Purpose

This section documents the data cleaning and preparation phase for the ridge regression model predicting sales from multi-channel advertising spend. Perfect data retention (100%) indicates no observations were removed during preprocessing, meaning the full dataset of 200 records was available for model training and evaluation.

Key Findings

Initial Rows: 200 observations with no missing values in predictors or Sales columns
Final Rows: 200 observations retained after preprocessing
Retention Rate: 100% — no data loss occurred during cleaning
Data Split: Not explicitly documented in preprocessing; cross-validation (10 folds) was used instead of traditional train/test split

Interpretation

The absence of data removal suggests the dataset was clean and complete at intake, with no missing values requiring exclusion. This full retention is favorable for model stability and statistical power. However, the lack of a documented train/test split indicates the model evaluation relied entirely on cross-validation metrics rather than holdout validation, which may slightly overestimate generalization performance compared to independent test set evaluation.

Context

The preprocessing quality directly supports the model's R² of 0.763 and RMSE of 1310.17, as no data quality issues compromised the analysis. The 10-fold cross-validation approach (evident from the cv_plot_data) substitutes for explicit train/test documentation but provides robust