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| Section | Weight | Objectives |
|---|---|---|
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Evaluate the Model | 15% | - Validate model generalizability - Assess classification/regression metrics - Identify bias and overfitting |
| Prepare the Data | 18% | - Feature engineering and selection - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Handle missing values and outliers |
| Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis |
| Build the Model | 20% | - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS - Compare and select best performing models - Select appropriate ML algorithms |
| Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints |
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