💳 Credit Scoring Model

This project is part of my CodeAlpha Machine Learning Internship, where I developed a machine learning model to predict an individual's likelihood of credit default using financial data.

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📊 Project Highlights

• Predicts credit default risk based on financial and demographic features.
• Implemented Logistic Regression and Random Forest Classifier.
• Evaluated models with Precision, Recall, F1-Score, ROC-AUC.
• Visualized most influential features for model interpretability.

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🗂 Dataset

• Source: UCI Credit Card Default Dataset (UCI_Credit_Card.csv)
• Contains client demographic data, credit history, bill statements, and payment history.
• Target Variable: default (1 = Default, 0 = No Default)

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🧠 Methodology

1. Data Preparation

   • Dropped unnecessary ID column.
   • Renamed target column from default.payment.next.month to default.
   • Split data into training (80%) and testing (20%).

2. Model Training

   • Logistic Regression (baseline model).
   • Random Forest Classifier (ensemble method).

3. Evaluation Metrics

   • Confusion Matrix
   • Classification Report (Precision, Recall, F1-score)
   • ROC-AUC Score

4. Feature Importance

   • Identified top predictors influencing credit default.

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🛠 Tools & Libraries

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn

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📈 Results

• Best ROC-AUC Score: ~0.75 (Random Forest)
• Top Predictors: PAY_0, AGE, BILL_AMT1–6, LIMIT_BAL

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📷 Visual Outputs

Top 10 Important Features

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💼 Internship Details:

This project was completed as part of the Machine Learning Internship at CodeAlpha.