This repo contains the Market Basket Analysis (MBA) project as part of my Data Mining course project.
Market basket analysis is a data mining technique used by retailers to increase sales by better understanding customer purchasing patterns. It involves analyzing large data sets, such as purchase history, to reveal product groupings, as well as products that are likely to be purchased together.
This project is design to find the most frequent pattern or combinations of items using Market Basket Analysis (MBA). It is based on developing an efficient model that performs the best available frequent pattern between items, consume less memory and time. The project uses file (e.g. csv, xlsx) as dataset. This dataset of customer transactions each consists of items purchased by a customer in a visit. At this project used an efficient algorithm FP-Growth Algorithm that generates all significant association rules between items in the dataset. Firstly we preprocessing the dataset since it may contain error or other inconsistency and then show visual analysis on that. Afterward the frequent item sets are mined from database using the fpgrowth algorithm and then the association rules are generated. The project is beneficial for retailer to determine the relationship between the items that are purchased by their customers. Also it help retailer to develop marketing strategies such as which items frequently brought by customer, what are the trending items that customer buy most etc.
Besides fpgrowth algorithm also used Apriori algorithm to check the difference.
The dataset is publicly available from the Kaggle website.
