The project provides the Apriori algorithm and Market Basket Analysis (MBA) to analyze transactional data, generating personalized recommendations based on Support, Confidence, and Lift metrics to enhance customer experience and boost sales.
Market Basket Analysis (MBA) is a powerful data mining technique used by retailers to gain insights into customer buying patterns. Market Basket Analysis is a data mining approach that helps retailers understand which products customers frequently buy together. By analyzing large datasets, such as purchase history, it uncovers item groupings and products that are likely to be purchased in the same transaction. The goal is to identify associations between items and improve sales strategies.
The primary objective is to find items that buyers desire to purchase together. It aids sales and marketing teams in developing effective tactics for product placement, pricing, cross-selling, and up-selling.
- Association Mining Rules: The foundation of market basket analysis. Utilized by algorithms such as Apriori, AIS, and SETM.
- Apriori Algorithm: Most frequently used MBA algorithm.
It uses association rule mining with an IF, THEN construct.
Example of association rule: “Bread” ➡️ “Butter.”
Definitions:
Antecedent: The “IF” element (e.g., bread).
Consequent: The item(s) encountered along with the antecedent (e.g., butter).
Market basket analysis empowers retailers to optimize their offerings, enhance customer experiences, and drive sales growth. 🛒📊