Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

A new study proposes a hybrid machine learning model that combines a Retail Deep Neural Network with Extreme Gradient Boosting to forecast customer purchase behavior, using transaction data from a UK-based online retailer [1]. The research, submitted for publication on June 16, 2026, addresses a persistent challenge in electronic commerce: retail platforms struggle to interpret customer behavior and predict future purchases [1]. The authors sourced data from a United Kingdom-based online retailer containing almost 500,000 transaction records [1]. The dataset underwent preprocessing steps including data cleaning, outlier handling, temporal feature extraction, feature encoding, and z-score normalization before model training [2]. The proposed architecture uses a Retail Deep Neural Network (Ret-DNN) as a feature extractor to capture the full context of customer transactions [1]. The extracted features are then fed into an Extreme Gradient Boosting (XGBoost) model, which outputs a purchase probability for each customer [1]. The hybrid model achieved a Mean Absolute Error of 0.2193, outperforming the standalone Ret-DNN model [1]. The paper is available on arXiv, a preprint server that hosts research across disciplines including machine learning and artificial intelligence [2]. arXiv papers can be linked to models, datasets, and interactive demos on platforms such as Hugging Face, where community members can discuss findings and claim authorship [4]. Hugging Face and arXiv have also collaborated to embed executable demos directly alongside paper abstracts, allowing readers to test models without writing code [5]. The broader machine learning landscape has seen rapid development of large language models from organizations such as DeepSeek and Alibaba Cloud, though the current study focuses on predictive analytics for retail rather than generative text [7][9]. The authors did not report deployment of the model on quantum hardware or integration with quantum circuit generation systems, a gap noted in separate reviews of generative systems for quantum computing [3].