Market basket analysis is a data science technique used by companies to identify items that are frequently purchased together. This script uses the Apriori algorithm to analyze a grocery sales dataset to discover associations between different items.
Steps: Data preparation: Load and clean the data, creating a column for single transactions per customer per day. Basket creation: Generate a cross-tabulation (basket) to represent the frequency of items in each transaction. Apriori algorithm: Apply the Apriori algorithm to discover frequent itemsets. Association rules: Analyze association rules using Zhang's metric for evaluation. Visualization: Visualize product associations using heatmaps.
Benefits: Heatmaps: Provide clear and intuitive visualizations of product associations. Zhang's metric: Offers a comprehensive evaluation of association rules, considering the negative values.
Outcome: This script helps discover hidden patterns in customer purchase behavior, enabling better product placement, marketing strategies, and inventory management.