Market Basket Analysis Using Association Rule

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.

Association Rules are widely used to analyze retail basket or transaction data, and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules. The outcome of this type of technique is, in simple terms, a set of rules that can be understood as “if this, then that”.

Association rule learning:- is a rule-based machine learning method for discovering interesting relations between variables in large databases.

Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale systems in supermarkets ,found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements.

Support :- You will understand support with an example,Let say i have a shop

i got 6 transaction,

1 transaction:-Apple,Banana,Orange,Juice Maker.

2 transaction:-Apple,Juice Maker.

3 transaction:-Apple.

4 transaction:-Bread,Butter.

5 transaction:-Toast,Milk.

We have to find Support of Apple:- no. of time apple/no. of transaction=3/5=0.6, 60% transaction are of apple’s.

Confidence:- Confidence is an indication of how often the rule has been found to be true.

Confidence of Apple and Juice Maker= no. of time Apple and Juice Maker/Apple=2/3=0.88% probability to buy both item.

Lift:

Lift is the ratio between the confidence and support.

Lift says how likely item Y is purchased when item X is purchased, while controlling for how popular item Y is.

Lift of Apple->Juice Maker= Support of (Apple,Juice Maker)/Support of(Apple) * Support of(Juice Maker).

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