4.6 KiB
EDA Report: [Dataset Name]
Date: [Date] | Analyst: [Name]
Executive Summary
[Concise summary of key findings and recommendations]
Key Findings:
- [Finding 1]
- [Finding 2]
- [Finding 3]
Recommendations:
- [Recommendation 1]
- [Recommendation 2]
1. Dataset Overview
Source: [Source name] | Format: [CSV/Excel/JSON/etc.] | Period: [Date range]
Structure: [Rows] observations × [Columns] variables | Memory: [Size] MB
Variable Types:
- Numeric ([Count]): [List names]
- Categorical ([Count]): [List names]
- Datetime ([Count]): [List names]
- Boolean ([Count]): [List names]
2. Data Quality
Completeness: [Percentage]% | Duplicates: [Count] ([%]%)
Missing Data:
| Column | Missing % | Assessment |
|---|---|---|
| [Column 1] | [%] | [High/Medium/Low] |
| [Column 2] | [%] | [High/Medium/Low] |
Quality Issues:
- [Issue 1]
- [Issue 2]
3. Univariate Analysis
Numeric: [Variable Name]
Stats: Mean: [Value] | Median: [Value] | Std: [Value] | Range: Min]-[Max
Distribution: Skewness: [Value] | Kurtosis: [Value] | Normality: [Yes/No]
Outliers: IQR: [Count] ([%]%) | Z-score: [Count] ([%]%)
Insights: [Key observations]
Categorical: [Variable Name]
Stats: [Count] unique values | Most common: [Value] ([%]%) | Balance: [Balanced/Imbalanced]
| Category | Count | % |
|---|---|---|
| [Cat 1] | [Count] | [%] |
| [Cat 2] | [Count] | [%] |
Insights: [Key observations]
Temporal: [Variable Name]
Range: [Start] to [End] ([Duration]) | Trend: [Increasing/Decreasing/Stable] | Seasonality: [Yes/No]
Insights: [Key observations]
4. Bivariate Analysis
Correlation Summary: [Count] strong positive | [Count] strong negative | [Count] weak/none
Notable Correlations:
| Var 1 | Var 2 | Pearson | Spearman | Strength |
|---|---|---|---|---|
| [Var 1] | [Var 2] | [Value] | [Value] | [Strong/Moderate/Weak] |
Insights: [Multicollinearity issues, feature engineering opportunities]
Key Relationship: [Var 1] vs [Var 2]
Type: [Linear/Non-linear/None] | r: [Value] | p-value: [Value]
Insights: [Description and implications]
5. Multivariate Analysis
Patterns: [Key observations]
Clustering (if performed): [Method] | [Count] clusters identified
6. Outliers
Overall Rate: [%]%
| Variable | Outlier % | Method | Action |
|---|---|---|---|
| [Var 1] | [%] | [IQR/Z-score] | [Keep/Investigate/Remove] |
| [Var 2] | [%] | [IQR/Z-score] | [Keep/Investigate/Remove] |
Investigation: [Description of significant outliers, causes, validity]
7. Key Insights
Data Quality:
- [Insight with implication]
- [Insight with implication]
Statistical Patterns:
- [Insight with implication]
- [Insight with implication]
Domain/Research Insights:
- [Insight with implication]
- [Insight with implication]
Unexpected Findings:
- [Finding and significance]
8. Recommendations
Data Quality Actions:
- [Action - priority]
- [Action - priority]
Next Steps:
- [Step with rationale]
- [Step with rationale]
Feature Engineering:
- [Opportunity]
- [Opportunity]
Modeling Considerations:
- [Consideration]
- [Consideration]
9. Limitations
Data: [Key limitations]
Analysis: [Key limitations]
Assumptions: [Key assumptions made]
Appendices
A: Technical Details
Environment: Python with pandas, numpy, scipy, matplotlib, seaborn
Scripts: [Repository/location]
B: Variable Dictionary
| Variable | Type | Description | Unit | Range | Missing % |
|---|---|---|---|---|---|
| [Var 1] | [Type] | [Description] | [Unit] | [Range] | [%] |
C: Statistical Tests
Normality:
| Variable | Test | Statistic | p-value | Result |
|---|---|---|---|---|
| [Var 1] | Shapiro-Wilk | [Value] | [Value] | [Normal/Non-normal] |
Correlations:
| Var 1 | Var 2 | r | p-value | Significant |
|---|---|---|---|---|
| [Var 1] | [Var 2] | [Value] | [Value] | [Yes/No] |
