CarRentalCaseStudy _{Data Science Project on systematically solutions using EDA and data visualisation.}

Business Understanding

A popular Car Rental company in an Urban city of India. Primiarly used by customer who wants to shuffle between the airport and city. However, high number of customers face the problem of cancellation by the driver or non-availability of cars. This is not just the customer's concern, as these very issues also impact the business of the car rental company. If drivers cancel the request of riders or if cars are unavailable, the car rental company loses out on its revenue.

Project Goal

The analyze and identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of analysis, present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.

Project Execution

Data Understanding

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

• Request id: A unique identifier of the request
• Time of request: The date and time at which the customer made the trip request
• Drop-off time: The drop-off date and time, in case the trip was completed
• Pick-up point: The point from which the request was made
• Driver id: The unique identification number of the driver
• Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available

Note: For this assignment, only the trips to and from the airport are being considered.

Data Cleaning and Preparation

• Identify the data quality issues and clean the data so that you can use it for analysis.
• Ensure that the dates and time are in the proper format. Derive new variables which will be useful for analysis.

Data Analysis

• Visually identify the most pressing problems for Uber. Created plots to visualise the frequency of requests that get cancelled or show 'no cars available'; identify the most problematic types of requests (city to airport / airport to city etc.) and the time slots (early mornings, late evenings etc.) using plots
• Find out the gap between supply and demand and show the same using plots.
• Find the time slots when the highest gap exists
• Find the types of requests (city-airport or airport-city) for which the gap is the most severe in the identified time slots

Recommendations

For details please refer to the PDF. Below is the high level
• For the trips in the morning, drivers can be incentivised to make those trips.
• For the evening, since the number of drivers is less, some of the ways are:

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