Market Basket Analysis
Market basket analysis is all about finding these relationships in a store or online shop. It is a data mining technique that helps stores to discover the association between products or services that are frequently purchased together by the customers.
Market Basket analysis in various industries
Retail & E-commerce: Placement of products together, bundling offers, cross-selling & up-selling, discounting
Healthcare: Co-occurrence of diseases and to identify risk factors
Edu-tech: Courses that get purchased together, profiling learners into categories
Banking: Financial products (e.g. insurance, etc.) to offer personalization in services
Streaming Media: Movies/Series recommendations based on previously watched titles
Here are the steps to conduct Market Basket Analysis
Step 1: Collect Data
Step 2: Find Patterns
The Apriori algorithm is a widely used model for Market Basket Analysis. It works on the principle of association rule mining, where the algorithm tries to find the frequent itemsets and generates the association rules.
The frequent itemsets are the sets of items that frequently occur together in the transactions.
The association rules are the rules that show the relationship between the items based on the frequency of occurrence.
Here is a sample Python code to perform Market Basket Analysis using the Apriori algorithm.
The three most relevant evaluation metrics used in Market Basket Analysis are support, confidence, and lift.
FREQUENTLY ASKED QUESTIONS (FAQs)
Q. What is the minimum support value to consider an itemset as frequent?
A. The minimum support value depends on the dataset and the business problem. Generally, a minimum support value of 0.1 to 0.5 is considered.
Q. How to interpret the association rules in Market Basket Analysis?
A. The association rules can be interpreted based on the evaluation metrics such as support, confidence, and lift. For example, if the lift value is greater than 1, it indicates a positive association between the antecedent and consequent items.
Q. Can Market Basket Analysis be used for customer segmentation?
A. Yes, it can be used for customer segmentation by clustering the customers based on their purchasing behavior.
Q: What are some challenges in applying market basket analysis to large datasets?
A: Market basket analysis can be computationally intensive and time-consuming when applied to large datasets. This can be addressed by using parallel processing or distributed computing techniques, or by sampling the data to reduce the computational load.