Analyze & Build a Churn Prediction Model in R

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

• Analyze and prepare customer data for churn prediction models in R
• Build and evaluate machine learning models using logistic regression and trees
• Interpret model results to support data-driven business decisions

• No deep prior experience is required, but basic computer and internet skills are helpful
• Ability to read course instructions in English and complete short practice activities