Enhancing Accuracy: Iris Flower Classification with Ensemble Models

This research study investigates the classification of Iris flowers based on their morphological structures, addressing the challenges posed by variations in attributes like size, shape, and color. This study explores various classification techniques and their practical implementations by conducting a comparative analysis using the IRIS dataset. With 21 attributes and three species (Setosa, Versicolor, and Virginica), each comprising 400 samples, this research aims to leverage machine learning algorithms to achieve precise classification. Key steps include data preprocessing, model selection, and hyperparameter tuning, with evaluation metrics to enhance the model performance. Furthermore, this study also explores ensemble-based learning to improve the prediction accuracy. Comparative analysis reveals the accuracy of each model, demonstrating effective predictions with accuracy rates of 95.5% and 97%. In summary, this research study offers a comprehensive overview of Iris flower species classification and prediction using machine learning techniques, contributing to advancements in this domain.