/Credit-Risk-Prediction

This Jupyter Notebook focuses on credit risk prediction using a Random Forest Classifier. It covers data preprocessing, exploratory data analysis (EDA), model training, and handling class imbalance. Additionally, essential metrics such as precision, recall, F1-score, and confusion matrices are computed to evaluate the model's performance.

Primary LanguageJupyter Notebook

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