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Dr. P. Rajkumar is a Professor in the Department of Computer Science and Business Systems at KGISL Institute of Technology, Coimbatore, Tamil Nadu, India. His research focuses on Artificial Intelligence, Machine Learning, data analytics, intelligent forecasting, and cloud-based computing solutions. He has contributed to academic research and student mentoring in advanced computing technologies and real-world AI applications.
Forecasting is essential for facilitating informed decision-making in numerous practical sectors, such as healthcare, agriculture, banking, manufacturing, transportation, energy, meteorology, smart urban development, and supply chain logistics. The growing accessibility of extensive datasets produced by Internet of Things (IoT) devices, cloud computing infrastructures, and digital systems has hastened the use of Machine Learning (ML)-driven forecasting models. In contrast to traditional statistical forecasting methods, machine learning algorithms possess the capability to autonomously identify intricate nonlinear correlations, extract concealed patterns from both historical and real-time data, and perpetually enhance predictive precision. Sophisticated algorithms including Linear Regression, Decision Trees, Random Forest, Support Vector Regression (SVR), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer models have exhibited exceptional efficacy in predictive analytics. This chapter delineates the principles, framework, predictive models, applications, obstacles, and prospective research avenues of machine learning-driven forecasting for astute real-world decision-making support.