Predicting Customer Purchases with TabNet: A Deep Learning Approach to Consumer Behaviour

Purchase prediction is vital to the contemporary e-commerce website in that it can facilitate personalized marketing campaign, optimal inventory management, and enhanced customer experience. To accurately predict the purchase intention of a user through behavioral data, which is often high-dimensional and sparse, is an intricate challenge. Conventional machine learning models such as LightGBM and XGBoost also performed fairly well but tend to fall short when they need to capture complex feature interdependencies and higher-order interactions within big tabular datasets. Here, in this work, we introduce using TabNet, a deep network specifically tailored for tabular data that uses sequential attention mechanisms for selecting relevant features at training time dynamically. We implement TabNet on a real-world user interaction dataset and evaluate its performances compared to LightGBM and XGBoost. The experimental results shows that TabNet dramatically surpasses the traditional models, with an accuracy of 97%, outperforming 88.5% and 86.8% by LightGBM and XGBoost, respectively. TabNet also shows greater precision, recall, and F1-score, suggesting its stability and reliability in purchase behavior prediction. This paper summarizes end-to-end methodology, such as data preprocessing, model selection, training pipeline, and evaluation metrics, while emphasizing deep learning strengths for consumer behavior analytics in e-commerce settings.