Market Basket Analysis using R

Market Basket Analysis using Association Rule Mining (Apriori Algorithm) to determine goods that are frequently bought together.

The data set used is publicly available on Kaggle.

(See the Report)

Association Rules

• If-then statements that helps us to show the probability of relationships between data items withn large data sets

• Eg

  • Movie Recommendation: If they watched this, then they also watched this.
  • Market Basket Optimisation : If they bought this, then they also bought this

• Has two parts:

  1. An antecedent (if) - An item found within the data
  2. A consequent (then) - An item found in combination with the data

• Note that implication here is co-occurrence and not causality

• There are three metrics that help us understand the strength of the relationship (rule):

  1. Support -indicates how frequently the itemset occurs

  2. Confidence - the number of times the rule is found to be true (i.e. likelines of occurrence of consequent given the the antecedent has occurred)

  3. Lift - How many times the rule is expected to be found true

     • Ratio of confidence to the baseline probability of occurrence of the consequent
     • Generally:
       • A value of lift greater than 1 vouches for high association i.e. having $M_1$ on the itemset increases the chances of having $M_2$ on the itemset
       • If the value is positive, there is a positive correlation
       • If the value is negative, there is a negative correlation
       • If the value is 1, there is no correlation

Reference

https://towardsdatascience.com/association-rules-2-aa9a77241654

https://searchbusinessanalytics.techtarget.com/definition/association-rules-in-data-mining

https://datascienceplus.com/visualize-market-basket-analysis-in-r/

https://www.datacamp.com/community/tutorials/market-basket-analysis-r