Customer shopping behaviour prediction has become a crucial aspect in modern retail and ecommerce industries, especially in the era of digital transformation and data-driven decision making. With the rapid increase in online transactions and customer interactions, organizations are leveraging large volumes of structured and unstructured data to understand consumer preferences. Machine Learning (ML) algorithms have demonstrated significant capabilities in analyse customer data, identifying hidden patterns, and predicting future purchasing behaviour with high accuracy. However, challenges such as data sparsity, dynamic customer preferences, customer diversity, and model interpretability limit the real-world deployment of these predictive systems. This study presents a comprehensive review and proposes an advanced framework for predicting customer shopping behaviour using machine learning techniques. The research focuses on both supervised and unsupervised learning approaches, including classification models such as Decision Trees, Random Forest, and Support Vector Machines (SVM), as well as deep learning models like Artificial Neural Networks (ANN) for handling large-scale datasets. In addition, recommendation system techniques such as collaborative filtering and contentbased filtering are explored to enhance personalized shopping experiences. The study also addresses critical limitations such as overfitting, bias in customer segmentation, cold-start problems in recommendation systems, and lack of explainability in complex models. The proposed methodology integrates multiple stages, including data preprocessing, feature engineering, customer segmentation, predictive modelling, and performance evaluation. Advanced techniques such as data normalization, dimensionality reduction, and feature selection are applied to improve model efficiency. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to assess model performance. Furthermore, the study emphasizes the role of real-time data processing and behavioural analytics in improving prediction reliability. The expected outcomes include improved prediction accuracy, efficient customer segmentation, and highly personalized product recommendations. These improvements can lead to increased customer satisfaction, better customer retention, and higher business profitability. Ultimately, this work aims to bridge the gap between theoretical predictive analytics and real-worldbusiness applications by providing a scalable, efficient, and interpretable machine learning framework for customer shopping behaviour prediction in modern retail systems.
Kumar et al. (Tue,) studied this question.