PREDICTIVE ANALYTICS USING MACHINE LEARNING

Abstract

Abstract - Predictive analytics using Machine Learning (ML) has become one of the most influential technologies in data-driven decision-making. It combines statistical analysis, data mining, artificial intelligence, and machine learning algorithms to analyze historical and real-time data for predicting future events, trends, and behaviors. Organizations across healthcare, finance, retail, manufacturing, education, agriculture, transportation, and cybersecurity increasingly rely on predictive analytics to improve operational efficiency, reduce risks, optimize resources, and enhance customer experiences. Machine learning enables predictive models to automatically learn from data and continuously improve their prediction accuracy without explicit programming. The integration of cloud computing, Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) has further expanded the capabilities of predictive analytics. This paper discusses the concept, objectives, working process, machine learning techniques, algorithms, applications, benefits, challenges, and future trends of predictive analytics using machine learning.

Keywords: Predictive Analytics, Machine Learning, Artificial Intelligence, Data Mining, Big Data, Predictive Modeling, Classification, Regression, Deep Learning, Business Intelligence.