This repository contains a Market Basket Analysis project implemented using the Apriori algorithm. Market Basket Analysis is a technique used to discover relationships and patterns between items purchased by customers. It helps businesses understand customer behavior and make informed decisions regarding product placement, promotions, and recommendations.

The dataset used for this project is a transaction dataset containing records of items purchased by customers. The dataset is provided in the dataset directory.

The Apriori algorithm is a popular association rule mining technique used to find frequent itemsets and generate association rules. It's a fundamental component of Market Basket Analysis. The Apriori algorithm implementation can be found in the apriori.py script in the src directory.

Using the Apriori algorithm, we discovered valuable association rules in the transaction dataset, enabling insights into customer purchase patterns. These rules provide actionable information for product recommendations, store layout optimization, and targeted marketing strategies.

https://github.com/Neo28A/MarketBasketAnalysis/tree/main/MarketBasketAnalysis

If you'd like to contribute to this project, please open an issue or submit a pull request. We welcome your suggestions, improvements, and collaboration.

• Special thanks to the creators of the Apriori algorithm and the data source used for this project.