Predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company.
The data are a scaled down version of the full database generously donated by an anonymous wireless telephone company. There are 7043 customers in the database, and 20 potential predictors. The data are available in one data file with 7043 rows that combines the calibration and validation customers.
ML algorithm used for Churn Prediction -
- DNN
- Logistics Regression
- XGBoost Algorithm
- LGBM Algorithm
- CATBoost Algorithm