Project Overview
This project applies the Apriori and PCY algorithms to identify frequent itemsets and generate association rules for market basket analysis. Using the Kaggle Groceries Dataset, customer purchasing behaviors are explored to provide actionable insights.
Objectives
- Implement Apriori and PCY algorithms to discover frequent itemsets.
- Generate association rules with thresholds for support and confidence.
- Compare the performance of Apriori and PCY algorithms.
Key Outcomes
- Frequent itemsets and association rules were successfully extracted.
- Support, confidence, and lift metrics were used to evaluate rules.
- PCY algorithm demonstrated higher efficiency with large datasets.
Tools and Libraries
- Python
- NumPy and Pandas
- Matplotlib and Seaborn
- MLxtend
View the Code
Click the link below to view the full code and documentation for this project on GitHub:
View on GitHub