Iris Dataset For Data Science And AI

One of the most well-known datasets in data science and machine learning is the Iris dataset. Three iris flower species are covered in it: Iris setosa, Iris versicolor, and Iris virginica. The dataset comprises four primary variables, each measured in centimeters: petal length, petal width, sepal length, and sepal width. With the use of these characteristics, trends may be examined, relationships can be seen, and classification models that can identify an iris flower's species from its measurements can be created.

Content

This dataset includes measurements of iris flowers collected from three different species — Iris setosa, Iris versicolor, and Iris virginica.
It contains the following columns:

sepal_length – Length of the sepal in centimeters

sepal_width – Width of the sepal in centimeters

petal_length – Length of the petal in centimeters

petal_width – Width of the petal in centimeters

species – The class or category of the iris flower

Each row in the dataset represents one flower sample with its respective measurements and species label.

Context

Sir Ronald A. Fisher, a British scientist, initially presented the Iris dataset in 1936. In data science and machine learning, it is one of the most used datasets for training and evaluating classification systems. For novices, the dataset offers a straightforward and well-balanced illustration of important ideas including feature correlations, data visualization, and predictive modeling. It is frequently used in tutorials, research papers, and educational initiatives due to its clarity and compact size.