Unsupervised Learning in Python

Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This tutorial will guide you through the basics of unsupervised learning in Python.

Key Concepts

• Clustering: Grouping similar data points together. Common algorithms include K-means, DBSCAN, and hierarchical clustering.
• Dimensionality Reduction: Reducing the number of features in the data. Techniques like PCA (Principal Component Analysis) and t-SNE are commonly used.
• Anomaly Detection: Identifying data points that deviate significantly from the rest of the data. This is useful for fraud detection and outlier analysis.

Practical Examples

Here's a simple example using the K-means clustering algorithm:

from sklearn.cluster import KMeans
import numpy as np

# Generate some synthetic data
data = np.array([[1, 2], [1, 4], [1, 0],
                  [10, 2], [10, 4], [10, 0]])

# Create a KMeans instance
kmeans = KMeans(n_clusters=2, random_state=0).fit(data)

# Get the cluster labels
labels = kmeans.labels_

# Print the cluster labels
print(labels)

Further Reading

For more in-depth knowledge, we recommend the following tutorials:

• Python for Data Science
• Machine Learning Basics

K-means Clustering