The steps of dimensionality reduction using PCA is given below, arrange them in proper order

Transform the Old variables using the Eigenvectors to new variables.

Calculate Eigenvectors $($principal components$)$ and Eigenvalues $($variance explained by the Eigenvectors$).$

Normalize the data and then calculate the Covariance matrix.

Decide the number of dimensions to reduce based on the variance explained by the principal components.