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