Project 2 - Supply and Demand Gap Analysis

UBER Supply and Demand Gap Analysis Aim – The aim of this is project is to identify and analyze the gap in supply and demand of uber cabs having two pickup points namely airport and city from the dataset downloaded from kaggle.

Dataset:

https://www.kaggle.com/code/hellbuoy/uber-supply-demand-gap

UBER Supply and Demand Gap Analysis

Aim –

The aim of this is project is to identify and analyze the gap in supply and demand of uber cabs having two pickup points namely airport and city from the dataset downloaded from kaggle.

Introduction –

Supply and demand gap analysis is done by every company to know the reasons for the company’s success or failure and to be prepared based on the customer trend and improve the company’s performance.

Uber is a cab service which provides its services through mobile application. The current dataset taken has the details for five days in the month of July of 2016. The main objective of this project is to analyze the dataset and give recommendations based on our findings.

Methodology –

Step 1: Data Exploration –

In order to take any necessary steps we must understand the data we have, which is data exploration and based on our understanding we can go ahead with the appropriate data cleaning steps required.

Step 2: Data cleaning –

I have used Power BI tool to clean and transform the data as per my understanding and I have replaced values, grouped the data and created a new table where it seemed fit.

Step 3: Data Visualization –

I have used Power BI tool to visualize the data available to gain meaningful insights and better understand the dataset. I have used different visualizations like bar graphs, line graphs which are available in the tool.

Analysis –

The dataset contains the following columns

Request Id- Gives a unique id when the customer makes a request for the cab.

Pickup Point – gives the point of pickup of the customer (airport or city).

Status – gives the status of the request whether it was completed or not (trip completed, no cars available, cancelled).

Driver id – gives the id of the driver who got the request if cars were available.

Request timestamp –gives the date and time of the request.

Drop timestamp – gives the date and time of the drop off.

The dataset contains null values in the driver id when there are no cars available and contains null values in the drop timestamp when the trip was cancelled or when no cars are available.

In the analysis part first | have analysed Sum of Request id for City (11640680) was higher than Airport (11188750). City accounted for 50.99% of Sum of Request id. Airport had 11188750 Sum of Request id and City had 11640680. With the help of clusterd column chart. And used summary to show the details of visual

In the second analysis show the status in which the no of cancels cars, No cars available and the trip completed in the pickup points with the help of clusterd column chart with the background of uber logo. And the result of No Cars Available had the highest total Sum of Request id at 9501514, followed by Trip Completed at 9378847 and Cancelled at 3949069. Airport in Status made up 27.24% of Sum of Request id.No Cars Available had the highest average Sum of Request id at 4750757, followed by Trip Completed at 46,89,423.50 and Cancelled at 19,74,534.50.

In the third analysis the data shows the sum of request id by status the result shows the No Cars Available had the highest Sum of Request id at 9501514, followed by Trip Completed at 9378847 and Cancelled at 3949069 No Cars Available accounted for 41.62% of Sum of Request id..

The below donut chart shows the no of trip completed and the total no of trip cancelled. As the result see the rate of cancelled trip and no cars available are higher than the the trip completed

In the fourth analysis the graph shows the no of requests in a one day count in the airport and city with the add the slicer menu to see the specific day counts.

The fifth analysis show the Most request are in evening and after morning, there is low count of request in noon and early morning. The visual are shows with the help of line chart. And used a zoom slider to see the view with zoom

Insights –

In the Peak Morning Slot where the demand for City to Airport trips are high, there are maximum number of trip cancellations leading to great Gap in the Demand & Supply.A high gap of 71.85% can be seen during the morning peak hours.In the Peak Evening Slot where the demand for Airport to City trips are high, there are maximum number of “No Cars Available” leading to great Gap in the Demand & Supply.A high gap of 79.28% can be seen during the evening peak hours.

Recommendations –

The increase in number of vehicles will be one solution.Giving out rewards based on the number of trips during the peak hours without cancelling them.

Conclusion –

Based on the data analysis performed, following recommendation can be used by Uber to bridge the gap between supply and demand –For bridging the demand supply gap from airport to city, making a permanent stand in the airport itself where the cabs will be available at all times and the incomplete requests can come down significantly. 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. 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.