Unsupervised Learning using K-Means Clustering

Introduction to Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in the data without any prior knowledge of the output. In this tutorial, we will learn how to perform unsupervised learning using K-Means clustering and Scikit-Learn.

Installing the Required Libraries

To install the required libraries, run the following command in your terminal:

pip install scikit-learn

Loading the Dataset

We will use the Iris dataset, a classic multiclass classification problem.

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data

Building the Model

We will build a simple K-Means clustering model using Scikit-Learn.

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
print(kmeans.labels_)

Conclusion

In this tutorial, we learned how to perform unsupervised learning using K-Means clustering and Scikit-Learn. We loaded the dataset, built the model, and trained it on the data.