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Design real-time data science project
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Module 3
Comparative Modeling and Selection
1
Handling Imbalanced Data with SMOTE
Implement a modeling pipeline that incorporates a technique to address class imbalance (e.g., SMOTE).
2
Model Selection: Logistic Regression vs. XGBoost
Train and compare a baseline model (Logistic Regression) with a complex model (XGBoost) to select the final model based on the pre-defined evaluation metrics.
Previous module
Strategic Data Preparation for Credit Risk
Next module
Interpretation, Reporting, and Deployment Strategy