Purpose

This section documents the data quality and retention outcomes during preprocessing for the PCA analysis. Perfect retention is critical for dimensionality reduction, as PCA requires complete feature matrices to compute meaningful variance structures across all 200 marketing observations.

Key Findings

Retention Rate: 100% (200/200 rows preserved) - All observations successfully passed quality checks with no exclusions
Rows Removed: 0 - No data loss occurred during cleaning or standardization procedures
Data Completeness: Full dataset available for PCA computation across all 4 marketing features
Train/Test Split: Not applicable - PCA is unsupervised and operates on the complete dataset without partitioning

Interpretation

The perfect retention rate indicates robust data quality in the marketing spend and sales dataset. No missing values or anomalies triggered removal, allowing the full 200-observation sample to contribute to principal component calculations. This maximizes statistical power for identifying variance dimensions and ensures the 75.4% cumulative variance explained by PC1 and PC2 is based on complete information rather than imputed or filtered data.

Context

While 100% retention is favorable, the analysis assumes features were already standardized (scale_data=TRUE) during PCA execution. The lack of train/test splitting reflects PCA's unsupervised nature; however, this means no independent validation