Purpose

This section provides a structural overview of the dataset's composition, identifying which columns are numeric versus categorical. Understanding column types is foundational for selecting appropriate statistical methods, interpreting distributions, and detecting data quality issues across the 500-row dataset.

Key Findings

Total Columns: 31 columns analyzed with clear type separation
Numeric Columns: 12 columns suitable for correlation, regression, and outlier detection
Categorical Columns: 19 columns appropriate for frequency analysis and cross-tabulation
Missing Data: 0% overall missing rate indicates complete data integrity across all columns
Data Quality Score: 97.7% reflects high-quality, analysis-ready data with minimal anomalies

Interpretation

The dataset demonstrates balanced composition between numeric and categorical variables, enabling comprehensive multivariate analysis. The absence of missing values eliminates imputation concerns and ensures all 500 observations are usable across all 31 dimensions. The high data quality score validates that the subsequent correlation analysis (4 high-correlation pairs identified) and outlier detection (6 columns with outliers) are based on reliable, complete information.

Context

The column profile table appears empty in the provided summary, suggesting detailed per-column statistics are available elsewhere in the analysis framework. The type detection logic applied here (numeric >10 unique values; categorical ≤10