HYBRID MACHINE LEARNING MODEL FOR PREDICTIVE ANALYTICS AND DECISION MAKING

Hybrid Machine Learning (HML) models have emerged
as an effective solution for improving predictive analytics and intelligent decision-making across multiple domains
such as healthcare, finance, manufacturing, and smart cities. Traditional machine learning algorithms often suffer from limitations related to scalability, accuracy, overfitting, and interpretability when applied independently. This research paper proposes a hybrid machine-learning framework that integrates supervised learning, unsupervised learning, and ensemble techniques to improve prediction accuracy and support robust
decision-making. The study evaluates the proposed framework using performance metrics such as accuracy, precision, recall, F1- score, and computational efficiency.
Experimental analysis demonstrates that the
hybrid model significantly outperforms
conventional machine learning approaches.
The paper also discusses applications,
challenges, and future enhancements in
hybrid intelligent systems.