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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
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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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Andrey, a Russian entrepreneur living in Brazil who, fearing retaliation, asked that NPR not use his last name, said Telegram has become one of the few places Russians can access independent news about the war. As the war in Ukraine rages, the messaging app Telegram has emerged as the go-to place for unfiltered live war updates for both Ukrainian refugees and increasingly isolated Russians alike. Two days after Russia invaded Ukraine, an account on the Telegram messaging platform posing as President Volodymyr Zelenskiy urged his armed forces to surrender. READ MORE At the start of 2018, the company attempted to launch an Initial Coin Offering (ICO) which would enable it to enable payments (and earn the cash that comes from doing so). The initial signals were promising, especially given Telegram’s user base is already fairly crypto-savvy. It raised an initial tranche of cash – worth more than a billion dollars – to help develop the coin before opening sales to the public. Unfortunately, third-party sales of coins bought in those initial fundraising rounds raised the ire of the SEC, which brought the hammer down on the whole operation. In 2020, officials ordered Telegram to pay a fine of $18.5 million and hand back much of the cash that it had raised.
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