Project 2 - Supply and Demand Gap Analysis

AIM:

<!-- [if !supportLists]-->·        <!--[endif]-->The main aim of this project is to collect the data and analyze it according to the dataset. Purpose of the project is to create a dashboard with the help of insights. For this Power BI Desktop software is used which is useful to find insights from the data.

INTRODUCTION:

<!-- [if !supportLists]-->·        <!--[endif]-->We have a cab trip dataset wherein details regarding request id, pick up point, driver id, Status, Request timestamp and Drop timestamp. Based on these features, I have cleaned the data and create some useful insights. We may have some experience of travelling to and from the airport. Main objective of this case study is to find the actual reason of gap between demand and supply of the cars. I will try to identify the problem.

PROBLEM STATEMENT:

<!-- [if !supportLists]-->·        <!--[endif]-->You have an emergency to reach a destination point, but unfortunately no cabs are available or maybe cancelled by cab driver. Most of us faced this situation in our real life, while travelling to Airport or Railway station etc. This will surely affect the revenue and reputation of Uber. That too in the peak hours of travel the driver’s are cancelling the trip or demanding for a high fare, which is not acceptable by the company.

METHODOLOGY:

<!-- [if !supportLists]-->·        <!--[endif]-->Data cleaning and making the dataset ready for analysis: This involves importing the data into Power BI and organizing it in a way that is suitable for analysis. This may involve cleaning and transforming the data, as well as creating relationships between different data tables.

<!-- [if !supportLists]-->·        <!--[endif]-->Data mining and visualization : Use the various visualization tools in Power BI to explore the data and identify trends, patterns, and create relationships. This may involve creating charts, maps, and other visualizations to represent the data in a way that is easy to understand.

<!-- [if !supportLists]-->·        <!--[endif]-->Analyze the data: Use the analytical tools in Power BI to perform more in-depth analysis of the data.

ANALYSIS :

There are six attributes associated with each request made by a customer:

<!-- [if !supportLists]-->·        <!--[endif]-->Request id: A unique identifier of the request

<!-- [if !supportLists]-->·        <!--[endif]-->Time of request: The date and time at which the customer made the trip request

<!-- [if !supportLists]-->·        <!--[endif]-->Drop-off time: The drop-off date and time, in case the trip was completed

<!-- [if !supportLists]-->·        <!--[endif]-->Pick-up point: The point from which the request was made

<!-- [if !supportLists]-->·        <!--[endif]-->Driver id: The unique identification number of the driver

<!-- [if !supportLists]-->·        <!--[endif]-->Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available

Data Cleaning: After importing the data first I need to clean the data, so I can utilize it. In this process I need to check all the data are in the required formats. Since we require checking the time of the request separately, the request time has been split as date and time in power query editor.

Data Visualization: Once the data is loaded to the Power BI, we need to prepare some measures of the data to add in the visualization.

INSIGHT:

From dashboard we create some useful insights. Count of driver id is 4095 which is good and total no. of request for pickup is 6745.Although, overall gap between airport and city are not high, But if we divided time into parts we will find that the Peak Evening Slot where the demand for Airport to City trips is high, there is a maximum number of “No Cars Available” leading to a great Gap in Demand & Supply.

RECOMMENDATION:

Uber should provide higher monetary incentives for the driver taking a Peak Morning trip from City to Airport.

Setting targets for drivers to take up the City to Airport or Airport to City trips and then suitably rewarding them through lucky draw schemes, and gift Vouchers.

Incentivize Airport to City trips at night so that more drivers from the City reach the Airport thus increasing the availability of Cars.

CONCLUSION:

The demand during morning and afternoon hours from the Airport to City is quite low, which means that a driver who completed a City to Airport route in the morning hours may have to wait a really long time to get a ride back to the City, or he may have to come back without a passenger. Both situations are not idle for drivers, which might be the reason for highest number of "Cancelled" trips in the City to Airport trip in the morning hours

The vice versa happens in evening when there are too many flight coming in to the airport and hence there is a high demand for cabs and not enough supply. This is why most number of “No Cars Available” was observed in Evening in the Airport to City route.