Dimensionality Reduction

Here you’ll find everything related to dimensionality reduction.

Dimensionality reduction is a technique used to reduce the number of features or dimensions in a dataset while retaining as much information as possible. It is often used when working with high-dimensional datasets, as these can be difficult to visualize and can also require more computational resources to process. There are several approaches to dimensionality reduction, including feature selection, feature extraction, and manifold learning.

Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python

clustering stock markets machine learning cryptocurrencies blockchain bitcoin ethereum-min

Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more