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https://github.com/ojas-arora/principal-component-analysis
https://github.com/ojas-arora/principal-component-analysis
Last synced: about 11 hours ago
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- Host: GitHub
- URL: https://github.com/ojas-arora/principal-component-analysis
- Owner: Ojas-Arora
- Created: 2024-11-05T12:02:11.000Z (3 days ago)
- Default Branch: main
- Last Pushed: 2024-11-05T13:12:26.000Z (3 days ago)
- Last Synced: 2024-11-05T13:31:44.271Z (3 days ago)
- Language: Jupyter Notebook
- Size: 243 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 📊 Principal Component Analysis (PCA)
## 📊 Overview
Principal Component Analysis (PCA) is a powerful dimensionality reduction technique used in data analysis and machine learning. 🌟 It transforms a dataset into a set of linearly uncorrelated variables called principal components, which capture the most variance in the data. 📉 This simplification makes complex datasets more manageable and interpretable.
## 🛠️ Key Features
- **Dimensionality Reduction:** 📉 Reduces the number of features while retaining essential information, making datasets easier to analyze and visualize.
- **Noise Reduction:** 🚫 By eliminating less important features, PCA reduces noise and improves model performance.
- **Data Visualization:** 🖼️ Enables the visualization of high-dimensional data in 2D or 3D, revealing patterns and insights.
## 📈 How PCA Works
- **Standardize the Data:** 🌐 Center the dataset to have a mean of zero and scale it to have unit variance.
- **Calculate the Covariance Matrix:** 🧮 Understand relationships between features and how they vary together.
- **Compute Eigenvalues and Eigenvectors:** 📊 Identify the direction and magnitude of variance in the dataset.
- **Select Principal Components:** 🎯 Choose the top components that capture the most variance, facilitating reduced-dimensionality analysis.