how to load iris dataset in python from sklearn

Python is a popular programming language that is widely used in various fields, including Machine Learning. One of the popular datasets used in Machine Learning is the Iris dataset, which is used for classification problems. In this article, we will learn how to load the Iris dataset in Python using the scikit-learn library.

What is the Iris dataset?

The Iris dataset consists of 150 observations of iris flowers. The dataset contains four features: sepal length, sepal width, petal length, and petal width. The target variable is the Iris species, which has three categories: Iris Setosa, Iris Versicolour, and Iris Virginica. Each category has 50 observations. The dataset is often used for classification problems, such as predicting the species of an iris flower based on its features.

Installing scikit-learn

Scikit-learn is a popular Python library that provides various algorithms for Machine Learning. It also includes many datasets for practice and testing. To install scikit-learn, we need to have Python and pip installed on our computer. We can install scikit-learn using pip by typing the following command in the terminal or command prompt:

pip install scikit-learn

Loading the Iris dataset

Now that we have installed scikit-learn, we can load the Iris dataset. Scikit-learn includes the Iris dataset in its datasets module. We can load the dataset using the load_iris function. Here's the code to load the Iris dataset:

from sklearn.datasets import load_iris

iris = load_iris()

This code will load the Iris dataset into the variable named iris. We can access the features and target variables using the following code:

X = iris.data

y = iris.target

The X variable contains the features, and the y variable contains the target variable.

Exploring the Iris dataset

We can explore the Iris dataset to understand its features and target variables. The features are the sepal length, sepal width, petal length, and petal width. We can print the features using the following code:

print(iris.feature_names)

This code will print the names of the features.The target variable is the Iris species, which has three categories: Iris Setosa, Iris Versicolour, and Iris Virginica. We can print the target names using the following code:

print(iris.target_names)

This code will print the names of the target variables.We can also visualize the dataset using various libraries, such as Matplotlib and Seaborn, to gain insights into the dataset.

Conclusion

In this article, we learned how to load the Iris dataset in Python using the scikit-learn library. The Iris dataset is a popular dataset used for classification problems. We can use the load_iris function to load the dataset into our Python environment. We can also explore the dataset using various libraries to gain insights into the features and target variables.