# Python Data Science Project Note Guidelines

## Data Processing Decisions

- ETL pipeline design choices
- Data cleaning and validation strategies
- Feature engineering rationale
- Handling missing data approaches

## Model Development

- Algorithm selection criteria
- Hyperparameter tuning strategies
- Cross-validation approaches
- Model evaluation metrics choices

## Performance Optimizations

- Vectorization strategies
- Memory management for large datasets
- Parallel processing decisions
- GPU utilization patterns

## Reproducibility

- Random seed management
- Environment configuration
- Data versioning strategies
- Experiment tracking approaches