BI_Task3_Iris_Flower_Classification

Iris flower classification is a popular machine learning problem that involves categorizing iris flowers into three species: setosa, versicolor, and virginica, based on their features. The most commonly used machine learning algorithms for iris flower classification include k-Nearest Neighbors, Support Vector Machines, and Decision Trees. The task of iris flower classification is considered a classic introductory problem in the field of pattern recognition and machine learning. By accurately classifying iris flowers, the model can assist botanists and researchers in identifying and studying different species in a more efficient manner. The Iris dataset contains four features: sepal length, sepal width, petal length, and petal width, all measured in centimeters. Due to its simplicity and well-structured dataset, iris flower classification serves as a foundational example for teaching machine learning concepts and techniques.