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https://github.com/nishant2018/pca-feature-selection-scratch
Principal Component Analysis (PCA) is a powerful dimensionality reduction technique commonly used in machine learning and data analysis. It transforms a dataset into a set of linearly uncorrelated variables called principal components.
https://github.com/nishant2018/pca-feature-selection-scratch
feature-selection linear-algebra machine-learning pca statistics
Last synced: 3 days ago
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Principal Component Analysis (PCA) is a powerful dimensionality reduction technique commonly used in machine learning and data analysis. It transforms a dataset into a set of linearly uncorrelated variables called principal components.
- Host: GitHub
- URL: https://github.com/nishant2018/pca-feature-selection-scratch
- Owner: Nishant2018
- Created: 2024-06-10T09:03:10.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-06-10T10:11:28.000Z (5 months ago)
- Last Synced: 2024-06-11T10:54:58.085Z (5 months ago)
- Topics: feature-selection, linear-algebra, machine-learning, pca, statistics
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/endofnight17j03/pca-feature-selection-scratch
- Size: 669 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0