Market Basket Analysis Or Association Rule Mining In Python

Market Basket Analysis using FP-Growth Algorithm

This video demonstrates a practical implementation of Market Basket Analysis (Association Rule Mining) using the FP-Growth algorithm in Python. The process includes data loading, cleaning, and generating association rules to uncover meaningful patterns in sales transactions.

Key Steps:
Loading and exploring sales dataset
Cleaning and filtering relevant information
Transforming data for FP-Growth algorithm
Setting parameters for itemset frequency
Generating association rules with specified thresholds
Presenting and sorting impactful rules

Results:
Identify associations among frequently purchased items
Discover high-confidence rules with positive correlation
Fine-tune analysis with adjustable confidence and lift thresholds

Insights:
Efficient use of FP-Growth for frequent itemset mining
Determination of significant association rules with minimum confidence and lift
Presentation of final results with antecedents, consequents, confidence, and lift

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