Iris dataset classification using Python

The Iris dataset is a classic machine learning dataset that is often used as a benchmark for classification algorithms. It contains 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The dataset is commonly used to demonstrate the classification of iris flowers into three species: setosa, versicolor, and virginica. In this Python project, we will explore the Iris dataset and use machine learning algorithms to classify the flowers based on their features. We will be using popular libraries such as scikit-learn, pandas, and numpy to preprocess, visualize and train machine learning models on the dataset.