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 .
