OSEMN Framework : The Data Scientist’s Obsessive Process
The OSEMN framework (Obtain, Scrub, Explore, Model, iNterpret) provides a battle-tested structure for data science projects. Used by 72% of top Kaggle competitors, this 5-step approach ensures nothing gets overlooked.
1. Obtain: Data Acquisition
Modern Sources:
- APIs (Twitter, Google Analytics)
- Web scraping (BeautifulSoup)
- Public datasets (Kaggle, Google Dataset Search)
Pro Tip: Always document data provenance – critical for reproducibility.
2. Scrub: Data Cleaning
Common Tasks:
python
# Handle missing values df.fillna(method='ffill', inplace=True) # Remove duplicates df.drop_duplicates(subset=['user_id'], keep='last')
2024 Tools:
- Great Expectations (validation)
- PyJanitor (cleaning)
3. Explore: EDA
Must-Create Visualizations:
- Distribution plots
- Correlation matrices
- Geospatial mappings
Critical Question: “What patterns violate our assumptions?”
4. Model: Algorithm Selection
| Problem Type | First-Algorithm Choices |
|---|---|
| Classification | Random Forest |
| Regression | XGBoost |
| Clustering | K-Means |
5. iNterpret: Business Insights
Avoid This Mistake: “The model has 92% accuracy” → Instead: “This reduces customer churn by $1.2M annually”
3. OSEMN in Action: Retail Case Study
Project: Optimize supermarket layouts
- Obtain: 6 months of IoT foot traffic data
- Scrub: Fixed 12% missing sensor readings
- Explore: Discovered 3pm congestion hotspot
- Model: GNN predicted optimal product placement
- iNterpret: Projected 15% sales increase
(Source: Towards Data Science)
4. 2024 Adaptations
- Obtain: Synthetic data generation
- Scrub: AI-assisted cleaning (GPT-4 for text)
- Interpret: SHAP values for executives
5. Free Resources
- [Download]: OSEMN Checklist PDF
- [Template]: Jupyter Notebook Skeleton
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