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In order to address this research question, the paper will delve into a study of the data and come up with a prediction model that classifies customers’ purchasing intention based on the characteristics of …

Classify 30% of the dataset (test data) for the target variable “revenue”. Apply Support Vector Machine (SVM) and Random Forest (RF) algorithms for the classifications.

The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific …

at https://www.kaggle.com/static/assets/app.js?v=6714c0e58afef268d3de:2:987822. at Object.next (https://www.kaggle.com/static/assets/app.js?v=6714c0e58afef268d3de:2:987927) at ce …

The dataset used in our analysis was obtained from the UC Irvine Machine Learning Repository, a popular website with hundreds of datasets available for analysis.

The Online Shoppers Purchasing Intention Dataset from the UCI Repository is a dataset designed to understand the factors influencing online shopping behavior, particularly whether a user will end up …

Jul 29, 2025 · The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any …

For our particular set of variables, we found that the Decision tree was better able to predict the shoppers purchasing intent than the Clustering models because of the limitations of our datasets.

Examples of nudges include highlighting popular products through social proof, and exit intent overlay on webpages. The dataset used in our analysis was obtained from the UC Irvine Machine Learning …