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PCA (Principal Component Analysis) Pro
Comprehensive dimensionality reduction with variance analysis and visualization
PCA Configuration
Maximum: 4
Keep components until this variance is explained (0-1)
Dataset Information
Samples
6
Features
4
Dataset
My Dataset
PCA Results
Dimension Reduction
4D → 2D
50.0% reduction
Explained Variance
93.35%
of total variance retained
Variance by Component
PC1:(67.6%)
67.6%
PC2:(93.4%)
25.8%
Data Preview (6 samples)
| # | Feature 1 | Feature 2 | Feature 3 | Feature 4 | Label | Actions |
|---|---|---|---|---|---|---|
| 1 | 5.10 | 3.50 | 1.40 | 0.20 | Setosa | |
| 2 | 4.90 | 3.00 | 1.40 | 0.20 | Setosa | |
| 3 | 7.00 | 3.20 | 4.70 | 1.40 | Versicolor | |
| 4 | 6.40 | 3.20 | 4.50 | 1.50 | Versicolor | |
| 5 | 6.30 | 3.30 | 6.00 | 2.50 | Virginica | |
| 6 | 5.80 | 2.70 | 5.10 | 1.90 | Virginica |
Related Topics & Algorithms
Explore these related algorithms and concepts to deepen your understanding and discover complementary techniques.
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Fundamentals
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Regression
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Regression with principal components
Deep Learning
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