Comparative Analysis of Machine Learning Techniques for Predictive Modeling in Social and Infrastructural Systems

Abstract

Machine learning techniques have emerged as powerful tools for predictive modeling in various domains, including social and infrastructural systems. This research paper presents a comparative analysis of different machine learning techniques applied to four key areas: transportation, energy, healthcare, and social networks. The aim is to assess the effectiveness and suitability of these techniques for predicting critical outcomes and optimizing system performance. The paper discusses the strengths and limitations of popular machine learning algorithms, such as decision trees, random forests, support vector machines, and deep learning architectures, in the context of each domain. The analysis highlights the importance of considering domain-specific challenges, data characteristics, and performance metrics when selecting and applying machine learning techniques. The paper also identifies current research gaps and proposes future directions for enhancing the predictive capabilities of machine learning models in social and infrastructural systems. These include the development of semi-supervised and unsupervised learning approaches, the integration of domain knowledge, and the exploration of transfer learning and multi-task learning strategies. The findings of this comparative analysis contribute to a better understanding of the potential of machine learning in tackling complex prediction tasks across diverse domains and provide guidance for researchers and practitioners working on predictive modeling in social and infrastructural systems