OPTIMIZING CONSUMER BEHAVIOUR ANALYTICS THROUGH ADVANCED MACHINE LEARNING ALGORITHMS

Journal of Science Technology and Research (JSTAR) 5 (1):360-368 (2024)
  Copy   BIBTEX

Abstract

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs.

Analytics

Added to PP
2024-08-24

Downloads
90 (#96,100)

6 months
90 (#63,147)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?