Analyze & Build a Churn Prediction Model in R

Overview

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.

Syllabus

Foundations of Churn Prediction

This module introduces the fundamentals of churn prediction in machine learning, covering core data concepts, exploratory analysis, real-world business applications, and an overview of datasets and modeling approaches used to predict customer churn effectively.

Building & Evaluating the Churn Model in R

This module focuses on the practical implementation of a churn prediction model using R Studio, including environment setup, data cleaning and transformation, model development, and performance evaluation using industry-standard techniques.