Author: Kashish Project Type: Exploratory Data Analysis (EDA)
This analysis dives into the operational inefficiencies of Uber rides, specifically examining the route between the City and the Airport. The goal is to uncover why a large percentage of ride requests remain unfulfilled and to propose data-backed strategies to bridge this gap.
The dataset reveals a staggering 72% unfulfilled request rate on the City-Airport route. This translates to massive revenue loss and a poor customer experience, primarily driven by:
- High rate of driver Cancellations.
- Severe "No Cars Available" situations at critical hours.
- The Morning Bottleneck (5 AM - 9 AM): * Trend: High volume of requests from City to Airport.
- Issue: Drivers actively cancel these trips fearing "dead mileage" on the return journey.
- The Evening Bottleneck (5 PM - 9 PM): * Trend: High volume of incoming flights leading to massive requests from Airport to City.
- Issue: A severe shortage of cabs physically present at the airport (No Cars Available).
To mitigate these operational gaps, the following interventions are suggested:
- Morning Peak: Introduce targeted financial incentives or higher commissions for city-to-airport drops to compensate for potential empty return trips.
- Evening Peak: Implement dynamic "Rush Hour" pricing for trips originating from the airport to attract available city drivers.
- Algorithmic Shift: Prioritize drivers completing airport drops for immediate return pickups.
Uber_EDA_Kashish.ipynb: The main Jupyter Notebook containing data wrangling, python code, and all visualizations.Uber_Insights_Presentation.pdf: A concise pitch deck summarizing the problem and proposed solutions.
Built with Python, Pandas, Matplotlib & Seaborn.