Enhanced Iris Flower Classification Using Optimized Machine Learning Techniques

The classification of Iris flower species is a well-known problem in machine learning and is commonly used to evaluate the performance of classification algorithms. Although many previous studies have reported very high accuracy, they often lack proper validation techniques, which may result in overfitting and unreliable conclusions. In this study, we aim to improve the reliability and effectiveness of the classification process using simple yet efficient methods. Techniques such as feature scaling, hyperparameter tuning, and K-Fold cross-validation are applied to enhance model performance. To maintain simplicity and clarity, only two models—Support Vector Machine (SVM) and Random Forest—are used. The experimental results show that the proposed approach achieves 100% accuracy on the test dataset, while cross-validation accuracy reaches up to 96.66%, indicating strong generalization capability. This demonstrates that even simple models can achieve high performance when proper evaluation techniques are applied.