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https://github.com/ljadhav25/principle-component-analysis-pca---machine-learning
Principal Component Analysis (PCA) is a statistical technique used in machine learning and data science for dimensionality reduction. The main goal of PCA is to reduce the number of variables in a dataset while preserving as much information as possible.
https://github.com/ljadhav25/principle-component-analysis-pca---machine-learning
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Principal Component Analysis (PCA) is a statistical technique used in machine learning and data science for dimensionality reduction. The main goal of PCA is to reduce the number of variables in a dataset while preserving as much information as possible.
- Host: GitHub
- URL: https://github.com/ljadhav25/principle-component-analysis-pca---machine-learning
- Owner: LJadhav25
- Created: 2024-07-08T12:18:02.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-08T12:18:29.000Z (7 months ago)
- Last Synced: 2024-11-18T20:54:11.857Z (2 months ago)
- Language: Jupyter Notebook
- Size: 77.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Principle-Component-Analysis-PCA---Machine-Learning
Principal Component Analysis (PCA) is a statistical technique used in machine learning and data science for dimensionality reduction. The main goal of PCA is to reduce the number of variables in a dataset while preserving as much information as possible.