E-Commerce Website Usability Analysis Using the Association Rule Mining and Machine Learning Algorithm

The overall effectiveness of a website as an e-commerce platform is influenced by how usable it is. This study aimed to find out if advanced web metrics, derived from Google Analytics software, could be used evaluate the usability sites and identify potential issues. It simple gather indicators, but processing interpretation take time. data produced through several digital channels, including mobile. Big has proven very helpful in variety online platforms, social networking websites, etc. sheer amount that needs processed assessed useful one main issues with today result revolution. Additionally, on media crucial growth strategy for usage BDA capabilities guideline boost sales draw clients suppliers. In this paper, we have KMP algorithm-based multivariate pruning method web-based index searching different analytics algorithm machine learning classifiers achieve patterns transactional gathered websites. Moreover, use log-based data, research presented paper suggests new learning-based evaluation evaluating To underlying relationship between eLearning system its predictor factors, three techniques multiple linear regressions are create prediction models. will lead industry economically profitable stage. capability can assist vendor keeping track customers items they viewed, well categorizing their emporium so cater specific needs. been proposed models, offering trustworthy prognoses, aid excellent usability. Such models might incorporated into prognostic calculator or tool help treatment selection possibly increase visibility. However, none these recommended reusability because concerns about deployment technical One problem science solved explainability. For instance, let us say B 10 all people our population even. hash function’s behavior not random since only buckets 0, 2, 4, 6, 8 value h(x). = 11, would 1/11th even integers transmitted each 11 buckets. function work situation.