[Feature Request]: Add Principal Component Analysis (PCA) in Machine Learning

[pavitraag]

Is there an existing issue for this?

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Feature Description

Principal Component Analysis (PCA) is a powerful technique for dimensionality reduction that transforms a large set of variables into a smaller one, retaining most of the original data's variability. Integrating PCA into our project would allow for more efficient data preprocessing and improved performance of subsequent machine learning algorithms. This enhancement will enable users to reduce the complexity of their datasets, visualize high-dimensional data in two or three dimensions, and identify underlying patterns more effectively.

Use Case

-Enhancing Data Preprocessing -Facilitating Data Visualization

Priority

High

Record

• [X] I have read the Contributing Guidelines
• [X] I'm a GSSOC'24 contributor
• [X] I have starred the repository

[github-actions[bot]]

Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!

[github-actions[bot]]

Hello @pavitraag! Your issue #3406 has been closed. Thank you for your contribution!