In todays class we have discussed about
Principal Component Analysis (PCA) is a statistical method used for simplifying complex data sets. It aims to reduce the number of variables while retaining the key information present in the data.
PCA works by transforming a set of correlated variables into a smaller set of uncorrelated variables known as principal components. These components are calculated in a way that the first principal component captures the most variance in the data. Subsequent components capture less and less variance in descending order.
The primary goal of PCA is to find patterns and structures within the data, allowing for easier interpretation and analysis. It’s commonly used for dimensionality reduction, data visualization, and identifying the most critical factors influencing the data.