- Built a generalized rule inspection pipeline to parse Apriori output into a structured DataFrame, supporting multi-condition filtering by support, confidence, lift, and item name across 74 extracted rules.
- Designed a weighted scoring function using MinMaxScaler-normalized support, confidence, and lift to rank rules beyond default metrics, surfacing associations not captured by lift alone.
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Association Rule Mining
Market Basket Analysis using Apriori
- Machine Learning
- Apriori
- Python