Purpose

This section evaluates the data preprocessing pipeline's effectiveness in preparing the network traffic dataset for analysis. A 100% retention rate indicates no data loss occurred during cleaning, which is critical for maintaining the integrity of statistical tests and pattern detection across the 8,000 events.

Key Findings

Retention Rate: 100% (8,000 of 8,000 rows retained) - No observations were removed during preprocessing, suggesting either pristine data quality or minimal validation criteria applied
Rows Removed: 0 - Complete dataset preservation indicates no missing values, duplicates, or outliers were flagged for exclusion
Data Integrity: Full dataset availability enables robust statistical testing, including chi-square independence tests (p < 0.001) and Kruskal-Wallis analysis across 16 event types

Interpretation

The perfect retention rate reflects a dataset that required minimal cleaning intervention. This is advantageous for maintaining statistical power in hypothesis testing and preserving the natural distribution of network traffic patterns. However, the absence of any filtering suggests either the source data was exceptionally clean or preprocessing thresholds were lenient. The complete dataset enabled reliable detection of the strong source-protocol association (Cramér's V = 0.729) and identification of temporal anomalies.

Context

No train-test split information is provided, limiting visibility into validation methodology