Integrate a Principal Component Analysis (PCA) step to reduce the dimensionality and capture the essential features of the data. Ensure the following:
Explained Variance: Visualize the explained variance to justify the number of principal components retained.
Scalability: Ensure the PCA implementation scales well with the dataset size.
Reusability: Encapsulate the PCA steps in a function for reusability and clarity.
Documentation: Include documentation explaining the PCA process and its impact on the model performance.
Integrate a Principal Component Analysis (PCA) step to reduce the dimensionality and capture the essential features of the data. Ensure the following:
Explained Variance: Visualize the explained variance to justify the number of principal components retained. Scalability: Ensure the PCA implementation scales well with the dataset size. Reusability: Encapsulate the PCA steps in a function for reusability and clarity. Documentation: Include documentation explaining the PCA process and its impact on the model performance.