MetroCar-PerformanceOptimization

Executive Summary

The MetroCar project focuses on analyzing user interactions within the MetroCar ride-hailing platform, evaluating critical areas for improvement, including user signups, ride requests, cancellations, and driver behaviors. The aim is to identify bottlenecks in the user funnel and enhance both driver and rider satisfaction through data-driven insights.

Objectives

Analyze key metrics from MetroCar, including user ride requests, cancellations, and ratings. Evaluate the efficiency of the platform's service, identifying critical drop-off points. Provide actionable insights to improve service performance and user retention.

Methodology

This project utilizes SQL and Python to extract and analyze data from various MetroCar datasets, including ride requests, transactions, and reviews. Key steps include:

Extracting data from a PostgreSQL database. Performing data cleaning and processing with Pandas. Conducting visualizations to highlight trends in user behavior. Analyzing relationships between peak hours and cancellation rates.

Key Findings

Key Insights	Action Plans
Supply-Demand Gap: The supply of ride service during peak hours is 75% less than the demand.	Implement surge pricing to attract more drivers during rush hours and capitalize on high demand.
Incomplete User Data: The forms at signup have missing information about users' age range.	Investigate and revise the signup process to include age range data, reducing the "unknown" group.
Android User Acquisition: Opportunity to increase Android app downloads and user sign-ups.	Enhance Android’s App Store Optimization (ASO) and offer incentives like free rides and discounts to boost downloads and sign-ups.

Google Colab Notebook

Access the Google Colab notebook for the MetroCar project here .

Group Presentation Video

Watch the project presentation video here .