AlessandroDiLauro/Unsupervised-Learning-Techniques

Unsupervised Learning Techniques

Clustering for dataset exploration

Unsupervised learning can be a powerful tool for exploring and understanding your data. Clustering algorithms can help you identify patterns and relationships within your data that may not be immediately apparent. In this repository, you'll find examples of how to use clustering to discover insights in your data using the scikit-learn library in Python.

Visualization with hierarchical clustering and t-SNE

Effective visualization is an important aspect of exploring and understanding your data. In this repository, you'll find examples of how to use hierarchical clustering and t-SNE to visualize high-dimensional datasets in a 2D space using the seaborn library in Python. This can be particularly helpful when working with large datasets that are difficult to visualize directly.

Decorrelating your data and dimension reduction with PCA

PCA is a powerful technique for decorrelating your data and reducing the dimensionality of your dataset. In this repository, you'll find examples of how to use PCA for dimension reduction and visualization of high-dimensional datasets using the NumPy and matplotlib libraries in Python. For more information on PCA, see the Wikipedia page.

Discovering interpretable features with NMF

Non-negative matrix factorization (NMF) is a technique for discovering interpretable features in your data. In this repository, you'll find examples of how to use NMF to extract meaningful features from your dataset and understand the relationships within your data using the NMF library in Python.

Example code

Here's an example of how to use the scikit-learn library to perform k-means clustering on a dataset:

from sklearn.cluster import KMeans

# Load the dataset
X = ...

# Create the model
model = KMeans(n_clusters=4)

# Fit the model to the data
model.fit(X)

# Predict the cluster labels for new data
new_data = ...
predictions = model.predict(new_data)