1.9 KiB
1.9 KiB
Data Science Project Checklist
Problem Definition & Data Requirements
- Business problem is clearly defined
- Success metrics are established
- Required data sources are identified
- Data access methods are established
- Privacy and compliance requirements are identified
- Project timeline and resources are planned
Data Collection & Exploration
- Data collection pipeline is established
- Data quality assessment is completed
- Exploratory data analysis is performed
- Data distributions and relationships are understood
- Missing data strategy is defined
- Outlier handling approach is determined
Feature Engineering & Preprocessing
- Feature selection/creation strategy is defined
- Data transformations are implemented
- Feature scaling/normalization is applied where needed
- Categorical encoding is implemented appropriately
- Data splitting strategy (train/test/validation) is defined
- Data preprocessing pipeline is reproducible
Model Development
- Appropriate algorithms are selected for the problem
- Baseline models are established
- Hyperparameter tuning strategy is defined
- Model evaluation metrics are appropriate for problem
- Cross-validation approach is implemented
- Model interpretability requirements are met
Model Validation & Testing
- Models are evaluated on holdout data
- Performance meets business requirements
- Model generalization is assessed
- Model bias and fairness are evaluated
- Model limitations are documented
- A/B testing plan is defined (if applicable)
Deployment & Monitoring
- Model deployment approach is defined
- Model versioning is implemented
- Inference performance is acceptable
- Monitoring for model drift is established
- Retraining strategy is defined
- Feedback loop for model improvement is established