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A Step By Step Implementation of Principal Component Analysis Principal Component Analysis or PCA is a commonly used dimensionality reduction method It works by computing the principal components and performing a change of basis It retains the data in the direction of maximum variance The reduced features are uncorrelated with each other
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Step-By-Step Guide to Principal Component Analysis With Example In this section, you will get to know about the steps involved in the Principal Component Analysis technique STEP 1: STANDARDIZATION The range of variables is calculated and standardized in this process to analyze the contribution of each variable equally
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Understanding Principle Component Analysis(PCA) step by step. Steps Involved in the PCA Step 1: Standardize the dataset Step 2: Calculate the covariance matrix for the features in the dataset Step 3: Calculate the eigenvalues and eigenvectors for the