Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering

DOI: http://dx.doi.org/10.15439/2023R63

Citation: Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering, Pradeep Kumar, Manuel Cardona, Vijender Kumar Solanki, Tran Duc Tan, Abdul Wahid (eds). ACSIS, Vol. 38, pages 7–10 (2023)

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Abstract. In this study, plant species were classified on the Iris dataset using Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and K-Means algorithms. In this process, models were developed for each method, success rates were obtained, and a model with a minimum error rate was introduced. The dataset of the study was obtained from the Kaggle website. The classification process was applied repeatedly on the iris dataset, and the classification or prediction with the minimum error rate was aimed at the established models. In the study process, first of all, the dataset was obtained, prepared, and visualized. Models were created using the Jupiter Notebook editor via the Anaconda desktop GUI. Then, the models were analyzed and the most successful algorithm was selected. As a result, according to the prediction/classification models, it was seen that the most successful model was obtained with the KNN algorithm, and the most unsuccessful model was obtained with the ANN algorithm.

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