Association Rule Mining for Market Basket Analysis on Online Retail Store

The rise of association rule mining has been attributed to the increasing amount of transactional data that has been collected and analyzed. This technique is commonly used to identify relationships and patterns in this data. This paper aims to provide an overview of the various aspects of this process and its potential applications. The literature review on association rule mining was conducted to analyze its applications in various industries. A case study was then carried out to evaluate its effectiveness in analyzing bank loan data. The findings of the study revealed that this process can be utilized to identify meaningful relationships and patterns in the data and produce recommendations and predictions. Due to the complexity of the process, it is often difficult to implement effective association rule mining techniques. This paper aims to provide a comprehensive overview of the various aspects of this process and its potential applications. In addition, it also explores the influence of certain parameters on the performance of the algorithms. The study's findings provide valuable insight into association rule mining's application in analyzing bank loan data, and it highlights the opportunities and challenges that this process presents. The conclusions of the research will be beneficial for data mining researchers and practitioners, and will help them understand more about this process and its possible applications.