Leveraging Machine Learning for Predictive Analytics in Diverse Domains

EasyChair Preprint 15764

Abstract

Machine learning (ML) has become a transformative technology across multiple domains, offering advanced capabilities for predictive analytics, decision-making, and automation. This paper provides an overview of ML methodologies and their applications, emphasizing supervised, unsupervised, and reinforcement learning. We review related work to highlight key advancements and gaps in existing research. Our proposed approach leverages a hybrid model combining neural networks and ensemble methods to improve prediction accuracy in real-world scenarios. Results demonstrate significant performance gains compared to traditional methods. The paper concludes by discussing the implications of these findings and potential future research directions.

Keyphrases: Model Interpretability, Predictive Analytics, Reinforcement Learning, data-driven technologies, ensemble methods, hybrid models, machine learning, neural networks, supervised learning, unsupervised learning

@booklet{EasyChair:15764,
  author    = {Amit Anaya and Aditya Priya and Vikram Kavya and Diego Martinez},
  title     = {Leveraging Machine Learning for Predictive Analytics in Diverse Domains},
  howpublished = {EasyChair Preprint 15764},
  year      = {EasyChair, 2025}}