Iris Flower Species Detection using Machine Learning Technique

Iris flower species classification and prediction is a popular task in machine learning, where the goal is to predict the species of an iris flower based on its physical characteristics. This task is typically performed using the Iris dataset, which contains measurements for sepal length, sepal width, and petal length for 150 iris flowers. This paper presents machine learning techniques to classify iris flower species using Decision Trees, Gaussian Naive Bayes, Support Vector Machines, and k-nearest neighbor models. We discuss the data preprocessing steps, model selection, hyperparameter tuning, and evaluation metrics used to optimize the performance of each model. We compare each model’s accuracy, precision, recall, and F1 score and demonstrate how the selected model can be used to make predictions on new data. Our results show that the Gaussian Naive Bayes model performs the best, achieving an accuracy of 1.0 on the Iris dataset. We have found that the Gaussian Naive Bayes classifier gives the best accuracy compared to KNN, SVM, and Decision Tree models. Overall, this paper provides a comprehensive overview of iris flower species classification and prediction using machine learning.