Evaluating Machine Learning Models in Various Domains: An Extensive Analysis and Comparison

y facilitating automation, administration work, and predictive analysis, Machine Learning (ML) has transformed several industries. Deployments in a wide range of applications such as educational institutions, healthcare, agriculture, finance, and cybersecurity demand models of different levels of accuracy, interpretability, and computational power. Decision Trees, Support Vector Machines (SVM), Ensemble Methods, and Deep Learning structures are a few of the Machine Learning (ML) models that are deeply contrasted in this work. The research gives insights towards the choice of models by focusing on strengths, weaknesses, and area-specialized fitness. It also discusses challenges such as model interpretability, data sparseness, and ethical aspects. Future perspectives are also discussed such as AutoML, amalgam models, and ethical protocols.