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from mlxtend.frequent_patterns import apriori from mlxtend.frequent_patterns import association_rules import pandas as pd # Sample dataset data = {'TransactionID': [1, 2, 3, 4, 5], 'Items': [['A', 'B', 'D'], ['B', 'C'], ['A', 'B', 'E'], ['B', 'E'], ['A', 'C']] } df = pd.DataFrame(data) # Convert the items column to a one-hot encoded format oht = pd.Series(df['Items']).str.join('|').str.get_dummies() frequent_itemsets = apriori(oht, min_support=0.2, use_colnames=True) # Generate the association rules rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7) print("Frequent itemsets:") print(frequent_itemsets) print("\nAssociation rules:") print(rules)
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