Title: Innovations and Discoveries in the Multidisciplinary Research
Chief Editor: Dr. Padmavathi S. M.
Associate Editor: Dr. Poonam Sachin Kadlag
Co-Editor: Dr. Shailaja A Akkur
ISBN: 978-81-69857-36-9
Chapter: 13
DOI: https://doi.org/10.59646/785/13
Authors: Dr. S. Sathyapriya, Dr. S. Lakshmanan, Dr. V. A. Jane, Dr. K. Loura Jency, and Dr. J. Antony John Prabhu
Abstract
Machine learning has become one of the most influential technologies in predictive analytics, helping organizations transform large volumes of data into meaningful insights for better decision-making. From healthcare and finance to manufacturing, education, and retail, machine learning algorithms are increasingly being used to identify patterns, predict future outcomes, and improve operational efficiency. However, selecting the most appropriate algorithm remains a challenge because each technique performs differently depending on the nature, quality, and complexity of the data. This study reviews and compares widely used machine learning algorithms, including Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Gradient Boosting methods, and Artificial Neural Networks. The review evaluates their predictive accuracy, computational efficiency, interpretability, scalability, and suitability across different application domains. The findings indicate that ensemble learning methods and deep learning models generally provide superior predictive performance for complex datasets, while simpler models continue to offer advantages in terms of transparency, ease of implementation, and lower computational cost. The study also discusses practical use cases across various industries, highlighting the strengths and limitations of different algorithms in real-world environments. Overall, the review emphasizes that no single machine learning algorithm is universally optimal; instead, model selection should be guided by the specific characteristics of the dataset, business objectives, available computational resources, and the need for model interpretability. These insights provide researchers and practitioners with a practical framework for choosing appropriate predictive models and developing reliable, efficient, and responsible machine learning solutions.
Keywords: Machine Learning, Predictive Analytics, Classification Algorithms, Regression Models, Ensemble Learning, Deep Learning, Artificial Intelligence, Decision Trees, Random Forest, Support Vector Machine, Predictive Modeling, Data Mining, Model Performance, Comparative Analysis.