Uber Technologies, Inc. (Uber) is an American mobility as a service provider. EDA and data visualisation is used to systematically study the gap between demand and supply of Uber cabs running between the city and the airport and address the problem Uber is facing - driver cancellation and non-availability of cars leading to loss of potential revenue.
Some of the problems faced by customers who travel to and from the airport are cancellation of trip by the driver or non-availability of cars. 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.
As an analyst, we decide to address the problem Uber is facing - driver cancellation and non-availability of cars leading to loss of potential revenue.
The aim of analysis is to 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 the analysis, we present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.
The data set is a masked data set which is similar to what data analysts at Uber handle. 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: Only the trips to and from the airport are being considered.