Iris Classification with PCA

Dimensionality reduction and classification on the classic Iris flower dataset using PCA and Logistic Regression.

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Dataset

• Source: load_iris() from scikit-learn
• Classes: Setosa, Versicolor, Virginica
• Features: 4 numeric (sepal & petal length/width)

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PCA (Principal Component Analysis)

• Reduced original 4D data to 2D using PCA
• Helped visualize how well classes are separated
• Explained variance: ⬜ 95–99% with 2 components

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Model

• Algorithm: Logistic Regression
• Accuracy: ⬜ ~0.97 (actual value after training)
• Evaluated using accuracy and classification report

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Visualization

• 2D PCA scatter plot shows class separation
• Colored points by class label

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Tools

• Python, pandas, scikit-learn, matplotlib, seaborn

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✅ How to Run

pip install pandas scikit-learn matplotlib seaborn
python iris_pca_classifier.ipynb  # or run in Colab