Jeeny Ride-Hailing Demand & Supply Analysis – GTP 2025 Case Study

In this project, I analyzed demand and supply data for the ride-hailing platform Jeeny to identify coverage gaps between passengers and drivers over multiple weeks, with a focus on peak hours.

Key steps included:

Cleaning and merging datasets (demand and supply) using Python.

Analyzing demand–supply patterns across hours and days of the week to detect critical undersupply hours.

Identifying time gaps where demand exceeds supply, especially during evenings and weekends.

Calculating additional online driver hours required to fulfill unserved ride requests.

Building an interactive Power BI dashboard featuring:

24-hour demand vs. supply curve

Weekly undersupply view

Seasonal demand–supply trends

Recommendations to improve driver availability and reduce unfulfilled requests

Outcome:

The analysis revealed consistent evening coverage gaps between 4 PM and 11 PM, particularly on Fridays and Saturdays, highlighting the need for increased driver availability during these periods to enhance fulfillment rates.