Apriori algorithm works on the principle of Association Rule Mining.
In this project, it is implemented in python in jupyter.
Theory of Apriori Algorithm There are 3 major components of Apriori algorithm.
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Support Frequency of occurence of a itemset. Support (A) = (Transactions containing (A)) / (Total Transactions)
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Confidence It refers to the likelihood that an item B is also bought if item A is bought.
Confidence(A->B) = (Transactions containing both (A and B)) / (Transactions containing (A))
- Lift Lift refers to the increase in the ratio of the sale of B when A is sold.
Lift = (Confidence (A->B))/(Support (B))
Association rule by Lift lift = 1 -> There is no association between A and B
lift < 1 -> A and B are unlikely to be bought together
lift > 1 -> greater the lift greater is the likelihood of buying both products together.
#Cross_sell detection for e_commerce recommender system. ->find group_by transaction_id and item_id ->merge the group_by with Apriori rules output with Left_on="item_id" and right_on="antecedents"