This project is a machine learning-based approach to predict customer churn for a telecom company. The project aims to help the company identify customers who are likely to churn and take proactive measures to retain them.
- 3.1 Churn prediction project
- 3.2 Data preparation
- 3.3 Setting up the validation framework
- 3.4 EDA
- 3.5 Feature importance: Churn rate and risk ratio
- 3.6 Feature importance: Mutual information
- 3.7 Feature importance: Correlation
- 3.8 One-hot encoding
- 3.9 Logistic regression
- 3.10 Training logistic regression with Scikit-Learn
- 3.11 Model interpretation
- 3.12 Using the model