Comparative Performance Analysis of Supervised Machine Learning Models for Big Data Classification

Abstract

The exponential growth of big data has intensified the need for efficient and accurate classification techniques capable of handling high-dimensional and large-scale datasets. Supervised machine learning models are widely used for classification tasks across domains such as healthcare, finance, social media, and e-commerce. This paper presents a comparative performance analysis of commonly used supervised machine learning models for big data classification. Algorithms including logistic regression, support vector machines, decision trees, random forests, and k-nearest neighbors are evaluated based on classification accuracy, precision, recall, computational efficiency, and scalability. The study highlights the strengths and limitations of each model and provides insights into selecting appropriate algorithms for big data classification tasks.