Market Basket Analysis (MBA) is a data mining technique used to discover patterns in transaction datasets — specifically, which items are frequently purchased together.
In this project, we use the Apriori algorithm to extract frequent itemsets and generate association rules that describe relationships between products.
These insights are valuable for retail analytics, cross-selling strategies, and product placement optimization.
git clone https://github.com/SelvamathanS/Market-basket-analysis-using-apriori-algorithm-association-rule-.git
cd Market-basket-analysis-using-apriori-algorithm-association-rule-Launch Jupyter Notebook:
jupyter notebookOpen and run Association_rule_mining.ipynb.
| Metric | Meaning |
|---|---|
| Support | How often an itemset appears in the dataset. |
| Confidence | Likelihood of purchasing item B if item A is bought. |
| Lift | Measures how much more likely item B is bought with A compared to random chance. |