Apply R Techniques for Telecom Customer Churn Prediction

Learners will be able to prepare telecom customer data, apply feature engineering techniques, and build a structured dataset for churn prediction using R. By completing this course, learners gain practical skills in encoding categorical variables, scaling numerical features, selecting optimal model parameters, and organizing datasets for machine learning workflows. This course helps learners develop hands-on experience with real-world telecom churn prediction challenges, focusing on data preparation steps that directly impact model accuracy. Learners will understand how to transform raw telec…