Top Algorithms in Machine Learning for Predicting Insights in Smart Applications

Machine learning has become an essential tool for extracting valuable insights in smart applications in areas ranging from smart grids, healthcare, and agriculture to transportation and retail. This paper addresses the top machine learning algorithms that drive predictive analytics in these applications. It highlights the significance of ensemble learning methods like Random Forest and XGBoost, deep learning methods like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, and emerging trends like Graph Neural Networks (GNNs) and Federated Learning. The study also addresses the use of automated machine learning (AutoML) and metaheuristic-optimized models to improve prediction accuracy and reduce computational costs. In addition, it briefly addresses the use of explainable AI (XAI) to enhance decision-making transparency. Through a comparison of the performance, efficiency, and flexibility of these algorithms, this paper provides an in-depth understanding of how machine learning is revolutionizing the future of smart applications.