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🚀 Train Loan Prediction Models with Synthetic Data using CTGAN
📊 | #FinTech #MachineLearning #DataScience #SyntheticData #CTGAN
In real-world financial environments, access to high-quality, privacy-compliant loan data can be extremely limited due to regulatory and ethical constraints.
That’s why in my latest FinTech ML project, I explore how to train accurate loan prediction models using synthetic datasets generated by CTGAN (Conditional Tabular GAN).
đź’ˇ Why this matters:
Maintain data privacy without sacrificing model realism
Generate diverse borrower profiles and edge cases
Build ML-ready datasets with class balance and feature richness
🔍 What’s covered:
Simulate loan application data (income, credit score, loan amount, status, etc.)
Generate synthetic records using CTGAN from SDV
Train and evaluate classification models (XGBoost, RandomForest)
Compare real vs synthetic model performance
đź› Tools: Python, Pandas, CTGAN, Scikit-learn, Matplotlib
Let’s advance ethical AI in finance—one synthetic sample at a time.
đź’¬ Curious to try synthetic data in your projects? Drop your thoughts or questions below!
https://youtu.be/cqGLJsOpNPU
BY Epython Lab
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