The major steps which are to be followed while using the PCA algorithm are as follows:
Step-1: Get the dataset.
Step-2: Compute the mean vector (µ).
Step-3: Subtract the means from the given data.
Step-4: Compute the covariance matrix.
Step-5: Determine the eigenvectors and eigenvalues of the covariance matrix.
Step-6: Choosing Principal Components and forming a feature vector.
Step-7: Deriving the new data set by taking the projection on the weight vector.