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Academic project
Fraud Detection and Model Benchmarking
Applied Machine Learning Project
Random ForestSVMk-NNLogistic RegressionGradient BoostingMLPROC-AUC
Technical approach
- Random Forest
- Support Vector Machine
- k-nearest neighbours
- Logistic Regression
- Gradient Boosting
- Multi-layer perceptron
- Scaling
- Outlier handling
- Cross-validation
- ROC-AUC
- Confusion-matrix analysis
Limitations
- Imbalanced-class evaluation
- False-positive and false-negative trade-offs
- Need for human review
- Responsible use in risk-sensitive decisions
Visuals and artefacts
Visual placeholder
ROC and confusion-matrix visuals
Asset →
/images/projects/fraud-detection-results.pngTechnologies
Random ForestSVMk-NNLogistic RegressionGradient BoostingMLPROC-AUC