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 telecom data into a machine-learning-ready format, apply K-Nearest Neighbors preprocessing logic, and structure datasets for unbiased model evaluation. Through guided, practical lessons, learners practice removing irrelevant variables, creating and reducing dummy variables, and splitting datasets for training and testing. What makes this course unique is its end-to-end, practice-driven approach to churn prediction using R, with clear alignment between data preprocessing decisions and their impact on predictive performance. Designed for aspiring data analysts and machine learning beginners, this course bridges theory and applied analytics, enabling learners to confidently prepare telecom datasets for customer churn modeling in real-world scenarios.