Purpose

This section documents the data preprocessing pipeline for the Elastic Net regression analysis. It shows that all 300 observations were retained without any rows removed during cleaning, indicating either pristine input data or minimal preprocessing requirements. Understanding data retention is critical for assessing whether the final model is trained on a representative sample and whether any systematic data loss could bias the regression results.

Key Findings

Initial Rows: 300 observations with no exclusions during preprocessing
Retention Rate: 100% — all records passed quality checks and were included in model training
Rows Removed: 0 — no filtering, outlier removal, or missing value imputation was necessary
Train/Test Split: Not explicitly documented, though cross-validation (10-fold CV) was applied during model fitting

Interpretation

The perfect retention rate suggests the input dataset was clean and complete, with no missing values, duplicates, or quality issues requiring remediation. This is favorable for model reliability, as the full sample size of 300 observations supports the Elastic Net's ability to estimate 6 predictors and achieve an R² of 0.834. However, the absence of documented train/test splits means model performance metrics (RMSE=5.035, MAE=4.106) reflect cross-validated estimates rather than holdout validation.

Context

The 10-fold cross-