Principal Component Analysis (PCA) in Machine Learning

Principal Component Analysis (PCA) is a dimensionality reduction technique introduced by Karl Pearson in 1901. It transforms correlated variables into uncorrelated ones, preserving the most important relationships in the data. PCA captures maximum variance in the data and helps in data visualization, feature selection, and compression. This unsupervised learning algorithm reduces the dataset's dimensionality while retaining valuable information. PCA is widely used in machine learning and exploratory data analysis for predictive modeling.

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