Prediction Model based on Iris Dataset Via Some Machine Learning Algorithms
DOI:
https://doi.org/10.31642/JoKMC/2018/100210%20Keywords:
Machine Learning, Iris Flower, Logistic Regression, K-Nearest Neighbors, Decision Tree, Random ForestAbstract
Abstract— Supervised Machine Learning algorithm has an important approach to Classification. We are predicting the deal type of the Iris plant using various algorithms of machine learning. Iris plants are determined by numerous factors such as the size of the length and width of the property. A horticultural skill announces that some of the plants are different in some physical appearances like size, shape, and color. Hence it is difficult to recognize any species. Versicolor, Setosa, and Virginica have three identical subspecies of The Iris flower species. This paper uses machine learning algorithms to recognize all classes of the flower with an accuracy degree of %100 for KNN, %95 for RF, %97 for DT, and %98 for LR. The Iris dataset is frequently available, and it is implemented using Scikit tools. and build the prediction model for Plants. Here, algorithms of machine learning such as Logistic Regression (LR), Decision Tree (DT), K Nearest Neighbor (KNN), and Random Forest (RF) are employed to construct a predictive model.
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