Data is everywhere; everything is data. In traditional days we stored structured and smaller data using centralized database SQL, MySQL, and Oracle. In the modern era, we need to store structured, semi-structured, and unstructured data in a database to analyse real-time data. Big data analytics helps retrieve information from big and complex datasets. As the volume of data increases, conventional data processing becomes inefficient. We use machine learning algorithms to identify hidden patterns, predict outcomes, and make decisions. This paper reviews three algorithms—Decision Tree, Support Vector Machine (SVM), and K-Means clustering—used in big data analytics. We used a machine learning algorithm to analyse big data effectively. We analyse the results and discuss future enhancements of the algorithms for real-time big data processing, scalability improvement, and integration with deep learning techniques..
Download
Download XML