Analyze and Predict Card Purchases Using R

Overview

By the end of this course, learners will be able to analyze customer data, evaluate predictive features, build and optimize classification models, and assess model performance to accurately predict card purchase behavior using R. Learners will develop practical skills in logistic regression and decision tree modeling while applying industry-relevant evaluation techniques. This hands-on, project-based course guides learners through a complete predictive modeling workflow using a real-world card purchase use case. Starting with data import and feature assessment using Information Value, learners progress through visualization, data preparation, and model development. The course emphasizes model evaluation through lift charts, ROC analysis, and testing on unseen data, ensuring learners understand not just how to build models, but how to validate and trust them. Learners also gain experience saving and reusing trained models, a critical skill for real-world deployment. What makes this course unique is its strong focus on practical decision-making, model interpretability, and end-to-end implementation in R. By completing this course, learners strengthen their analytical thinking and gain job-ready skills applicable to roles such as data analyst, marketing analyst, and risk analyst.

Syllabus

Building the Foundation for Purchase Prediction

This module introduces learners to the end-to-end process of preparing data for card purchase prediction using R, including dataset import, feature evaluation with Information Value, exploratory visualization, data splitting, and building an optimized logistic regression model for binary classification.

Model Evaluation, Deployment, and Tree-Based Learning

This module focuses on evaluating and validating predictive models using lift charts and performance metrics, testing models on unseen data, saving trained models in R, and implementing decision tree models to compare and enhance card purchase prediction results.