Association Rule Mining is an unsupervised machine learning technique used to find hidden rules in data. Apriori is one of the famous algorithms for the same. More information about it can be found here.
You can learn more about association rule mining in the below video.
You can learn more about Apriori algorithm using below video.
The below code will help to find rules in any given transaction data.
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# Library installtion for apriori # !pip install apyori # Sample code to do Apriori in Python import apyori # Creating Sample Transactions transactions = [ ['Milk', 'Bread', 'Saffron'], ['Milk', 'Saffron'], ['Bread', 'Saffron','Wafer'], ['Bread','Wafer'], ] # Generating association rules Rules = list(apyori.apriori(transactions, min_support=0.5, min_confidence=0.5)) # Extracting rules from the object for i in range(len(Rules)): LHS=list(Rules[i][2][0][0]) RHS=list(Rules[i][2][0][1]) support=Rules[i][1] confidence=Rules[i][2][0][2] lift=Rules[i][2][0][3] print("LHS:",LHS,"--","RHS:",RHS) print("Support:",support) print("Confidence:",confidence) print("Lift:",lift) print(10*"----") |
Sample Output
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