Principal Component Analysis for Dimensionality Reduction

In this lesson, you'll learn about Principal Component Analysis, which is a famous dimensionality reduction technique to help represent data in lower dimensions which can be helpful in visualizations and modeling.

Principal Component Analysis

PCA stands for Principal Component Analysis. It helps us transform high-dimensional datasets (having a large number of features) into a low-dimensional one (having a smaller number of features) without losing too much information. These datasets can include images or simple structured datasets. This helps us deal with the curse of dimensionality, which results in complex models and difficulty in visualizing.

Data represented in lower dimensions can be easily visualized. It would also help in modelling as the model won’t have to take into account extraneous features represented in higher dimensions. It also helps us remove the Multi-collinearity situation in which some input features are correlated with each other and provide redundant information.

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