Large-Scale Fraud Classification
This project explored fraud classification on a large, high-dimensional dataset with more than one million records and hundreds of features. The main challenge was not only model selection, but also the practical work required to prepare data at scale, reduce noise, and evaluate classification performance responsibly.
Data-science focus
Fraud data is usually imbalanced, noisy, and operationally sensitive. The analysis focused on the core modeling workflow: feature preparation, class-balance awareness, model comparison, validation, and interpretation of model behavior.
What the project demonstrates
- Working with large tabular data
- Preparing high-dimensional features for classification
- Comparing machine-learning models under realistic constraints
- Thinking about model quality beyond raw accuracy
Repository note
The public GitHub repository is available at IEEE_Fraud_Detection. The full production-style tuned pipeline is not included in the public version, but the project remains useful as a portfolio example of large-scale classification framing.