Developed a credit risk prediction model using Kaggle’s Home Credit dataset (~200k rows, 122 features). Applied EDA, preprocessing, SMOTE for class imbalance, and tested multiple ML models. Logistic Regression achieved the best ROC AUC (0.6757), providing insights to improve lending practices and financial inclusion.
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Developed a credit risk prediction model using Kaggle’s Home Credit dataset (~200k rows, 122 features). Applied EDA, preprocessing, SMOTE for class imbalance, and tested multiple ML models. Logistic Regression achieved the best ROC AUC (0.6757), providing insights to improve lending practices and financial inclusion.
shiviesaksenaa06/Home-Credit-Risk
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Developed a credit risk prediction model using Kaggle’s Home Credit dataset (~200k rows, 122 features). Applied EDA, preprocessing, SMOTE for class imbalance, and tested multiple ML models. Logistic Regression achieved the best ROC AUC (0.6757), providing insights to improve lending practices and financial inclusion.
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