Ms. Swetha Chinta
Integrating Machine Learning Algorithms in Big Data Analytics: A Framework for Enhancing Predictive Insights
Abstract:
In the era of exponential data growth, the integration of machine learning algorithms into big data analytics offers promising avenues for enhancing predictive insights across various domains. This research article presents a comprehensive framework designed to facilitate the effective integration of machine learning techniques within big data environments. By addressing the inherent challenges of traditional data analysis methods, the proposed framework aims to optimize data processing, improve model accuracy, and yield actionable insights. The study explores the current landscape of big data analytics, reviews existing methodologies for machine learning integration, and identifies gaps in the literature. Through the development of a structured approach, this article elucidates the critical components necessary for successful implementation, including data preprocessing, algorithm selection, and performance evaluation. Case studies illustrate the practical application of the framework, demonstrating significant improvements in predictive accuracy and decision-making processes. The findings underscore the transformative potential of machine learning in big data analytics, paving the way for future research and industry applications.