🟪 1-Minute Summary
After completing EDA, you must synthesize findings into conclusions that guide modeling decisions. Key outputs: (1) Data quality assessment, (2) Feature insights (which matter, which don’t), (3) Recommended transformations, (4) Potential model approaches, (5) Known limitations. Good conclusions bridge EDA and modeling.
🟦 Core Notes (Must-Know)
What to Conclude From EDA
Data Quality Summary
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Feature Insights
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Distribution Patterns
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Relationship Findings
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Recommended Next Steps
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“After EDA, how do you decide which features to keep?”
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“What would make you choose a non-linear model during EDA?”
- [Answer: Non-linear relationships in scatter plots, interactions]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Conclusions without supporting evidence
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Mistake 2: Not connecting EDA to business problem
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🟩 Mini Example (Quick Application)
Scenario
[Sample EDA conclusion report]
Solution
# EDA Conclusions for [Dataset Name]
## Data Quality
- Total rows: X, Total features: Y
- Missing values: [list critical ones]
- Duplicates: [count]
- Data quality score: [assessment]
## Key Findings
1. [Finding 1]
2. [Finding 2]
...
## Recommended Actions
- Clean: [specific steps]
- Transform: [which features, how]
- Engineer: [new features to create]
- Model approach: [suggestions based on patterns]
🔗 Related Topics
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