<![CDATA[This study addresses the problem of object classification using numerical feature representations in machine learning environments. The research aims to compare the performance of four supervised learning algorithms, namely Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest, in predicting object classes. The methodology consists of data preprocessing, normalization using Min-Max Scaling, model training, and evaluation using accuracy, precision, recall, and F1-score. A dataset of 2,400 samples with 18 numerical features and four object classes was used, with an 80:20 train-test split and cross-validation for robustness. The results show that Random Forest achieved the highest performance with 95.1% accuracy and 0.949 F1-score, followed by SVM with 93.2% accuracy. KNN and Decision Tree achieved 90.4% and 88.1% accuracy, respectively.The novelty of this study lies in the structured experimental pipeline and comprehensive multi-metric evaluation combined with computational efficiency analysis for object prediction using tabular data.It can be concluded that ensemble-based methods such as Random Forest provide superior generalization and stability for heterogeneous object data.]]>
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