Evaluation of leading Machine Learning Models

In simple terms, machine learning methods are algorithms that can turn data into meaningful information. Leveraging statistical algorithms and mathematical optimization, machine learning algorithms can use data to understand patterns, make predictions or classifications, and provide useful insights through minimal instructions. This has helped ML find its application in fields like data mining, medical image processing, Natural Language Processing (NLP), finance, and many others. However, ML models come with inherent challenges, including overfitting, interpretability issues, and computational trade-offs, which must be carefully considered to ensure their effectiveness. This study evaluates the performance of three widely used ML models; XGBoost, Random Forests, and Deep Neural Networks; across classification and prediction tasks. To conduct this analysis, three real-world datasets were selected: a credit card default dataset for classification, a real estate sales dataset for property value prediction, and a vehicle sales dataset for price estimation. These datasets provide diverse testing grounds for assessing model accuracy, computational efficiency, and interpretability. Evaluation metrics such as Mean Squared Error (MSE) and R-squared are used for prediction tasks, while F1-score, Kappa Statistics, and Area Under the Curve (AUC) measure classification performance. The findings of this study highlight the trade-offs between different ML models, demonstrating how each model performs under varying data characteristics and problem contexts. This research aims to provide insights into model selection and optimization strategies for practitioners working with structured datasets in finance, real estate, and other fields. By comparing the strengths and weaknesses of these ML approaches, this study contributes to a deeper understanding of how predictive and classification models can be effectively utilized for data-driven decision-making.