Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase

The deployment of self-learning computer algorithms that can automatically enhance their performance via experience is referred to as machine learning in ecommerce and a crucial trend the retail digital transformation. Machine be unambiguously trained by analysing big datasets, identifying repeating patterns, relationships, anomalies among all this data, creating mathematical models resembling such associations. These are improved when analyse ever-increasing amounts providing us with useful insights into specific ecommerce-related events links between variables underlie them. A tool has been quite effective studying current affairs, predicting future trends, making data-driven decisions. present work investigates implementation predict user intention for purchasing product on store's website. An Online Shoppers Purchasing Intention data set from UC Irvine Learning Repository was used investigation. In study, two classification-based i.e. Stochastic Gradient Descent (SGD) algorithm Random Forest were used. SGD first time prediction online intention. results showed resulted highest F1-Score 0.90 contrast algorithm.