Predictive Analytics for Website User Behavior Analysis

This study examines the application of predictive analytics to user behavior analysis on websites, with a particular emphasis on a dataset of customer behavior from e-commerce. To forecast behavioral patterns, the study will apply the random forest method. Additionally, exploratory data analysis (EDA) will be carried out to extract insights from the dataset. The dataset employed in this study includes a variety of factors, such as purchase history, browsing habits, and consumer demographics, among other pertinent data. The research attempts to derive useful insights that can improve the comprehension and prediction of user behavior on the website by utilizing predictive analytics approaches. In this study, the random forest algorithm which is well-known for its capacity to manage intricate datasets and generate precise predictions is employed. The method builds several decision trees and combines their predictions to produce more reliable and accurate outcomes by utilizing ensemble learning. Predicting different aspects of user behavior, including browsing patterns, chance of making a purchase, and customer segmentation, is the aim. When paired with the insights from EDA, the outcomes of the behavioral analysis prediction made with the random forest algorithm can provide e-commerce companies with useful data. Gaining insight into user behavior can help with customer engagement, website design optimization, personalization, and overall user experience enhancement.