- From the dataset of an online retail store, frequent itemsets that were bought by the customers were identified using Apriori Algorithm
- These frequent itemsets are the ones which have at least minimum support threshold values.
- These frequent itemsets were then used to form association rules using Association Rule Mining
- Each corresponding association rule (pair of antecedent-consequent) were having support, confidence and lift values.
- These assoctaion rules can be used to increase sells of one product using popularity of other associated product.
- These rules can also be utilized in making combination of associated products or giving promotional offers and discounts on associated products.
- Dataset is huge in size ( > 25 MB) hence not uploaded, it is available in Kaggle