Evidence before impressive scores
Student Placement Prediction
Use synthetic data to practise leakage-safe splitting, preprocessing, a baseline, logistic regression, validation-only threshold choice and test evaluation.
- Reproducible synthetic dataset
- Pipeline and missing-value handling
- Metric and coefficient interpretation
House-Price Prediction
Develop a regression pipeline, compare a median baseline, select complexity on validation and audit residuals and empirical uncertainty.
- Numeric and categorical preprocessing
- MAE, RMSE and R²
- Residual range and slice audit
Medical Diagnosis Classification
Audit a teaching classifier with stratified validation, sensitivity, specificity, threshold trade-offs and explicit clinical limitations.
- Positive-class definition
- Cross-validation and held-out test
- False-negative review and governance
Every result must be defensible
1. Frame
Define the decision, label and error costs.
2. Separate
Protect validation and test data from training.
3. Compare
Beat a meaningful baseline on chosen metrics.
4. Govern
Document limitations, monitoring and oversight.
Original teaching content with transparent sources
The placement and housing data are generated synthetically. The scenarios, explanations, code structure, traces, questions and evaluation workflows were written for CodeBhavya. The medical program transparently uses scikit-learn’s public teaching dataset and links to its official documentation; datasets and standard algorithms are not presented as original CodeBhavya inventions.