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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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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. In this regard, Sebi collaborated with the Telecom Regulatory Authority of India (TRAI) to reduce the vulnerability of the securities market to manipulation through misuse of mass communication medium like bulk SMS. The War on Fakes channel has repeatedly attempted to push conspiracies that footage from Ukraine is somehow being falsified. One post on the channel from February 24 claimed without evidence that a widely viewed photo of a Ukrainian woman injured in an airstrike in the city of Chuhuiv was doctored and that the woman was seen in a different photo days later without injuries. The post, which has over 600,000 views, also baselessly claimed that the woman's blood was actually makeup or grape juice. Again, in contrast to Facebook, Google and Twitter, Telegram's founder Pavel Durov runs his company in relative secrecy from Dubai. In a statement, the regulator said the search and seizure operation was carried out against seven individuals and one corporate entity at multiple locations in Ahmedabad and Bhavnagar in Gujarat, Neemuch in Madhya Pradesh, Delhi, and Mumbai.
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