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 (10 months ago)
• Default Branch: main
• Last Pushed: 2024-07-08T12:18:29.000Z (10 months ago)
• Last Synced: 2025-01-19T18:11:42.560Z (4 months ago)
• Language: Jupyter Notebook
• Size: 77.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# 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.