Learning Objectives

• Create project presentation
• Write comprehensive documentation
• Prepare demo

1. Problem Statement (1 slide): What problem? Why does it matter?
2. Data Overview (1-2 slides): Source, size, features, target distribution
3. EDA Findings (2-3 slides): Key insights, patterns, correlations
4. Methodology (1-2 slides): Preprocessing, feature engineering, algorithms tried
5. Results (1-2 slides): Model comparison, final metrics, confusion matrix
6. Interpretation (1 slide): Most important features, SHAP summary
7. Demo (2-3 min): Live prediction or walkthrough
8. Conclusion (1 slide): Key takeaways, limitations, future work

• Problem clearly defined
• EDA with visualizations
• Data preprocessing pipeline
• Multiple models compared
• Hyperparameter tuning
• Model interpretation (SHAP)
• Prediction API deployed
• README with complete documentation
• Presentation slides (10-15 min)
• Demo video (3-5 min)
• Code on GitHub
• Requirements.txt with versions
• Reproducible (random_state set)
• No data leakage in pipeline
• Test set only used once

Q1: What is data leakage and how to prevent?

Information from test set leaking into training. Examples: scaling before split, using future data, target encoding with test statistics. Prevention: split BEFORE preprocessing. Q2: How to ensure reproducibility?

Set random_state everywhere, version control code+data, use Docker/environment.yml, document hyperparameters and steps, log experiments. Q3: How to structure README?

Project title, description, dataset, installation, usage, results, model card, limitations, license, references. Q4: What to include in requirements.txt?

All packages with versions (pip freeze > requirements.txt). For ML: numpy, pandas, scikit-learn, matplotlib, seaborn, jupyter, shap, flask, gunicorn. Q5: How to make a good demo?

Focus on user flow: input -> prediction -> explanation. Clean UI, handle errors gracefully, show uncertainty (probabilities), compare with baseline if relevant. Q6: Example model card:

markdown
# Model Card: Credit Risk Classifier
## Model Details
- Algorithm: XGBoost
- Features: 25 (demographic + financial)
- Training data: Lending Club 2020 (50k samples)
- Performance: Accuracy 0.82, AUC 0.89, F1 0.78
- Limitations: Not calibrated for recession periods
- Fairness: Equal accuracy across gender groups