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Dimensionality ReductionIntermediateStandardScalerPCA
PCA Dimensionality Reduction
Reduce Iris dataset from 4D to 2D and visualize class separation.
Key highlights
- Standardizes features before PCA
- Projects to 2 components for visualization
- Color-coded by species
Metrics
EigenvaluesExplained variance
Outputs
Iris PCA projection in 2D
Code snapshot
Key lines that anchor the experiment workflow.
df_scaled = scaler.fit_transform(df[features])
pca = PCA(n_components=2)
principal_components = pca.fit_transform(df_scaled)Next steps
Want to explore further? Try the full gallery or open the raw script to tweak parameters.