Project 2 - Supply and Demand Gap Analysis

Project 2 - Supply and Demand Gap Analysis

Aim:

The aim of this project is to collect and analyse the data in detail about the Pickup point from Airport and from City of uber cars

Introduction:

Uber provides the 2016 data. By using this data find out the demand and supply Analysis gap of the cab.

The main objective of this project is to analyse the data on, Uber Request data. Through the analysis of data, we can find some important insights.

Problem statement:

You may have some experience of travelling to and from the airport. Have you ever used Uber or any other cab service for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars?

Well, if these are the problems faced by customers, these very issues also impact the business of Uber. If drivers cancel the request of riders or if cars are unavailable, Uber loses out on its revenue.

the root cause of the problem (i.e. cancellation and non-availability of cars to and from the airport) and recommend ways to improve the situation. As a result of the analysis, we should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.

Methodology:

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

1. Request id: A unique identifier of the request
2. Time of request: The date and time at which the customer made the trip request
3. Drop-off time: The drop-off date and time, in case the trip was completed
4. Pick-up point: The point from which the request was made
5. Driver id: The unique identification number of the driver
6. Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available
7. Gap: whether the trip is completed or not
8. Request time slot: At what time the slot is booked like early morning, morning, late morning, noon, evening, night.

The first step is cleaning the data. In data cleaning I cleaned the data with the transform data option present in Power BI because the dataset was not cleaned and included some missing values, so with the help of the duplicates function in Power BI, I cleaned the data and replace the null values with 0 and then started working on it.

After Data cleaning performs the Data interpretation step. In Data interpretation found some important information.

The data set was almost clean and completed.

Replace the Null values with 0

There is a total of 6745 rows and 8 columns are present.

Visualization:

In visualization, I took the help of Power BI Desktop software to make graphs and charts here some relevant graphs and charts are attached.

Analysis:

From the above Visualization find some interesting points that are mentioned below

1.From this visual we can say that the request made at city pickup point is high compare to the request made at airport and high number of trips completed from city pickup point. due to lack of cars available there is less number of trips from airport.

2. pickup point are more from city(55.99%) then airport(48.01%)
3. morning section there are high number of requests from city and in evening section more number of requests from airport
4. trips are getting cancelled(2.7k) due to unavailability of cars
5. there are less number of trip completed in any request time slot . in the evening the trip not completed due to unavailability of cars
6. evening high number of requests are made from airport and high number of requests from city in the morning

pickup Point - City :

As per the analysis, the morning time slot is most problematic where the requests are being cancelled. Most probably the requests are being cancelled by the drivers due to the morning rush as it being the office hours and seeing the destination as airport which would be too far, the driver would think to earn more for the shorter trips within the city.

Pickup Point - Airport :

Upon analysis, the evening time slot seems to be most problematic for pickup points as airport where the requests being No Cars Available. The reason seems to be that not enough cars are available to service the requests as cars might not be available at the airport due to the cars serving inside the city.

Insights:

In conclusion, we can take a look at the final Dashboard for further Analysis.

We analysed the data according to the request ID

Recommendation:

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.

Uber can provide some incentives to the driver who complete the trip from city to airport in the morning part. This might result the driver to not cancel the request from city to airport trips.

Last but sure solution to bring down the gap is to increase the numbers of cab in its fleet.

We can analyse the data of the overall Trips in the City.

Like this dataset, we can Analyse the Data according to the Different Cities-Area it takes more clarity to how to fill the supply and demand gap

We can analyse the data with the proper format

We can collect good datasets for more effective analysis by using charts.

Conclusions:

In Conclusion, we can take look at the Dashboard for further Analysis.

According to analysis, almost 1713 trips are cancelled for Airport trips. In airports, trips need to increase the drivers that help to increase the Business.

The second points are that almost 1066 trips are cancelled by the customer in the city area and need to improve some features.

A total of 1327 Drivers successfully completed the Airport trips and 1504 Drivers successfully completed the City trips.