By the end of this course, learners will be able to analyze transactional datasets, calculate and adjust support thresholds, generate and interpret association rules, clean real-world grocery data, and apply advanced algorithms such as Eclat to uncover meaningful purchasing patterns using R.

This hands-on project-based course guides learners step by step through the complete Market Basket Analysis workflow. Starting with loading and understanding transactional data, learners progress to calculating minimum support, training association rule models, visualizing rules, and optimizing results through parameter tuning. The course then shifts to practical data preparation using a real grocery dataset, emphasizing duplicate removal, co-purchase analysis, and efficient frequent itemset mining. What makes this course unique is its strong focus on applied learning using authentic datasets and industry-relevant techniques. Rather than emphasizing theory alone, learners gain practical experience implementing Market Basket Analysis end to end in R, mirroring real analytical tasks performed in retail analytics, recommendation systems, and customer behavior analysis. By completing this course, learners build job-ready skills in association rule mining, data preprocessing, and exploratory analysis—capabilities directly applicable to data analytics, data science, and business intelligence roles.