A Systematic Analysis and Adaptive Hybrid Machine Learning Framework for Online Shopping Behavior Prediction
DOI:
https://doi.org/10.15662/IJARCST.2023.0606026Keywords:
Online Shopping Behavior Prediction, E-Commerce Analytics, Hybrid Machine Learning, Adaptive Learning Framework, Predictive Modeling, Data Characteristics Analysis, Model Evaluation Framework, Consumer Behavior Analytics, Machine Learning Performance MetricsAbstract
The rapid growth of e-commerce platforms has generated vast amounts of consumer data, making online shopping behavior prediction an important research area for improving customer experience, personalized recommendations, and business decision-making. However, traditional statistical models and standalone machine learning techniques often face limitations in handling complex data characteristics such as high dimensionality, data imbalance, dynamic user behavior, and heterogeneous feature types. These challenges reduce the overall effectiveness and reliability of predictive models in real-world e-commerce environments. This research presents a systematic analysis of existing statistical and machine learning approaches used for online shopping behavior prediction, highlighting their strengths, limitations, and performance gaps. The study further investigates the influence of key data characteristics—such as feature diversity, data sparsity, temporal patterns, and class imbalance—on model performance. To address these issues, a structured evaluation framework is proposed that assesses predictive models using comprehensive performance metrics beyond conventional accuracy, including precision, recall, F1-score, robustness, and model adaptability. Building upon these insights, the research designs and implements an adaptive hybrid machine learning framework that integrates multiple learning techniques to improve prediction accuracy, stability, and generalization capability. The proposed framework dynamically selects and combines suitable algorithms based on data characteristics and performance feedback. Experimental validation using real-world e-commerce datasets demonstrates that the adaptive hybrid framework significantly outperforms traditional and single-model approaches in predicting online shopping behavior. The findings contribute to the development of more intelligent, reliable, and scalable predictive systems for modern e-commerce applications.
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