MACHINE LEARNING ALGORITHMS FOR PREDICTIVE ANALYTICS: A REVIEW AND EVALUATION

Abstract

Predictive analytics has emerged as a powerful tool for businesses and organizations to forecast future trends, customer behaviors, and operational outcomes based on historical data. Machine learning (ML) algorithms form the backbone of predictive analytics by offering automated, data-driven methods that can uncover patterns and make accurate predictions. This paper provides a comprehensive review and evaluation of various machine learning algorithms commonly used for predictive analytics, including both traditional and contemporary techniques. The review explores supervised, unsupervised, and semi-supervised algorithms such as decision trees, random forests, support vector machines (SVMs), k-nearest neighbors (KNN), logistic regression, and neural networks, along with advanced deep learning methods like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each algorithm is examined in terms of its theoretical foundation, computational complexity, strengths, and limitations, as well as its applicability to specific types of predictive tasks, including classification, regression, and time-series forecasting. Additionally, the evaluation focuses on key performance metrics—such as accuracy, precision, recall, and F1 score—that are used to assess the effectiveness of these algorithms. Special attention is given to issues of model interpretability, scalability, and the handling of large datasets, which are crucial in real-world applications. The paper also discusses the latest advancements in ensemble learning techniques, such as boosting, bagging, and stacking, which combine the predictions of multiple models to improve overall performance. The impact of hyperparameter tuning, cross-validation, and feature engineering on the predictive power of machine learning models is critically analyzed. By presenting a thorough comparison of various algorithms across different predictive analytics use cases, this review offers valuable insights into how organizations can select the most appropriate machine learning techniques for their specific needs. Future trends in predictive analytics, such as the integration of explainable AI (XAI) and the role of ethical considerations in algorithmic decision-making, are also discussed. This paper aims to serve as a guide for data scientists, researchers, and business leaders who seek to implement machine learning solutions for predictive analytics in a practical and efficient manner.