To overcome the difficulties of high-dimensional data, Elastic Net Principal Component Regression (ENPCR), a potent statistical technique, combines Elastic Net regularization with Principal Component regression (PCR). When dealing with Multicollinearity among predictors, this method is especially helpful because it enables efficient variable selection while preserving interpretability. PCA is initially used in ENPCR to reduce the dataset's dimensionality by converting correlated variables into a group of uncorrelated principal components. The Elastic Net regression model then uses these elements as inputs and penalizes the regression coefficients using both L1 and L2 penalties. By promoting sparsity, this dual regularization lessens overfitting and helps the model concentrate on its most important components. simulated studies and Real datasets are used to demonstrate the our proposed method .

This paper will employ a novel approach that builds upon the lasso method, utilizing it in two stages.   The first stage applies to the principal components to select the important principal component and exclude the unimportant ones. This technique is effective in identifying significant principal components while attempting to eliminate bias in selecting these components over others. Additionally, it removes the ranking in determining the principal components compared to classical methods.  Moreover, the second stage involves determining the effective importance within each component by zeroing out the scores loading values within each component. To compare the performance of the proposed method in principal component analysis, a simulation approach can be used. Subsequently, the performance of the proposed method is tested using real data.