By the end of this course, learners will be able to analyze customer data, prepare datasets for machine learning, build churn prediction models using R, and evaluate model performance using industry-standard techniques. Learners will also gain the ability to interpret model outputs and apply insights to real-world business decision-making.

This course is designed to provide a practical, end-to-end understanding of churn prediction using machine learning in R Studio. Starting with foundational concepts such as data types and exploratory data analysis, the course progressively guides learners through dataset understanding, data preprocessing, and model selection. Hands-on lessons focus on implementing logistic regression, handling missing values, transforming data, and evaluating models using accuracy metrics, ROC curves, and decision trees. Learners benefit from a structured, project-based approach that mirrors real-world data science workflows. Unlike theory-heavy courses, this program emphasizes applied learning with step-by-step R code demonstrations and business-focused interpretation of results. The course is ideal for students, analysts, and professionals seeking to develop practical machine learning skills while understanding how churn prediction delivers measurable value across industries.