This project provides a comprehensive analysis of Ola's ride-sharing data to improve operational efficiency and customer satisfaction. By analyzing booking patterns, cancellation reasons, and revenue metrics, this project offers data-driven solutions for urban mobility challenges.
The project demonstrates a full data lifecycle: SQL-based extraction, Streamlit-driven interface, and Power BI visual storytelling.
- SQL: PostgreSQL (Core business logic and view creation)
- Python: Streamlit & Pandas (Web application and data manipulation)
- BI Tool: Power BI (Interactive reporting dashboards)
- Documentation: Microsoft Word (Technical documentation)
ola_bookings.csv: The primary data source containing ride-level details.ola_queries.sql: Contains 10+ professional SQL queries used for data extraction and business KPI monitoring.ola_dashboard.py: A Streamlit web application that allows users to run SQL queries and view analysis results interactively.ola powerbi.pbix: The master Power BI file containing advanced data visualizations.OLA project output.docx: Technical document containing the final analysis outputs and query results.OLA RIDE.docx: Project guidelines and problem statement details.
The analysis focused on solving specific business problems:
- Cancellation Analysis: Identified that "Driver not moving towards pickup" is the leading cause for customer-side cancellations.
- Revenue Performance: Calculated the total booking value for successful rides and segmented it by vehicle type.
- Payment Behavior: Analyzed the popularity of UPI vs. Cash payments in the ride-sharing ecosystem.
- Customer Satisfaction: Derived average ratings for different vehicle segments (Prime Sedan, SUV, etc.) to evaluate service quality.
Execute the scripts in ola_queries.sql within your SQL environment to generate the necessary views for "Successful Bookings," "Top 5 Customers," and "Revenue Trends."
The ola_dashboard.py script provides a user interface to interact with the SQL data. Run it locally using:
pip install streamlit pandas psycopg2
streamlit run ola_dashboard.py