Authors: Dr. C.K. Gomathy, Swaminathan S, Rohith Reddy S, Monishkumar V
Abstract: The exponential growth of data generated from social media, IoT devices, enterprise systems, online transactions, and cloud platforms has transformed big data analytics into a critical domain for modern organizations. Predictive analytics, a major branch of data analytics, leverages statistical models, machine learning algorithms, and AI-driven techniques to forecast future events and uncover hidden patterns within large-scale datasets. Traditional analytical approaches are increasingly inadequate for handling the velocity, variety, and volume of modern data environments. With advancements in distributed computing frameworks such as Hadoop, Spark, and cloud-native analytics systems, predictive analytics has become a powerful enabler for data-driven decision-making. This paper explores the principles of predictive analytics in big data environments, examining its methodologies, architectures, machine learning techniques, and industry applications. A detailed literature survey highlights developments from 2015–2025, focusing on model optimization, scalable processing, and domain-specific predictive frameworks. The methodology outlines an end-to-end predictive analytics pipeline, including data ingestion, preprocessing, model training, evaluation, and deployment. Implementation details demonstrate how predictive models can be integrated into distributed systems using containerized microservices and scalable cloud architectures. Experimental results confirm the effectiveness of the model in supporting real-time predictions, trend analysis, and intelligent automation. The findings emphasize predictive analytics as a foundational tool across sectors such as finance, healthcare, retail, manufacturing, and cybersecurity.
DOI: http://doi.org/10.5281/zenodo.17798573
