To develop a robust and interpretable churn prediction model that can be integrated into Ola’s operations. This model will allow the company to identify at-risk drivers early and implement personalized retention strategies, ultimately improving driver satisfaction and reducing operational costs. This project aims to serve as a data-driven solution to address the ongoing churn problem in the ride-hailing industry while providing actionable insights for sustainable workforce management
-You can access the complete project python file here - Python -You can access the complete project in pdf format here - Report
This study focuses on predicting driver churn based on a variety of driver attributes and operational data. By leveraging historical data from 2019 and 2020 for a segment of drivers, the goal is to build a predictive model to classify drivers into two categories: those likely to leave (churn) and those likely to stay.
| Feature | Description |
|---|---|
| MMMM-YY | Reporting Date (Monthly) |
| Driver_ID | Unique id for drivers |
| Age | Age of the driver |
| Gender | Gender of the driver – Male : 0, Female: 1 |
| City | City Code of the driver |
| Education_Level | Education level – 0 for 10+ ,1 for 12+ ,2 for graduate |
| Income | Monthly average Income of the driver |
| Country | Name of the country where each customer resides. |
| Date Of Joining | Joining date for the driver |
| LastWorkingDate | Last date of working for the driver |
| Joining Designation | Designation of the driver at the time of joining |
| Grade | Grade of the driver at the time of reporting |
| Total Business Value | The total business value acquired by the driver in a month (negative business indicates cancellation/refund or car EMI adjustments) |
| Quarterly Rating | Quarterly rating of the driver: 1,2,3,4,5 (higher is better) |
-The bagging as well as boosting algorithms have performed well with a good resting accuracy. -These models could be used to predict the churn of drivers in a future percpective. -Promotional offers like reduction in commission rate etc could be provided to the drivers to prevent them from leaving. -From the feature importance, Tenure, Total Business value, Rating increment, and Age are found to be most important is deciding the churn rate of drivers from Ola. -Drivers could be classified based on the above matrices to coin targeted promotional features which will help bring down the churn rate.