Ola Performance Analysis Project

Overview

This project provides insights into Ola's ride performance through data analysis using MySQL, Power BI, and Excel. The dataset, created with the assistance of ChatGPT, contains over 100,000 rows and 19 columns, allowing for a comprehensive examination of ride bookings, cancellations, customer ratings, and driver performance.

Dataset

• Total Rows: 100,000+
• Total Columns: 19
• Source: Generated using ChatGPT (virtual dataset, no traditional license)

Note: As this dataset is synthetic and generated for analysis purposes, it does not have a formal license. Feel free to use it for educational and analytical purposes.

Tools Used

• MySQL: For data querying and analysis
• Power BI: For visualizing data and creating interactive dashboards
• Excel: For data manipulation and additional analysis

Key Insights

SQL Analysis Questions

1. Retrieve all successful bookings.
2. Find the average ride distance for each vehicle type.
3. Get the total number of cancelled rides by customers.
4. List the top 5 customers who booked the highest number of rides.
5. Get the number of rides cancelled by drivers due to personal and car-related issues.
6. Find the maximum and minimum driver ratings for Prime Sedan bookings.
7. Retrieve all rides where payment was made using UPI.
8. Find the average customer rating per vehicle type.
9. Calculate the total booking value of rides completed successfully.
10. List all incomplete rides along with the reason.

Power BI Analysis Questions

1. Ride Volume Over Time.
2. Booking Status Breakdown.
3. Top 5 Vehicle Types by Ride Distance.
4. Average Customer Ratings by Vehicle Type.
5. Reasons for Cancelled Rides.
6. Revenue by Payment Method.
7. Top 5 Customers by Total Booking Value.
8. Ride Distance Distribution Per Day.
9. Driver Ratings Distribution.
10. Customer vs. Driver Ratings.

Insights Generated

From the analysis conducted using MySQL, Power BI, and Excel, the following key insights were derived:

1. Successful Bookings: A comprehensive overview of successful ride bookings, helping to understand overall performance.
2. Average Ride Distance: Insights into the average ride distance for different vehicle types, indicating customer preferences.
3. Cancellation Trends: Analysis of cancellation rates by customers and reasons for cancellations, aiding in identifying areas for improvement.
4. Top Customers: Identification of the top 5 customers who booked the most rides, which can inform targeted marketing strategies.
5. Driver Performance: Evaluation of driver ratings and performance metrics, enabling better driver management.
6. Payment Method Usage: Breakdown of payment methods used, highlighting trends in customer payment preferences.
7. Revenue Insights: Analysis of total booking values and revenue generated, providing financial insights for the business.
8. Customer vs. Driver Ratings: Comparison of customer and driver ratings, offering insights into service quality from both perspectives.

These insights can guide decision-making and strategy formulation for improving service delivery and customer satisfaction.

Conclusion

This project demonstrates the ability to derive valuable insights from large datasets using various analytical tools. The findings can assist stakeholders in making informed decisions to improve service quality and customer satisfaction.

Getting Started

To explore the project:

1. Clone this repository.
2. Open the dataset in your preferred tool (MySQL, Power BI, or Excel).
3. Review the insights and visualizations created.

License

As this dataset is synthetic and generated for analysis purposes, it does not have a formal license. Feel free to use it for educational and analytical purposes.

Acknowledgments

• Special thanks to ChatGPT for assisting in generating the dataset.