This article shows practising PCA with Iris dataset. More detail about PCA, read the post Principal Component Analysis. Preparation Import libraries Load and visualize the dataset Convert data to numpy array. x has shape (150, 4), corresponding to observation in rows and variables in columns. Compute PCA Calculate the covariance matrix. Besides np.cov of numpy,… Continue reading Principle component analysis (PCA) example: Experiments with Iris Dataset
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