262-Mini-Project-Regression-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from statsmodels.formula.api import ols\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "from datetime import datetime\n",
    "from pytz import timezone\n",
    "import pytz\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>None</th>\n",
       "      <th>Price</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Wait_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-08 17:51:51.493805</td>\n",
       "      <td>1541699632</td>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-11-08 17:55:56.128564</td>\n",
       "      <td>1541699877</td>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-08 17:56:57.918118</td>\n",
       "      <td>1541699939</td>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-08 17:57:59.659881</td>\n",
       "      <td>1541700001</td>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-08 17:59:01.625413</td>\n",
       "      <td>1541700063</td>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Time        None  Price  Duration  Distance  \\\n",
       "0  2018-11-08 17:51:51.493805  1541699632   8.71       960      3.27   \n",
       "1  2018-11-08 17:55:56.128564  1541699877   8.71       960      3.27   \n",
       "2  2018-11-08 17:56:57.918118  1541699939   8.71       960      3.27   \n",
       "3  2018-11-08 17:57:59.659881  1541700001   8.71       960      3.27   \n",
       "4  2018-11-08 17:59:01.625413  1541700063   8.71       960      3.27   \n",
       "\n",
       "   Wait_Time  \n",
       "0          4  \n",
       "1          3  \n",
       "2          2  \n",
       "3          2  \n",
       "4          4  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uberx = pd.read_csv('UberPriceTrackX.csv',header=None)\n",
    "uberx.columns = ['Time','None','Price','Duration','Distance','Wait_Time']\n",
    "uberx.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert time to Local Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_converter(Data):\n",
    "    \n",
    "    \"\"\"\n",
    "    Converts given time from UTC to specified timezone\n",
    "    \"\"\"\n",
    "    date = list(Data['Time'])\n",
    "\n",
    "    converted_date = []\n",
    "    \n",
    "    for date in date:\n",
    "        converted_date.append(convert_time(date))\n",
    "\n",
    "    Data['Converted Time'] = converted_date\n",
    "    Data_df = Data[['Time','Converted Time','Price','Duration','Distance','Wait_Time']]\n",
    "    \n",
    "    return Data_df, converted_date\n",
    "\n",
    "def convert_time(dt):\n",
    "    \n",
    "    \"\"\"\n",
    "    Converts given time from UTC to specified timezone\n",
    "    Also removes pytz timezone information as to not mess with plotting\n",
    "    \"\"\"\n",
    "    \n",
    "    date_format='%Y-%m-%d %H:%M:%S.%f'\n",
    "\n",
    "    date_current_utc = datetime.strptime(dt, date_format)\n",
    "    date_current_utc = pytz.utc.localize(date_current_utc).astimezone(pytz.UTC)\n",
    "    \n",
    "    date_changed = date_current_utc.astimezone(timezone('US/Pacific'))\n",
    "    \n",
    "    # Remove timezone information cause it messes with the plotting\n",
    "    date_changed = date_changed.replace(tzinfo=None)\n",
    "    return date_changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_X, Converted_Date_X = time_converter(uberx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain daily time values for the Weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hour_minute(s): \n",
    "    hours = s['Converted Time'].dt.hour.map(str)\n",
    "    minutes = s['Converted Time'].dt.minute.map(str)\n",
    "    \n",
    "    hour_minute = hours + ':' + minutes\n",
    "    s = []\n",
    "    for i in hour_minute:\n",
    "        s.append(datetime.strptime(i, '%H:%M'))\n",
    "    return s\n",
    "\n",
    "def get_day(Uber_data):\n",
    "    Uber_data['days'] = pd.to_datetime(Uber_data['Converted Time']).dt.day_name()\n",
    "    Uber_data['hour_minute'] = get_hour_minute(Uber_data)\n",
    "    return Uber_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find out what day it is for each record\n",
    "Data_X = get_day(Data_X)\n",
    "\n",
    "# Remove Saturday and Sunday\n",
    "Data_X_Weekdays = Data_X.drop(Data_X[(Data_X['days'] == 'Saturday') | (Data_X['days'] == 'Sunday')].index)\n",
    "\n",
    "# Drop 'Time, 'Converted Time', and 'days' columns to clean up\n",
    "Data_X_Weekdays = Data_X_Weekdays.drop(columns=['Time','Converted Time','days'])\n",
    "\n",
    "# Reset index to clean up\n",
    "Data_X_Weekdays = Data_X_Weekdays.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us visualize the distribution of price throughout the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple Plot\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "plt.plot_date(Data_X_Weekdays['hour_minute'],Data_X_Weekdays['Price'],alpha=0.1)#,c=Data_X_Weekdays['days'].apply(lambda x: colors[x]))\n",
    "plt.title('Price vs Time of Day')\n",
    "plt.xlabel('Time of Day')\n",
    "plt.ylabel('Trip Price ($)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that time of day has a cyclical relationship with price. Generally, prices go up during the day, and go down deep at night when people are home or asleep. Hence, it would not make sense to directly utilize this as an independent variable in an Ordinary Least Squares analysis. Instead, we categorized the day into bins and treat them as categorical data. Based on and general information and the plot above, we can divide the data into four mutually exclusive categories - AM Peak (6AM to 9AM), Midday (9AM to 4PM), PM Peak (4PM to 8PM), and Night (8PM to 6AM). Now, this categorization can be subjective and can be tweaked in many ways. For the purpose of this exercise, these are the categories that will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert time data to categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because they are mutually exclusive, we will need 3 columns to represent the 4 categories. The columns will be 'AM Peak', 'Midday', and 'PM Peak'. If the value is 0 for all three columns, that would indicate 'Night'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Wait_Time</th>\n",
       "      <th>hour_minute</th>\n",
       "      <th>Time_Of_Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4</td>\n",
       "      <td>1900-01-01 09:51:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 09:55:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 09:56:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 09:57:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4</td>\n",
       "      <td>1900-01-01 09:59:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Duration  Distance  Wait_Time         hour_minute Time_Of_Day\n",
       "0   8.71       960      3.27          4 1900-01-01 09:51:00      Midday\n",
       "1   8.71       960      3.27          3 1900-01-01 09:55:00      Midday\n",
       "2   8.71       960      3.27          2 1900-01-01 09:56:00      Midday\n",
       "3   8.71       960      3.27          2 1900-01-01 09:57:00      Midday\n",
       "4   8.71       960      3.27          4 1900-01-01 09:59:00      Midday"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create New Column Initialized to 'Night'\n",
    "Data_X_Weekdays['Time_Of_Day'] = '0_Night'\n",
    "\n",
    "# Categorize AM Peak\n",
    "Data_X_Weekdays.loc[(Data_X_Weekdays['hour_minute'] > '1900-01-01 06:00:00') &\n",
    "                    (Data_X_Weekdays['hour_minute'] < '1900-01-01 09:00:00'),'Time_Of_Day'] = 'AM_Peak'\n",
    "\n",
    "# Categorize Midday\n",
    "Data_X_Weekdays.loc[(Data_X_Weekdays['hour_minute'] > '1900-01-01 09:00:00') &\n",
    "                    (Data_X_Weekdays['hour_minute'] < '1900-01-01 16:00:00'),'Time_Of_Day'] = 'Midday'\n",
    "\n",
    "# Categorize PM Peak\n",
    "Data_X_Weekdays.loc[(Data_X_Weekdays['hour_minute'] > '1900-01-01 16:00:00') &\n",
    "                    (Data_X_Weekdays['hour_minute'] < '1900-01-01 20:00:00'),'Time_Of_Day'] = 'PM_Peak'\n",
    "\n",
    "# View\n",
    "Data_X_Weekdays.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can use the pandas function 'get_dummies' to quickly generate a one-hot encoded DataFrame as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOD_AM_Peak</th>\n",
       "      <th>TOD_Midday</th>\n",
       "      <th>TOD_PM_Peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOD_AM_Peak  TOD_Midday  TOD_PM_Peak\n",
       "0            0           1            0\n",
       "1            0           1            0\n",
       "2            0           1            0\n",
       "3            0           1            0\n",
       "4            0           1            0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Time_of_Day = pd.get_dummies(Data_X_Weekdays['Time_Of_Day'],drop_first=True,prefix='TOD')\n",
    "Time_of_Day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Wait_Time</th>\n",
       "      <th>hour_minute</th>\n",
       "      <th>Time_Of_Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4</td>\n",
       "      <td>1900-01-01 09:51:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 09:55:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 09:56:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 09:57:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.71</td>\n",
       "      <td>960</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4</td>\n",
       "      <td>1900-01-01 09:59:00</td>\n",
       "      <td>Midday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Duration  Distance  Wait_Time         hour_minute Time_Of_Day\n",
       "0   8.71       960      3.27          4 1900-01-01 09:51:00      Midday\n",
       "1   8.71       960      3.27          3 1900-01-01 09:55:00      Midday\n",
       "2   8.71       960      3.27          2 1900-01-01 09:56:00      Midday\n",
       "3   8.71       960      3.27          2 1900-01-01 09:57:00      Midday\n",
       "4   8.71       960      3.27          4 1900-01-01 09:59:00      Midday"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two DataFrames\n",
    "model_df = pd.merge(Data_X_Weekdays,Time_of_Day,left_index=True,right_index=True)\n",
    "\n",
    "# Drop 'hour_minute' and 'Time_Of_Day' to clean up\n",
    "model_df = model_df.drop(columns=['hour_minute','Time_Of_Day'])\n",
    "\n",
    "# Quick View\n",
    "Data_X_Weekdays.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data is ready for regression analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's first take a look at the correlation matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Wait_Time</th>\n",
       "      <th>TOD_AM_Peak</th>\n",
       "      <th>TOD_Midday</th>\n",
       "      <th>TOD_PM_Peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700839</td>\n",
       "      <td>0.484140</td>\n",
       "      <td>-0.169339</td>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.421560</td>\n",
       "      <td>0.225994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>0.700839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329317</td>\n",
       "      <td>-0.268228</td>\n",
       "      <td>0.184667</td>\n",
       "      <td>0.358283</td>\n",
       "      <td>0.302478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>0.484140</td>\n",
       "      <td>0.329317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.033255</td>\n",
       "      <td>-0.062323</td>\n",
       "      <td>0.213913</td>\n",
       "      <td>0.007354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait_Time</th>\n",
       "      <td>-0.169339</td>\n",
       "      <td>-0.268228</td>\n",
       "      <td>-0.033255</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>-0.099490</td>\n",
       "      <td>-0.102817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOD_AM_Peak</th>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.184667</td>\n",
       "      <td>-0.062323</td>\n",
       "      <td>0.004404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.189939</td>\n",
       "      <td>-0.132777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOD_Midday</th>\n",
       "      <td>0.421560</td>\n",
       "      <td>0.358283</td>\n",
       "      <td>0.213913</td>\n",
       "      <td>-0.099490</td>\n",
       "      <td>-0.189939</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.380781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOD_PM_Peak</th>\n",
       "      <td>0.225994</td>\n",
       "      <td>0.302478</td>\n",
       "      <td>0.007354</td>\n",
       "      <td>-0.102817</td>\n",
       "      <td>-0.132777</td>\n",
       "      <td>-0.380781</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price  Duration  Distance  Wait_Time  TOD_AM_Peak  TOD_Midday  \\\n",
       "Price        1.000000  0.700839  0.484140  -0.169339     0.012708    0.421560   \n",
       "Duration     0.700839  1.000000  0.329317  -0.268228     0.184667    0.358283   \n",
       "Distance     0.484140  0.329317  1.000000  -0.033255    -0.062323    0.213913   \n",
       "Wait_Time   -0.169339 -0.268228 -0.033255   1.000000     0.004404   -0.099490   \n",
       "TOD_AM_Peak  0.012708  0.184667 -0.062323   0.004404     1.000000   -0.189939   \n",
       "TOD_Midday   0.421560  0.358283  0.213913  -0.099490    -0.189939    1.000000   \n",
       "TOD_PM_Peak  0.225994  0.302478  0.007354  -0.102817    -0.132777   -0.380781   \n",
       "\n",
       "             TOD_PM_Peak  \n",
       "Price           0.225994  \n",
       "Duration        0.302478  \n",
       "Distance        0.007354  \n",
       "Wait_Time      -0.102817  \n",
       "TOD_AM_Peak    -0.132777  \n",
       "TOD_Midday     -0.380781  \n",
       "TOD_PM_Peak     1.000000  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we observe that Duration, Distance, and Time of Day (specifically Midday) have the highest correlation to Price.\n",
    "\n",
    "However, in the setup of our data collection, distance was fixed for each API request. We hence look at Duration and Time of Day. Although the correlation coefficients vary greatly among the three Time of Day columns (0.01 to 0.42), we will first include all three in this exercise and improve the model as we go along. We observe that Duration and Time have relatively low correlation with each other (0.18 to 0.36). We thus begin regression analysis with Duration as the predictor.\n",
    "\n",
    "Let's visualize these two features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Price vs Estimated Trip Duration**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.scatter(Data_X_Weekdays['Duration'],Data_X_Weekdays['Price'],alpha=0.1)\n",
    "plt.title('Price vs Estimated Trip Duration')\n",
    "plt.xlabel('Estimated Trip Duration (s)')\n",
    "plt.ylabel('Trip Price ($)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Price vs Time of Day**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_sorted = Data_X_Weekdays.sort_values(by=['Time_Of_Day'])\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "plt.scatter(x=data_sorted['Time_Of_Day'],y=data_sorted['Price'],alpha=0.5)\n",
    "plt.title('Price vs Time of Day')\n",
    "plt.xlabel('Time of Day')\n",
    "plt.ylabel('Trip Price ($)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall shape of Price vs Estimated Trip Duration validates the correlation calculated above. The Time of Day variable also has a similar trend present, but not as strongly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we fit an OLS Regression Model.\n",
    "  \n",
    "First, we start with Duration as the sole predictor, being the feature most correlated to price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Price</td>      <th>  R-squared:         </th> <td>   0.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4567.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 14 Dec 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:48:48</td>     <th>  Log-Likelihood:    </th> <td> -2389.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4733</td>      <th>  AIC:               </th> <td>   4782.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4731</td>      <th>  BIC:               </th> <td>   4795.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>Intercept</th> <td>    5.5927</td> <td>    0.040</td> <td>  139.615</td> <td> 0.000</td> <td>    5.514</td> <td>    5.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Duration</th>  <td>    0.0039</td> <td> 5.72e-05</td> <td>   67.579</td> <td> 0.000</td> <td>    0.004</td> <td>    0.004</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4410.959</td> <th>  Durbin-Watson:     </th>  <td>   0.242</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>367206.830</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.197</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>45.327</td>  <th>  Cond. No.          </th>  <td>4.81e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.81e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   R-squared:                       0.491\n",
       "Model:                            OLS   Adj. R-squared:                  0.491\n",
       "Method:                 Least Squares   F-statistic:                     4567.\n",
       "Date:                Fri, 14 Dec 2018   Prob (F-statistic):               0.00\n",
       "Time:                        00:48:48   Log-Likelihood:                -2389.2\n",
       "No. Observations:                4733   AIC:                             4782.\n",
       "Df Residuals:                    4731   BIC:                             4795.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      5.5927      0.040    139.615      0.000       5.514       5.671\n",
       "Duration       0.0039   5.72e-05     67.579      0.000       0.004       0.004\n",
       "==============================================================================\n",
       "Omnibus:                     4410.959   Durbin-Watson:                   0.242\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           367206.830\n",
       "Skew:                           4.197   Prob(JB):                         0.00\n",
       "Kurtosis:                      45.327   Cond. No.                     4.81e+03\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, 4.81e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols(formula=\"Price ~ Duration\", data=model_df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the regression model here performs decently, but R-squared is relatively low. This could indicate that the relationship is not linear and/or that there are other factors affecting price. Let us try introducing another variable, the Time of Day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Price</td>      <th>  R-squared:         </th> <td>   0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1408.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 14 Dec 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:48:50</td>     <th>  Log-Likelihood:    </th> <td> -2131.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4733</td>      <th>  AIC:               </th> <td>   4274.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4728</td>      <th>  BIC:               </th> <td>   4306.</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>Intercept</th>   <td>    6.0017</td> <td>    0.047</td> <td>  128.689</td> <td> 0.000</td> <td>    5.910</td> <td>    6.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Duration</th>    <td>    0.0030</td> <td> 7.62e-05</td> <td>   39.882</td> <td> 0.000</td> <td>    0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOD_AM_Peak</th> <td>   -0.0297</td> <td>    0.027</td> <td>   -1.099</td> <td> 0.272</td> <td>   -0.083</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOD_Midday</th>  <td>    0.3354</td> <td>    0.017</td> <td>   19.901</td> <td> 0.000</td> <td>    0.302</td> <td>    0.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOD_PM_Peak</th> <td>    0.2293</td> <td>    0.019</td> <td>   12.087</td> <td> 0.000</td> <td>    0.192</td> <td>    0.267</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4751.630</td> <th>  Durbin-Watson:     </th>  <td>   0.273</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>525460.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.661</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>53.770</td>  <th>  Cond. No.          </th>  <td>6.41e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.41e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   R-squared:                       0.544\n",
       "Model:                            OLS   Adj. R-squared:                  0.543\n",
       "Method:                 Least Squares   F-statistic:                     1408.\n",
       "Date:                Fri, 14 Dec 2018   Prob (F-statistic):               0.00\n",
       "Time:                        00:48:50   Log-Likelihood:                -2131.9\n",
       "No. Observations:                4733   AIC:                             4274.\n",
       "Df Residuals:                    4728   BIC:                             4306.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept       6.0017      0.047    128.689      0.000       5.910       6.093\n",
       "Duration        0.0030   7.62e-05     39.882      0.000       0.003       0.003\n",
       "TOD_AM_Peak    -0.0297      0.027     -1.099      0.272      -0.083       0.023\n",
       "TOD_Midday      0.3354      0.017     19.901      0.000       0.302       0.368\n",
       "TOD_PM_Peak     0.2293      0.019     12.087      0.000       0.192       0.267\n",
       "==============================================================================\n",
       "Omnibus:                     4751.630   Durbin-Watson:                   0.273\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           525460.201\n",
       "Skew:                           4.661   Prob(JB):                         0.00\n",
       "Kurtosis:                      53.770   Cond. No.                     6.41e+03\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, 6.41e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols(formula=\"Price ~ Duration + TOD_AM_Peak + TOD_Midday + TOD_PM_Peak\", data=model_df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the adjusted R-squared fit improved from 0.491 to 0.543. Although this is an improvement, this also means that there is much to the data left unexplained by our independent variables.\n",
    "\n",
    "We also observe that most of the P-values of the coefficient estimators are very low, indicating strength in the estimators for the coefficients of the regression. However, the p-value for the slope of 'TOD_AM_Peak' is very high at 0.272. This means that we cannot reject the null hypothesis that the slope of 'TOD_AM_Peak' equals zero. Intuitively, this makes sense as the correlation coefficient we obtained for this variable in the previous section was low at 0.01. Hence, we could opt to remove the variable from the model, which is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Price</td>      <th>  R-squared:         </th> <td>   0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1877.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 14 Dec 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:07:40</td>     <th>  Log-Likelihood:    </th> <td> -2132.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4733</td>      <th>  AIC:               </th> <td>   4273.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4729</td>      <th>  BIC:               </th> <td>   4299.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</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>Intercept</th>   <td>    6.0218</td> <td>    0.043</td> <td>  140.372</td> <td> 0.000</td> <td>    5.938</td> <td>    6.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Duration</th>    <td>    0.0030</td> <td> 6.74e-05</td> <td>   44.485</td> <td> 0.000</td> <td>    0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOD_Midday</th>  <td>    0.3442</td> <td>    0.015</td> <td>   23.229</td> <td> 0.000</td> <td>    0.315</td> <td>    0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOD_PM_Peak</th> <td>    0.2386</td> <td>    0.017</td> <td>   14.018</td> <td> 0.000</td> <td>    0.205</td> <td>    0.272</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4757.406</td> <th>  Durbin-Watson:     </th>  <td>   0.273</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>526474.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.670</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>53.817</td>  <th>  Cond. No.          </th>  <td>5.59e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.59e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   R-squared:                       0.543\n",
       "Model:                            OLS   Adj. R-squared:                  0.543\n",
       "Method:                 Least Squares   F-statistic:                     1877.\n",
       "Date:                Fri, 14 Dec 2018   Prob (F-statistic):               0.00\n",
       "Time:                        01:07:40   Log-Likelihood:                -2132.5\n",
       "No. Observations:                4733   AIC:                             4273.\n",
       "Df Residuals:                    4729   BIC:                             4299.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept       6.0218      0.043    140.372      0.000       5.938       6.106\n",
       "Duration        0.0030   6.74e-05     44.485      0.000       0.003       0.003\n",
       "TOD_Midday      0.3442      0.015     23.229      0.000       0.315       0.373\n",
       "TOD_PM_Peak     0.2386      0.017     14.018      0.000       0.205       0.272\n",
       "==============================================================================\n",
       "Omnibus:                     4757.406   Durbin-Watson:                   0.273\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           526474.905\n",
       "Skew:                           4.670   Prob(JB):                         0.00\n",
       "Kurtosis:                      53.817   Cond. No.                     5.59e+03\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, 5.59e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols(formula=\"Price ~ Duration + TOD_Midday + TOD_PM_Peak\", data=model_df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that after removing 'TOD_AM_Peak' as a variable, the adjusted R-squared stayed the same, but other indicators like AIC and BIC improved showing a reduction in overfitting and a better bias-variance balance than the previous model, without reducing the quality of the regression.\n",
    "\n",
    "Looking deeper into the characteristics of the final model, we observe that the value of the intercept is quite high. This value of intercept means that if all the variables have a value of 0, the price would be \\$6.02. Because the AM Peak and Night Time of Day categories are not included in the model, the price values for these two times of day are incorporated in the intercept. This could also indicate a base price that Uber imposes and/or that there are other factors not accounted for in the model, as the R-squared fit suggests.\n",
    "\n",
    "Furthermore, the coefficient for duration is very low at 0.003. This is primarily due to the fact that the values of the Duration variable are very high, with its units expressed in seconds. Depending on the purpose of the regression, one way to address this is to standardize all the variables.\n",
    "\n",
    "On the other hand, TOD_Midday and TOD_PM_Peak are directly comparable since their values are only either 0 or 1. Looking at the values of their coefficients, we can surmise that whether a trip happens during midday has a bigger impact to price than whether it happens during the PM peak. Looking back to the distribution of price vs time of day, this also makes sense as the most high-priced trips seem to occur during midday."
   ]
  }
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