A NOVEL STUDY ON MACHINE LEARNING ALGORITHMS FOR BIG DATA

Abstract

Big data's vastness and complexity pose a formidable challenge to traditional data analysis methods. Machine learning algorithms emerge as intrepid navigators, extracting meaningful patterns and hidden correlations from the deluge of information. Their versatility handles heterogeneous data formats, while their robust mechanisms ensure data quality. Machine learning empowers predictive modeling, anomaly detection, recommendation systems, fraud detection, and customer segmentation. Implementing these algorithms in big data environments presents challenges in data quality, scalability, and interpretability. Emerging trends like deep learning, edge computing, and explainable AI offer promising solutions, paving the way for a future where big data and machine learning shape data-driven decision-making. Keywords: Machine Learning, Data Quality, Recommendation system, deep learning.