Machine learning for Data Analysis and Decision Making
Keywords:
Machine learning, Data Analysis, Decision Making, Predictive Analytics, Real-Time AnalyticsAbstract
Machine Learning (ML) has become an innovative technology of enhanced data analysis and intelligent decision-making in the age of big data. This review paper will discuss the underpinning concepts, paradigms, and analytical abilities of ML that can be applied to predictive, prescriptive, and real-time decision systems. It addresses the concepts of supervised, unsupervised, and reinforcement learning strategies and their combination into data analytics processes including automated data preprocessing, pattern recognition, forecasting and anomaly detection. The research paper is a literature review of recent publications that indicate the recent use of ML in the healthcare, finance, manufacturing, retail, government, and supply chain domains. Moreover, it examines such emerging trends as deep learning, explainable AI, AutoML, and real-time analytics and deals with issues regarding data quality, scalability, interpretability, bias, and ethical issues. The review highlights the increasing overlap of ML and big data technologies to increase accuracy, efficiency and strategic decision-making. In general, ML can be considered a foundation of creating intelligent and data-driven organizational systems.
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