Pca retain column names1/13/2024 To load the Iris dataset from Scikit-learn, let’s load features and targets as arrays stored in their respective X and y variables. The iris dataset contains 4 features (predictor variables) describing 3 species of flowers (targets). The Iris dataset is useful to visualize how Principal Component Analysis works. ![]() Give Names for Your Plot by Mapping targets to Principal Components.Perform Dimension Reduction using PCA in Python.We will now install Scikit-learn and load the built-in Iris dataset. In this Python tutorial, we will perform principal component analysis on the Iris dataset using Scikit-learn. ![]() Getting Started with Principal Component Analysis in Python ![]() Plt.scatter(X_pca, X_pca, c=y, cmap='viridis', edgecolor='k') # Apply PCA with two components (for 2D visualization)
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