This case study focuses on analysing the business problem that the cab company faces and suggest possible solutions.

Description - Masked data set containing Uber cab request details

Source - Assignment Format - .csv (comma seperated values)

Number of rows - 6745

Number of cols - 6

Each row has - Uber cab request detail

Sampling method - Data is for 5 days (Days 11 through 15 in the month of June for the year 2016)

Column Details

Request.id - Type: Number | A unique identifier of the request Missing : 0 | Uniques : 6745

Request.timestamp- Date timestamp | The date and time at which the customer made the trip request Missing : 0 | Uniques : 5618

Drop.timestamp - Date timestamp | The drop-off date and time, in case the trip was completed Missing : 3914 | Uniques : 2598 Pickup.point - Character | The point from which the request was made Missing : 0 | Uniques : 2

Driver.id - Number | The unique identifier of the driver Missing : 2650 | Uniques : 301 Status - Character | The final status of the trip, that can be either completed, cancelled by the driver or no cars available Missing : 0 | Uniques : 3

Below steps were followed to understand and derive insights. All the steps folowed are in the R file available in the repository.

Data Cleanup

Deriving new variables

GGplot to visualize

Univariate and Segmented analysis

The detailed analysis on the demand supply gap, issues, possible causes and the recommended solution are all available in the PDF file in this repository