eda

name	eda
description	Exploratory Data Analysis for tabular data. Use when analyzing column distributions, checking data quality, examining class balance, detecting missing patterns, or generating summary statistics for datasets.

Exploratory Data Analysis (EDA)

Analyze tabular datasets to understand distributions, data quality, and patterns.

When to Use

• Understanding a new dataset before modeling
• Checking data quality (missing values, outliers, duplicates)
• Analyzing target variable distribution
• Identifying class imbalance
• Generating summary statistics

Analysis Process

1. Connect to data - Verify access and inspect schema
2. Analyze target variable first - Understand class balance
3. Check each column - Distribution, missing data, cardinality
4. Document findings - Save reports for reproducibility

Available Analyses

Analysis	Description
Column Distribution	Value counts, percentages, cardinality assessment
Missing Data	Null counts, patterns (MCAR/MAR/MNAR)
Class Balance	Imbalance detection for classification targets
Summary Stats	Count, unique, nulls per column

Column Distribution Analysis

For detailed analysis methodology and output format:

• See references/eda-analysis.md

Quick Reference

Cardinality Levels:

Level	Criteria	Action
Low	≤10 unique	Good for categorical encoding
Medium	11-100 or <1% of rows	May need encoding strategy
High	>100 and <50% of rows	Consider grouping/binning
Very High	>50% of rows	Likely identifier, exclude

Missing Data Thresholds:

Percentage	Assessment
0%	No missing data
<1%	Minimal - safe to drop or impute
1-5%	Some - consider imputation strategy
>5%	Significant - investigate pattern

Class Imbalance:

• 80% in top class: Imbalance detected

• 95% in top class: Extreme imbalance

Output Format

# Column Distribution: {column_name}

- **source**: path/to/data
- **column**: column_name

## Summary
- Total rows: N
- Null/missing: N (X%)
- Unique values: N
- Cardinality: Low|Medium|High|Very High

## Distribution
| Value | Count | Percentage | Cumulative |
|-------|-------|------------|------------|

## Observations
- Auto-generated insights

Best Practices

1. Start with schema inspection before deep analysis
2. Check target variable first for classification tasks
3. Missing data may not be random - investigate patterns
4. Save reports for reproducibility