To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
In this project, we will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.
Models used: Logistic Regression, SVMs and Random Forests.
The study also demonstrates the usage of KNN Imputation methods and SMOTE techniques for balancing data,