Artificial Intelligence and Machine Learning for Advanced Data Analytics

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have transformed data analytics from a primarily descriptive discipline into an intelligent, predictive, and increasingly autonomous process capable of discovering complex patterns, generating forecasts, detecting anomalies, and supporting data-driven decision-making. Modern organizations generate large volumes of structured and unstructured data through digital platforms, sensors, enterprise applications, financial transactions, social networks, and connected devices, creating both opportunities and challenges for conventional analytical systems. This research investigates the application of AI and ML techniques for advanced data analytics, with particular emphasis on supervised learning, unsupervised learning, deep learning, feature engineering, predictive modeling, anomaly detection, and automated analytical decision support. A unified analytical framework is proposed that integrates data preprocessing, feature construction, machine learning model selection, prediction, evaluation, and interpretation. An experimental evaluation is conducted using a representative structured dataset containing numerical and categorical variables for classification and predictive analytics. Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and a neural network model are evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve.