Principal Component Analysis (PCA) is a statistical dimensionality reduction technique used to identify underlying patterns and structures in multivariate data sets. PCA transforms a set of correlated variables into a set of uncorrelated variables called principal components, which account for most of the variability in the original data.
The objective of PCA is to reduce the dimensionality of the data by projecting them into a lower dimensional space while retaining as much information as possible. Principal components are calculated from a covariance or correlation matrix of the original variables and ordered according to their relative contribution to the total variability of the data set. The principal components are then used to reconstruct the original data, allowing for a reduced representation of the original data set.
PCA is commonly used in machine learning applications to simplify and compress data, facilitating analysis and visualisation. It is also used in data exploration to discover underlying patterns and structures in large multivariate datasets.
Clustering methods, or grouping, are a fundamental part of the data analysis process, since they allow an automatic segmentation of the data [...]
Read More »The use of Artificial Intelligence in business is becoming more and more common and necessary for the optimization and evolution of processes. In one of our [...]
Read More »An article published in April 2021 by Óscar Jiménez El Confidencial, was titled "34,000 M prize for banks for applying well i [...]
Read More »Artificial intelligence is increasingly used and applied in many sectors, and as it could not be less, it has entered with force in the field of [...]
Read More »
