Scalable and efficient big data–driven predictive analytics using machine learning algorithms

Big Data has significantly transformed the way organizations analyze and interpret large volumes of complex data. Predictive analytics, powered by machine learning algorithms, plays a vital role in extracting meaningful insights and forecasting future trends. However, ensuring scalability and efficiency remains a major challenge due to the increasing volume, velocity, and variety of data.
This paper presents a comprehensive study on scalable and efficient predictive analytics using machine learning techniques in Big Data environments. Various algorithms, including Decision Trees, Random Forest, and Support Vector Machines, are evaluated based on key performance metrics such as accuracy, execution time, and scalability. Furthermore, the study emphasizes the role of distributed computing frameworks in processing large-scale datasets efficiently.
The results demonstrate that selecting appropriate algorithms along with scalable architectures can significantly enhance performance in Big Data analytics. The proposed approach provides an effective solution for handling large datasets while maintaining high accuracy and computational efficiency.