A Categorization and Evaluation on Industry Specific Machine Learning Models and Their Acceptability

Machine learning (ML) is a powerful technology that is revolutionizing several industries, including medicine, finance, and transportation. This research offers a thorough examination and assessment of machine learning methods used in various fields, with a special emphasis on their effectiveness and versatility in tackling industry-specific obstacles. ML models including XGBoost, Random Forest, and Gradient Boosting have shown outstanding performance in healthcare by accurately predicting cardiovascular illnesses and diabetes. Among these models, XGBoost has achieved the greatest accuracy and recall. Within the realm of finance, a combination of algorithms such as XGBoost and Random Forest proved to be more effective than conventional models in detecting fraudulent activities. This ensemble method demonstrated increased precision and a decline in the frequency of false positives. The Temporal Graph Convolutional Network (T-GCN) has been identified as the most efficient model in the transportation industry for forecasting Norwegian air passenger traffic. It surpasses other models concerning error and accuracy metrics. This work demonstrates how machine learning may be used to enhance decision-making and highlights the importance of selecting the right model depending on application needs, efficiency, and prediction accuracy in several industries. The findings provide helpful information on the effectiveness of various machine learning models and their applicability to actual situations, therefore facilitating future progress and utilization of machine learning technology.