</td>\n",
       "      <td>37.82</td>\n",
       "      <td>11.35</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>552.39</td>\n",
       "      <td>1342.31</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1339.77</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5344.67</td>\n",
       "      <td>10.10</td>\n",
       "      <td>9.21</td>\n",
       "      <td>0.87</td>\n",
       "      <td>10.10</td>\n",
       "      <td>4.18</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>40.65</td>\n",
       "      <td>-74.03</td>\n",
       "      <td>72.00</td>\n",
       "      <td>40.65</td>\n",
       "      <td>-74.07</td>\n",
       "      <td>14529.00</td>\n",
       "      <td>1956.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>17.00</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>364.00</td>\n",
       "      <td>361.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>359.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>18679.00</td>\n",
       "      <td>1973.00</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0.57</td>\n",
       "      <td>30.00</td>\n",
       "      <td>8.64</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>597.00</td>\n",
       "      <td>486.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>485.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>25493.00</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>9.95</td>\n",
       "      <td>0.92</td>\n",
       "      <td>36.00</td>\n",
       "      <td>10.33</td>\n",
       "      <td>5.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1008.00</td>\n",
       "      <td>3140.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>3137.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>28130.00</td>\n",
       "      <td>1988.00</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1.51</td>\n",
       "      <td>45.00</td>\n",
       "      <td>12.75</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7198.00</td>\n",
       "      <td>3654.00</td>\n",
       "      <td>40.81</td>\n",
       "      <td>-73.91</td>\n",
       "      <td>3654.00</td>\n",
       "      <td>40.81</td>\n",
       "      <td>-73.91</td>\n",
       "      <td>33481.00</td>\n",
       "      <td>2001.00</td>\n",
       "      <td>119.97</td>\n",
       "      <td>10.20</td>\n",
       "      <td>62.00</td>\n",
       "      <td>31.83</td>\n",
       "      <td>19.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Trip Duration  Start Station ID  Start Station Latitude  \\\n",
       "count    14875380.00       14875380.00             14875380.00   \n",
       "mean          759.94           1343.85                   40.74   \n",
       "std           552.39           1342.31                    0.03   \n",
       "min            61.00             72.00                   40.65   \n",
       "25%           364.00            361.00                   40.72   \n",
       "50%           597.00            486.00                   40.74   \n",
       "75%          1008.00           3140.00                   40.76   \n",
       "max          7198.00           3654.00                   40.81   \n",
       "\n",
       "       Start Station Longitude  End Station ID  End Station Latitude  \\\n",
       "count              14875380.00     14875380.00           14875380.00   \n",
       "mean                    -73.98         1334.48                 40.74   \n",
       "std                       0.02         1339.77                  0.03   \n",
       "min                     -74.03           72.00                 40.65   \n",
       "25%                     -74.00          359.00                 40.72   \n",
       "50%                     -73.99          485.00                 40.74   \n",
       "75%                     -73.98         3137.00                 40.76   \n",
       "max                     -73.91         3654.00                 40.81   \n",
       "\n",
       "       End Station Longitude      Bike ID   Birth Year      Minutes  \\\n",
       "count            14875380.00  14875380.00  14875380.00  14875380.00   \n",
       "mean                  -73.98     23786.77      1980.18        12.67   \n",
       "std                     0.02      5344.67        10.10         9.21   \n",
       "min                   -74.07     14529.00      1956.00         1.02   \n",
       "25%                   -74.00     18679.00      1973.00         6.07   \n",
       "50%                   -73.99     25493.00      1982.00         9.95   \n",
       "75%                   -73.98     28130.00      1988.00        16.80   \n",
       "max                   -73.91     33481.00      2001.00       119.97   \n",
       "\n",
       "          Distance          Age     min_mile    mile_hour  \n",
       "count  14875380.00  14875380.00  14875380.00  14875380.00  \n",
       "mean          1.18        37.82        11.35         5.85  \n",
       "std           0.87        10.10         4.18         1.71  \n",
       "min           0.04        17.00         3.01         1.89  \n",
       "25%           0.57        30.00         8.64         4.71  \n",
       "50%           0.92        36.00        10.33         5.81  \n",
       "75%           1.51        45.00        12.75         6.95  \n",
       "max          10.20        62.00        31.83        19.94  "
      ]
     },
     "execution_count": 45,
     "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": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Cleaning up anomalies\n",
    "df = df.drop(df.index[(df['Trip Duration'] > 2700)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "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": 48,
   "metadata": {
    "collapsed": true
   },
   "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": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_basemodel = df_basemodel.drop('Start Time', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Minutes                 1.000000\n",
       "Distance                0.848710\n",
       "User Type_Subscriber   -0.273082\n",
       "Gender_1               -0.178851\n",
       "Gender_2                0.046463\n",
       "Name: Minutes, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_basemodel.corr().loc[:,'Minutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinit.shah1ibm.com/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "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.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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7670642738319764"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vinit.shah1ibm.com/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Minutes</td>     <th>  R-squared:         </th>  <td>   0.767</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.767</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>9.754e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 01 May 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:51:52</td>     <th>  Log-Likelihood:    </th> <td>-3.3413e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1182108</td>     <th>  AIC:               </th>  <td>6.683e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1182103</td>     <th>  BIC:               </th>  <td>6.683e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</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>    8.4552</td> <td>    0.015</td> <td>  581.543</td> <td> 0.000</td> <td>    8.427</td> <td>    8.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    8.3021</td> <td>    0.004</td> <td> 1867.643</td> <td> 0.000</td> <td>    8.293</td> <td>    8.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>   -5.6523</td> <td>    0.022</td> <td> -251.293</td> <td> 0.000</td> <td>   -5.696</td> <td>   -5.608</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.9573</td> <td>    0.023</td> <td>  -41.966</td> <td> 0.000</td> <td>   -1.002</td> <td>   -0.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>    0.3342</td> <td>    0.023</td> <td>   14.231</td> <td> 0.000</td> <td>    0.288</td> <td>    0.380</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>382321.757</td> <th>  Durbin-Watson:     </th>  <td>   2.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>2150008.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 1.450</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td> 8.937</td>   <th>  Cond. No.          </th>  <td>    20.0</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Minutes   R-squared:                       0.767\n",
       "Model:                            OLS   Adj. R-squared:                  0.767\n",
       "Method:                 Least Squares   F-statistic:                 9.754e+05\n",
       "Date:                Tue, 01 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        15:51:52   Log-Likelihood:            -3.3413e+06\n",
       "No. Observations:             1182108   AIC:                         6.683e+06\n",
       "Df Residuals:                 1182103   BIC:                         6.683e+06\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                    8.4552      0.015    581.543      0.000       8.427       8.484\n",
       "Distance                 8.3021      0.004   1867.643      0.000       8.293       8.311\n",
       "User Type_Subscriber    -5.6523      0.022   -251.293      0.000      -5.696      -5.608\n",
       "Gender_1                -0.9573      0.023    -41.966      0.000      -1.002      -0.913\n",
       "Gender_2                 0.3342      0.023     14.231      0.000       0.288       0.380\n",
       "==============================================================================\n",
       "Omnibus:                   382321.757   Durbin-Watson:                   2.000\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2150008.334\n",
       "Skew:                           1.450   Prob(JB):                         0.00\n",
       "Kurtosis:                       8.937   Cond. No.                         20.0\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "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": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "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": 58,
   "metadata": {
    "collapsed": true
   },
   "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.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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77049040023570248"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Minutes</td>     <th>  R-squared:         </th>  <td>   0.771</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.771</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>3.615e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 01 May 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:51:59</td>     <th>  Log-Likelihood:    </th> <td>-3.3326e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1182108</td>     <th>  AIC:               </th>  <td>6.665e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1182096</td>     <th>  BIC:               </th>  <td>6.665e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</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>    7.9327</td> <td>    0.022</td> <td>  367.828</td> <td> 0.000</td> <td>    7.890</td> <td>    7.975</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    8.3151</td> <td>    0.004</td> <td> 1878.448</td> <td> 0.000</td> <td>    8.306</td> <td>    8.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>   -5.3294</td> <td>    0.023</td> <td> -236.737</td> <td> 0.000</td> <td>   -5.373</td> <td>   -5.285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.9262</td> <td>    0.023</td> <td>  -40.896</td> <td> 0.000</td> <td>   -0.971</td> <td>   -0.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>    0.3275</td> <td>    0.023</td> <td>   14.046</td> <td> 0.000</td> <td>    0.282</td> <td>    0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>   -0.4752</td> <td>    0.009</td> <td>  -51.924</td> <td> 0.000</td> <td>   -0.493</td> <td>   -0.457</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>    0.1775</td> <td>    0.013</td> <td>   14.118</td> <td> 0.000</td> <td>    0.153</td> <td>    0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>    0.3682</td> <td>    0.012</td> <td>   31.296</td> <td> 0.000</td> <td>    0.345</td> <td>    0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>    0.3119</td> <td>    0.012</td> <td>   26.523</td> <td> 0.000</td> <td>    0.289</td> <td>    0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.3916</td> <td>    0.015</td> <td>  -26.792</td> <td> 0.000</td> <td>   -0.420</td> <td>   -0.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>    0.6450</td> <td>    0.014</td> <td>   45.188</td> <td> 0.000</td> <td>    0.617</td> <td>    0.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>    0.5259</td> <td>    0.013</td> <td>   39.268</td> <td> 0.000</td> <td>    0.500</td> <td>    0.552</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>375558.102</td> <th>  Durbin-Watson:     </th>  <td>   2.001</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>2101009.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 1.423</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td> 8.879</td>   <th>  Cond. No.          </th>  <td>    22.6</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Minutes   R-squared:                       0.771\n",
       "Model:                            OLS   Adj. R-squared:                  0.771\n",
       "Method:                 Least Squares   F-statistic:                 3.615e+05\n",
       "Date:                Tue, 01 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        15:51:59   Log-Likelihood:            -3.3326e+06\n",
       "No. Observations:             1182108   AIC:                         6.665e+06\n",
       "Df Residuals:                 1182096   BIC:                         6.665e+06\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                    7.9327      0.022    367.828      0.000       7.890       7.975\n",
       "Distance                 8.3151      0.004   1878.448      0.000       8.306       8.324\n",
       "User Type_Subscriber    -5.3294      0.023   -236.737      0.000      -5.373      -5.285\n",
       "Gender_1                -0.9262      0.023    -40.896      0.000      -0.971      -0.882\n",
       "Gender_2                 0.3275      0.023     14.046      0.000       0.282       0.373\n",
       "d_week_1                -0.4752      0.009    -51.924      0.000      -0.493      -0.457\n",
       "m_yr_1                   0.1775      0.013     14.118      0.000       0.153       0.202\n",
       "m_yr_2                   0.3682      0.012     31.296      0.000       0.345       0.391\n",
       "m_yr_3                   0.3119      0.012     26.523      0.000       0.289       0.335\n",
       "ToD_Morning             -0.3916      0.015    -26.792      0.000      -0.420      -0.363\n",
       "ToD_Afternoon            0.6450      0.014     45.188      0.000       0.617       0.673\n",
       "ToD_Evening              0.5259      0.013     39.268      0.000       0.500       0.552\n",
       "==============================================================================\n",
       "Omnibus:                   375558.102   Durbin-Watson:                   2.001\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2101009.022\n",
       "Skew:                           1.423   Prob(JB):                         0.00\n",
       "Kurtosis:                       8.879   Cond. No.                         22.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": 62,
   "metadata": {
    "collapsed": true
   },
   "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": 63,
   "metadata": {
    "collapsed": true
   },
   "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": 64,
   "metadata": {
    "collapsed": true
   },
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "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": 66,
   "metadata": {
    "collapsed": true
   },
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87318625351924339"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Minutes</td>     <th>  R-squared:         </th>  <td>   0.873</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.873</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>6.276e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 01 May 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:52:47</td>     <th>  Log-Likelihood:    </th> <td>-2.9817e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1182108</td>     <th>  AIC:               </th>  <td>5.963e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1182094</td>     <th>  BIC:               </th>  <td>5.964e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</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>    1.5326</td> <td>    0.031</td> <td>   49.352</td> <td> 0.000</td> <td>    1.472</td> <td>    1.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    1.0424</td> <td>    0.015</td> <td>   70.764</td> <td> 0.000</td> <td>    1.014</td> <td>    1.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_speed</th>            <td>   -0.2019</td> <td>    0.005</td> <td>  -44.060</td> <td> 0.000</td> <td>   -0.211</td> <td>   -0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_duration</th>         <td>    0.0148</td> <td> 2.47e-05</td> <td>  600.221</td> <td> 0.000</td> <td>    0.015</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>    0.4430</td> <td>    0.018</td> <td>   24.933</td> <td> 0.000</td> <td>    0.408</td> <td>    0.478</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.6099</td> <td>    0.017</td> <td>  -36.233</td> <td> 0.000</td> <td>   -0.643</td> <td>   -0.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>    0.2871</td> <td>    0.017</td> <td>   16.572</td> <td> 0.000</td> <td>    0.253</td> <td>    0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>   -0.3085</td> <td>    0.007</td> <td>  -45.352</td> <td> 0.000</td> <td>   -0.322</td> <td>   -0.295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>    0.1076</td> <td>    0.009</td> <td>   11.510</td> <td> 0.000</td> <td>    0.089</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>    0.2671</td> <td>    0.009</td> <td>   30.544</td> <td> 0.000</td> <td>    0.250</td> <td>    0.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>    0.2092</td> <td>    0.009</td> <td>   23.935</td> <td> 0.000</td> <td>    0.192</td> <td>    0.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.2132</td> <td>    0.011</td> <td>  -19.619</td> <td> 0.000</td> <td>   -0.234</td> <td>   -0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>    0.4367</td> <td>    0.011</td> <td>   41.158</td> <td> 0.000</td> <td>    0.416</td> <td>    0.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>    0.3530</td> <td>    0.010</td> <td>   35.462</td> <td> 0.000</td> <td>    0.334</td> <td>    0.373</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>586582.610</td> <th>  Durbin-Watson:     </th>  <td>   2.005</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>7187556.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 2.096</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>14.330</td>   <th>  Cond. No.          </th>  <td>1.06e+04</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Minutes   R-squared:                       0.873\n",
       "Model:                            OLS   Adj. R-squared:                  0.873\n",
       "Method:                 Least Squares   F-statistic:                 6.276e+05\n",
       "Date:                Tue, 01 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        15:52:47   Log-Likelihood:            -2.9817e+06\n",
       "No. Observations:             1182108   AIC:                         5.963e+06\n",
       "Df Residuals:                 1182094   BIC:                         5.964e+06\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                    1.5326      0.031     49.352      0.000       1.472       1.593\n",
       "Distance                 1.0424      0.015     70.764      0.000       1.014       1.071\n",
       "avg_speed               -0.2019      0.005    -44.060      0.000      -0.211      -0.193\n",
       "avg_duration             0.0148   2.47e-05    600.221      0.000       0.015       0.015\n",
       "User Type_Subscriber     0.4430      0.018     24.933      0.000       0.408       0.478\n",
       "Gender_1                -0.6099      0.017    -36.233      0.000      -0.643      -0.577\n",
       "Gender_2                 0.2871      0.017     16.572      0.000       0.253       0.321\n",
       "d_week_1                -0.3085      0.007    -45.352      0.000      -0.322      -0.295\n",
       "m_yr_1                   0.1076      0.009     11.510      0.000       0.089       0.126\n",
       "m_yr_2                   0.2671      0.009     30.544      0.000       0.250       0.284\n",
       "m_yr_3                   0.2092      0.009     23.935      0.000       0.192       0.226\n",
       "ToD_Morning             -0.2132      0.011    -19.619      0.000      -0.234      -0.192\n",
       "ToD_Afternoon            0.4367      0.011     41.158      0.000       0.416       0.458\n",
       "ToD_Evening              0.3530      0.010     35.462      0.000       0.334       0.373\n",
       "==============================================================================\n",
       "Omnibus:                   586582.610   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          7187556.319\n",
       "Skew:                           2.096   Prob(JB):                         0.00\n",
       "Kurtosis:                      14.330   Cond. No.                     1.06e+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, 1.06e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load and clean weather data from 2017 (Min Temp, Max Temp, Precipitation)\n",
    "df_weather = pd.read_csv('weather2017.csv')\n",
    "df_weather = df_weather.iloc[:,1:5]\n",
    "df_weather['DATE'] = to_datetime(df_weather['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "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": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87331937535506987"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Minutes</td>     <th>  R-squared:         </th>  <td>   0.874</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.874</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>5.105e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 01 May 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:53:50</td>     <th>  Log-Likelihood:    </th> <td>-2.9811e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1182108</td>     <th>  AIC:               </th>  <td>5.962e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1182091</td>     <th>  BIC:               </th>  <td>5.962e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    16</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>   12.3183</td> <td>    0.021</td> <td>  583.505</td> <td> 0.000</td> <td>   12.277</td> <td>   12.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    1.0406</td> <td>    0.015</td> <td>   70.673</td> <td> 0.000</td> <td>    1.012</td> <td>    1.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_speed</th>            <td>   -0.2016</td> <td>    0.005</td> <td>  -44.017</td> <td> 0.000</td> <td>   -0.211</td> <td>   -0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_duration</th>         <td>    7.0767</td> <td>    0.012</td> <td>  600.214</td> <td> 0.000</td> <td>    7.054</td> <td>    7.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PRCP</th>                 <td>   -0.0192</td> <td>    0.003</td> <td>   -6.696</td> <td> 0.000</td> <td>   -0.025</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TMAX</th>                 <td>    0.1723</td> <td>    0.009</td> <td>   18.791</td> <td> 0.000</td> <td>    0.154</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TMIN</th>                 <td>   -0.0471</td> <td>    0.009</td> <td>   -5.056</td> <td> 0.000</td> <td>   -0.065</td> <td>   -0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>    0.4588</td> <td>    0.018</td> <td>   25.825</td> <td> 0.000</td> <td>    0.424</td> <td>    0.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.6077</td> <td>    0.017</td> <td>  -36.119</td> <td> 0.000</td> <td>   -0.641</td> <td>   -0.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>    0.2858</td> <td>    0.017</td> <td>   16.500</td> <td> 0.000</td> <td>    0.252</td> <td>    0.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>   -0.3152</td> <td>    0.007</td> <td>  -46.312</td> <td> 0.000</td> <td>   -0.329</td> <td>   -0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>   -0.0479</td> <td>    0.011</td> <td>   -4.539</td> <td> 0.000</td> <td>   -0.069</td> <td>   -0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>   -0.0135</td> <td>    0.013</td> <td>   -1.033</td> <td> 0.302</td> <td>   -0.039</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>    0.0169</td> <td>    0.011</td> <td>    1.580</td> <td> 0.114</td> <td>   -0.004</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.2098</td> <td>    0.011</td> <td>  -19.316</td> <td> 0.000</td> <td>   -0.231</td> <td>   -0.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>    0.4388</td> <td>    0.011</td> <td>   41.358</td> <td> 0.000</td> <td>    0.418</td> <td>    0.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>    0.3532</td> <td>    0.010</td> <td>   35.492</td> <td> 0.000</td> <td>    0.334</td> <td>    0.373</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>586072.069</td> <th>  Durbin-Watson:     </th>  <td>   2.005</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>7180839.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 2.093</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>14.325</td>   <th>  Cond. No.          </th>  <td>    64.5</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Minutes   R-squared:                       0.874\n",
       "Model:                            OLS   Adj. R-squared:                  0.874\n",
       "Method:                 Least Squares   F-statistic:                 5.105e+05\n",
       "Date:                Tue, 01 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        15:53:50   Log-Likelihood:            -2.9811e+06\n",
       "No. Observations:             1182108   AIC:                         5.962e+06\n",
       "Df Residuals:                 1182091   BIC:                         5.962e+06\n",
       "Df Model:                          16                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                   12.3183      0.021    583.505      0.000      12.277      12.360\n",
       "Distance                 1.0406      0.015     70.673      0.000       1.012       1.069\n",
       "avg_speed               -0.2016      0.005    -44.017      0.000      -0.211      -0.193\n",
       "avg_duration             7.0767      0.012    600.214      0.000       7.054       7.100\n",
       "PRCP                    -0.0192      0.003     -6.696      0.000      -0.025      -0.014\n",
       "TMAX                     0.1723      0.009     18.791      0.000       0.154       0.190\n",
       "TMIN                    -0.0471      0.009     -5.056      0.000      -0.065      -0.029\n",
       "User Type_Subscriber     0.4588      0.018     25.825      0.000       0.424       0.494\n",
       "Gender_1                -0.6077      0.017    -36.119      0.000      -0.641      -0.575\n",
       "Gender_2                 0.2858      0.017     16.500      0.000       0.252       0.320\n",
       "d_week_1                -0.3152      0.007    -46.312      0.000      -0.329      -0.302\n",
       "m_yr_1                  -0.0479      0.011     -4.539      0.000      -0.069      -0.027\n",
       "m_yr_2                  -0.0135      0.013     -1.033      0.302      -0.039       0.012\n",
       "m_yr_3                   0.0169      0.011      1.580      0.114      -0.004       0.038\n",
       "ToD_Morning             -0.2098      0.011    -19.316      0.000      -0.231      -0.189\n",
       "ToD_Afternoon            0.4388      0.011     41.358      0.000       0.418       0.460\n",
       "ToD_Evening              0.3532      0.010     35.492      0.000       0.334       0.373\n",
       "==============================================================================\n",
       "Omnibus:                   586072.069   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          7180839.761\n",
       "Skew:                           2.093   Prob(JB):                         0.00\n",
       "Kurtosis:                      14.325   Cond. No.                         64.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "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": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy: 0.874 +/- 0.001\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": 77,
   "metadata": {
    "collapsed": true
   },
   "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": 78,
   "metadata": {
    "collapsed": true
   },
   "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": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\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",
    "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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87318625351924339"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87417082193616646"
      ]
     },
     "execution_count": 81,
     "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": 88,
   "metadata": {
    "collapsed": true
   },
   "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": 83,
   "metadata": {
    "collapsed": true
   },
   "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": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "#Predictor variable\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84681359784279886"
      ]
     },
     "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": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Minutes</td>     <th>  R-squared:         </th>  <td>   0.847</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.847</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>5.022e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 01 May 2018</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>16:09:28</td>     <th>  Log-Likelihood:    </th> <td>-3.0951e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>     <td>11821083</td>     <th>  AIC:               </th>  <td>6.190e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>         <td>11821069</td>     <th>  BIC:               </th>  <td>6.190e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</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>   11.1891</td> <td>    0.008</td> <td> 1386.081</td> <td> 0.000</td> <td>   11.173</td> <td>   11.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distance</th>             <td>    0.8640</td> <td>    0.006</td> <td>  152.365</td> <td> 0.000</td> <td>    0.853</td> <td>    0.875</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_speed</th>            <td>   -0.1208</td> <td>    0.002</td> <td>  -64.772</td> <td> 0.000</td> <td>   -0.124</td> <td>   -0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>avg_duration</th>         <td>    7.0782</td> <td>    0.005</td> <td> 1566.123</td> <td> 0.000</td> <td>    7.069</td> <td>    7.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>User Type_Subscriber</th> <td>    0.4831</td> <td>    0.006</td> <td>   76.538</td> <td> 0.000</td> <td>    0.471</td> <td>    0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_1</th>             <td>   -0.7442</td> <td>    0.006</td> <td> -126.893</td> <td> 0.000</td> <td>   -0.756</td> <td>   -0.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gender_2</th>             <td>    0.3127</td> <td>    0.006</td> <td>   51.771</td> <td> 0.000</td> <td>    0.301</td> <td>    0.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d_week_1</th>             <td>   -0.3606</td> <td>    0.002</td> <td> -152.289</td> <td> 0.000</td> <td>   -0.365</td> <td>   -0.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_1</th>               <td>    0.1159</td> <td>    0.003</td> <td>   35.574</td> <td> 0.000</td> <td>    0.109</td> <td>    0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_2</th>               <td>    0.2937</td> <td>    0.003</td> <td>   96.371</td> <td> 0.000</td> <td>    0.288</td> <td>    0.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m_yr_3</th>               <td>    0.2373</td> <td>    0.003</td> <td>   77.924</td> <td> 0.000</td> <td>    0.231</td> <td>    0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Morning</th>          <td>   -0.2384</td> <td>    0.004</td> <td>  -63.109</td> <td> 0.000</td> <td>   -0.246</td> <td>   -0.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Afternoon</th>        <td>    0.5252</td> <td>    0.004</td> <td>  142.360</td> <td> 0.000</td> <td>    0.518</td> <td>    0.532</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ToD_Evening</th>          <td>    0.4207</td> <td>    0.003</td> <td>  121.635</td> <td> 0.000</td> <td>    0.414</td> <td>    0.428</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5240434.515</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>47593846.818</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 1.911</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>12.057</td>    <th>  Cond. No.          </th>   <td>    24.3</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                Minutes   R-squared:                       0.847\n",
       "Model:                            OLS   Adj. R-squared:                  0.847\n",
       "Method:                 Least Squares   F-statistic:                 5.022e+06\n",
       "Date:                Tue, 01 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        16:09:28   Log-Likelihood:            -3.0951e+07\n",
       "No. Observations:            11821083   AIC:                         6.190e+07\n",
       "Df Residuals:                11821069   BIC:                         6.190e+07\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                   11.1891      0.008   1386.081      0.000      11.173      11.205\n",
       "Distance                 0.8640      0.006    152.365      0.000       0.853       0.875\n",
       "avg_speed               -0.1208      0.002    -64.772      0.000      -0.124      -0.117\n",
       "avg_duration             7.0782      0.005   1566.123      0.000       7.069       7.087\n",
       "User Type_Subscriber     0.4831      0.006     76.538      0.000       0.471       0.495\n",
       "Gender_1                -0.7442      0.006   -126.893      0.000      -0.756      -0.733\n",
       "Gender_2                 0.3127      0.006     51.771      0.000       0.301       0.325\n",
       "d_week_1                -0.3606      0.002   -152.289      0.000      -0.365      -0.356\n",
       "m_yr_1                   0.1159      0.003     35.574      0.000       0.109       0.122\n",
       "m_yr_2                   0.2937      0.003     96.371      0.000       0.288       0.300\n",
       "m_yr_3                   0.2373      0.003     77.924      0.000       0.231       0.243\n",
       "ToD_Morning             -0.2384      0.004    -63.109      0.000      -0.246      -0.231\n",
       "ToD_Afternoon            0.5252      0.004    142.360      0.000       0.518       0.532\n",
       "ToD_Evening              0.4207      0.003    121.635      0.000       0.414       0.428\n",
       "==============================================================================\n",
       "Omnibus:                  5240434.515   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         47593846.818\n",
       "Skew:                           1.911   Prob(JB):                         0.00\n",
       "Kurtosis:                      12.057   Cond. No.                         24.3\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 86,
     "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": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy: 0.847 +/- 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=5)\n",
    "print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))"
   ]
  }
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