Project 2 - Supply and Demand Gap Analysis

PROJECT -2 Uber Supply and Demand Gap Analysis

AIM –

The aim of this project is to analysis the gap in supply and demand of uber cabs having two pick up point namely city and the airport from the dataset that I downloaded from the kaggle .

INTRODUCTION –

Supply and demand gap analysis is very important because it is used by most of the company’s to find out the gap between the supply and demand to enhance the company’ progress growth .

Uber is the cab service facility which is run in the mobile application by downloading the uber app. The data is taken from the 2016 uber cab data .

METHODOLOGY –

Data exploration – firstly for making the visualisation we go through the data and examined the data , what questions we ask through the data , we explore the data and based on the information we get through the data , go ahead with the data .

Data cleaning – I have used power bi software to clean the data wherever dkbi query editor . I replaced the values , split the columns to understand the date and timings of pick up point easily , grouped the data .

Data visualisation - I have used power bi to make the visualisation based on the data that we get through uber cab service .

After cleaning the data we make charts , bar graphs , gauge , column charts which are available in the tool .

ANALYSIS -

The dataset contains the following columns .

Request id – 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 request whether it was completed or not .

Driver id – gives the id of the driver who got the request if car are available .

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

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

We split the time stamp column into date and time separately and apply the conditional formatting on it and add a column of time of the day ., also trip column to understand whether the trip is successful or not .

• According to the visualisation that we make tells us that the city customer is more than the airport .
• In the graph of request id by pickup point and status , the cancelled request is more in city respect to the airport .
• Most of Cars are not available for airport than city .
• Most of the trip are completed in city .

In the graph of request id by time of the day

• Most of the request by the customer is in the EVENING for cab service and least in early morning .
• Most of the car are not available in evening time .
• At noon time most of the trip are completed .

• There are 2.83k ie. 41.97% trip are completed , 2.65k 39.29% cars are not available at time , 18.74 % request are cancelled .

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Insights –

A high gap can be seen around 58.03% due to cars are not available on time and many of the requests are cancelled .

Supply is only around 41.97% because some trips are completed only .

RECCOMENDATIONS -

• The increase in the number of vehicles can be one of the solutions .
• Each and every cab service person should not cancelled the request whether some time delay may occur ,delay is not the problem but cancelled the request is the problem .

CONCLUSION –

Based on the data analysis performed , visualisation is made , and we know the gap between supply and demand .

Uber can increase the salary or give incentives based upon the 6 month time period according to his work that they complete his / her trip or not .