Purpose

Data Completeness: 500 observations with zero missing values and zero duplicates\u2014dataset is clean and ready for analysis
Outlier Prevalence: 348 outliers detected (69.6% of data points), concentrated in numeric variables with high skewness (8 skewed variables identified)
Target Imbalance: Binary target shows moderate imbalance (84.4% \"No\" vs. 15.6% \"Yes\"; ratio 5.41:1)\u2014requires monitoring for model bias
Predictive Signals: 10 numeric variables show significant associations with target; 5 categorical variables significantly associated (chi-square tests, p<0.05)
Correlation Structure: 7 high correlations (\u22650.7) identified; top predictor is numeric_14 (r=0.69)

This section documents the data preprocessing pipeline, showing that all 500 observations were retained without any rows removed during cleaning. This perfect retention rate indicates either exceptionally clean source data or minimal preprocessing interventions, which is critical context for understanding the reliability of downstream statistical tests and predictive modeling efforts.

Key Findings

Retention Rate: 100% (500/500 rows preserved) - No observations were excluded during quality checks or cleaning procedures
Rows Removed: 0 - Aligns with earlier findings showing zero missing values, zero duplicates, and zero type mismatches across all 27 variables
Data Integrity: Complete dataset preservation suggests the raw data met quality thresholds without requiring imputation, deduplication, or outlier removal

Interpretation

The perfect retention rate reflects the high baseline quality of the input dataset. Combined with the earlier analysis showing zero missing values, zero exact duplicates, and no data type mismatches, this indicates minimal data quality friction. However, this also means no rows were filtered despite detecting 348 outliers (69.6% of observations), suggesting outliers were retained for analysis rather than treated as errors—a deliberate choice that preserves variance but may inflate standard errors in statistical tests.

Context

The absence of a documented train/test split suggests this analysis is exploratory rather than predictive modeling. The complete dataset retention is