Hi Shahd,
Steps to Perform PCA from Scratch -
1. Load the Iris Dataset
2. Standardize the Data - Standardize the features to have zero mean and unit variance.
3. Compute the Covariance Matrix - Compute the covariance matrix of the standardized data.
4. Compute Eigenvalues and Eigenvectors - Compute the eigenvalues and eigenvectors of the covariance matrix.
5. Sort Eigenvalues and Corresponding Eigenvectors - Sort the eigenvalues in descending order and sort the eigenvectors accordingly.
6. Select Top `k` Eigenvectors - Select the top `k` eigenvectors (where `k` is the number of dimensions you want to reduce to, in this case, 2).
7. Transform the Data - Transform the original data to the new subspace using the selected principal components.
Hope this helps.
