Description of Invention
Modern consumers frequently make their purchases and utilize their services from platforms including Lazada, Shopee, and Amazon. Users benefit from such platforms because they can simultaneously compare prices together with quality ratings and item reviews across multiple e-commerce sites. Customer purchase prediction has become vital for businesses to remain competitive and boost their profits. Data mining tools discover valuable patterns hidden within voluminous data collections. A data mining technique called Market Basket Analysis (MBA enables the detection of product relationships that customers typically buy together. Association rules help identify regular combinations of frequently purchased merchandise. Association rules require implementation of three main algorithms: Apriori Algorithm together with FP-Tree Algorithm or ECLAT Algorithm. The project intends to apply association rules for both determining and predicting the shopping patterns customers use to pick their products. This investigation uses quantitative research methods to evaluate purchased item pattern through its chosen research methodology. For purchase pattern identification, the research will analyze the InstaCart dataset, which operates as an e-commerce platform