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.

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.

I have split the columns into date and time for ease of analysis. Further I created a conditional column called hour of the day from the timestamp so as to know the demand of the cabs based on the hours of the day.

Also created a new column considering only successful trips as supply and considering the no cars available and cancelled trips as gap.

I have created a new column called Time of Day based on Hour of Day.