Unsupervised learning is a type of machine learning where the algorithm is left to find patterns and insights from the data on its own. This tutorial will guide you through the basics of unsupervised learning in Python.

Introduction

Unsupervised learning is used to analyze and cluster large datasets to find hidden patterns or groupings. Some common applications of unsupervised learning include:

  • Clustering: Grouping data into clusters based on similarity.
  • Association: Finding interesting relationships between variables in large databases.
  • Dimensionality Reduction: Reducing the dimensionality of data.

Python Libraries

To perform unsupervised learning in Python, you can use libraries such as:

  • Scikit-learn: A popular machine learning library that provides simple and efficient tools for data analysis and modeling.
  • Pandas: A powerful data manipulation and analysis library.
  • NumPy: A fundamental package for scientific computing with Python.

Clustering

One of the most common unsupervised learning techniques is clustering. Let's take a look at how to perform clustering using the K-Means algorithm.

from sklearn.cluster import KMeans
import pandas as pd

# Load data
data = pd.read_csv('/path/to/data.csv')

# Perform K-Means clustering
kmeans = KMeans(n_clusters=3)
kmeans.fit(data)

# Get cluster labels
labels = kmeans.labels_

# Add cluster labels to the original data
data['cluster'] = labels

Association

Another common application of unsupervised learning is association, where you find interesting relationships between variables. The Apriori algorithm is a popular method for this task.

from apyori import apriori

# Load data
data = pd.read_csv('/path/to/data.csv')

# Perform Apriori algorithm
association_rules = apriori(data, min_support=0.5, min_confidence=0.7)

# Display association rules
for rule in association_rules:
    print(rule)

Dimensionality Reduction

Dimensionality reduction is the process of reducing the number of variables in a dataset while retaining most of the information. One popular method for dimensionality reduction is Principal Component Analysis (PCA).

from sklearn.decomposition import PCA
import pandas as pd

# Load data
data = pd.read_csv('/path/to/data.csv')

# Perform PCA
pca = PCA(n_components=2)
pca.fit(data)

# Transform data
data_reduced = pca.transform(data)

Conclusion

This tutorial provided an overview of unsupervised learning in Python. By using libraries such as Scikit-learn and Pandas, you can perform various unsupervised learning tasks such as clustering, association, and dimensionality reduction.

For more information on unsupervised learning, you can visit our Machine Learning tutorials.