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Used logistic regression to predict loan status with a focus on precision and recall for high-risk loans.\n\n## Tools \u0026 Technologies Used\n\n- Python\n- Pandas\n- Scikit-learn (Logistic Regression)\n- Classification Report\n- Confusion Matrix\n- Jupyter Notebooks\n\n## File Structure\n\n```text\n.\n├── Credit_Risk/\n│   ├── credit_risk_classification.ipynb    # Full model workflow\n│   └── lending_data.csv                    # Loan dataset\n```\n\n## Skills Demonstrated\n\n- Binary classification using Logistic Regression\n- Data preparation and feature engineering\n- Splitting data into training and testing sets\n- Evaluating model accuracy, precision, recall, and F1-score\n- Generating confusion matrix and classification report\n\n## Key Findings\n\n- Analyzed 19,384 loans, classifying them as healthy or high-risk.\n- Model achieved 100% precision and F1-score for healthy loans (label 0).\n- Model achieved 84% precision and 89% F1-score for high-risk loans (label 1), with 94% recall.\n- Model tends to predict healthy loans extremely well but occasionally misclassifies high-risk loans as healthy.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffbarffmann%2Fcredit-risk-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffbarffmann%2Fcredit-risk-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffbarffmann%2Fcredit-risk-classification/lists"}