Machine Learning Algorithms for Big Data Analytics Applications
Ms. Manyala Naga Sailaja, Dr. Talluri Upender, Ms.Yalamarthi Sailaja, Dr. S. Sree Dharinya
Indexed In: Google scholar
Release Date: 08/08/2024 | Copyright:© 2024 | Pages: 300
DOI: 10.71443/9788197282133
ISBN10: 8197282137 | ISBN13: 9788197282133
Machine learning (ML) algorithms play a crucial role in big data analytics by enabling the extraction of meaningful insights from vast and complex datasets. These algorithms, such as decision trees, support vector machines, k-means clustering, and deep learning models, are designed to process and analyze large volumes of data in real-time, identifying patterns, trends, and correlations that are otherwise difficult to detect. Applications range from predictive analytics, where models forecast future trends, to natural language processing (NLP) for understanding unstructured data, to anomaly detection in fields like cybersecurity. By leveraging the scalability and efficiency of these algorithms, industries such as healthcare, finance, and e-commerce can harness Big Data for smarter decision-making and personalized services.
The scope of machine learning algorithms in big data analytics is vast, as they enable the extraction of meaningful insights from massive, complex datasets that traditional methods struggle to handle. These algorithms, such as decision trees, neural networks, and deep learning, are critical in identifying patterns, making predictions, and automating decision-making processes across industries like healthcare, finance, e-commerce, and social media. With big data, machine learning models can continuously improve their accuracy by learning from increasing amounts of data, making them highly effective in applications like fraud detection, personalized marketing, predictive maintenance, and real-time data analysis. The combination of big data and machine learning drives innovation, enabling businesses to optimize operations, enhance customer experiences, and generate new revenue streams.
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