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💳 Credit Risk Analysis

A comprehensive machine learning project for analyzing and predicting credit risk using multiple classification algorithms. This project implements a complete ML pipeline from exploratory data analysis to model evaluation and comparison.

📋 Table of Contents

• Overview
• Features
• Project Structure
• Dataset
• Getting Started
• Usage
• Methodology
• Results
• Technologies Used
• License

🎯 Overview

Credit risk analysis is crucial for financial institutions to minimize losses and make informed lending decisions. This project implements multiple machine learning algorithms to predict the likelihood of loan default, enabling banks and lenders to:

• Assess applicant creditworthiness accurately
• Reduce financial losses by identifying high-risk borrowers
• Optimize lending strategies through data-driven insights
• Automate risk assessment processes

The project follows a complete machine learning pipeline including data exploration, feature engineering, model training, and comprehensive evaluation.

✨ Features

• 🔍 Comprehensive EDA: Exploratory data analysis with statistical insights and visualizations
• 📊 Data Visualizations: Distribution plots, correlation matrices, and feature analysis
• 🧹 Data Preprocessing: Automated handling of missing values, outliers, and encoding
• 🔧 Feature Engineering: Creation of new features including log transforms, ratios, and polynomial features
• 🤖 Multiple ML Models: Implementation of 8+ classification algorithms
• 📈 Model Comparison: Detailed performance metrics and accuracy differences
• 🎨 Visualizations: ROC curves, confusion matrices, and performance heatmaps
• ✅ Cross-Validation: 5-fold cross-validation for robust model evaluation

📁 Project Structure

Credit Risk/
│
├── README.md                    # Project documentation
├── main .ipynb                  # Main Jupyter notebook with complete analysis
├── credit_risk_dataset.csv      # Dataset containing credit risk information
├── requirements.txt             # Python dependencies
└── .gitignore                   # Git ignore file

📊 Dataset

The project uses credit_risk_dataset.csv which contains various features related to credit risk assessment, including:

• Demographic Information: Age, employment status, income
• Financial History: Credit history, debt-to-income ratio, existing loans
• Loan Details: Loan amount, loan term, interest rate, purpose
• Target Variable: Default status (binary classification)

Dataset Statistics:

• Total records: 32,582
• Features: Multiple numeric and categorical variables
• Target: Binary classification (default/non-default)

🚀 Getting Started

Prerequisites

• Python 3.7 or higher
• Jupyter Notebook or JupyterLab
• pip (Python package manager)

🔬 Methodology

1. Exploratory Data Analysis (EDA)

• Dataset shape and structure analysis
• Missing value identification
• Statistical summary of features
• Distribution visualization for numeric and categorical features
• Correlation matrix analysis
• Target variable distribution

2. Data Preprocessing

• Missing Value Treatment: Median imputation for numeric, mode for categorical
• Outlier Detection: IQR method for outlier clipping
• Encoding: Label encoding for categorical variables
• Scaling: RobustScaler for feature normalization

3. Feature Engineering

• Log transformations for skewed features
• Ratio features (debt-to-income, total interest)
• Polynomial features (squared, square root)
• Domain-specific feature creation

4. Model Training

The project implements and compares multiple algorithms:

Model	Type	Description
Logistic Regression	Linear	Baseline linear classification model
Random Forest	Ensemble	Ensemble of decision trees
Gradient Boosting	Ensemble	Sequential ensemble method
AdaBoost	Ensemble	Adaptive boosting algorithm
Decision Tree	Tree-based	Single decision tree classifier
SVM	Kernel-based	Support Vector Machine
KNN	Instance-based	K-Nearest Neighbors
Naive Bayes	Probabilistic	Gaussian Naive Bayes
XGBoost	Gradient Boosting	Optimized gradient boosting (optional)
LightGBM	Gradient Boosting	Microsoft's gradient boosting (optional)

5. Model Evaluation

• Metrics Calculated:
  • Accuracy
  • Precision
  • Recall
  • F1-Score
  • ROC-AUC Score
• Cross-Validation: 5-fold cross-validation for robust evaluation
• Visualizations:
  • Performance comparison charts
  • ROC curves
  • Confusion matrices
  • Performance heatmaps
  • Accuracy difference analysis

📈 Results

The notebook provides comprehensive results including:

• Model Performance Comparison: Side-by-side comparison of all models
• Accuracy Differences: Detailed analysis of accuracy differences from the best model
• Best Model Identification: Automatic identification of top-performing model
• Cross-Validation Scores: Mean and standard deviation of CV scores
• Overall Ranking: Weighted scoring system considering all metrics

Key Outputs:

• Performance metrics table
• Accuracy comparison visualization
• ROC curves for all models
• Confusion matrices
• Cross-validation results
• Final model ranking

🛠️ Technologies Used

• Python - Programming language
• Pandas - Data manipulation and analysis
• NumPy - Numerical computing
• Scikit-learn - Machine learning library
• Matplotlib - Data visualization
• Seaborn - Statistical data visualization
• XGBoost - Gradient boosting framework (optional)
• LightGBM - Gradient boosting framework (optional)
• Jupyter Notebook - Interactive development environment

📦 Dependencies

All dependencies are listed in requirements.txt:

pandas>=1.3.0
numpy>=1.21.0
scikit-learn>=1.0.0
matplotlib>=3.4.0
seaborn>=0.11.0
jupyter>=1.0.0
ipykernel>=6.0.0
xgboost>=1.5.0
lightgbm>=3.3.0

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Omar Elemary

🙏 Acknowledgments

• Dataset source: Credit Risk Dataset
• Thanks to the open-source community for excellent ML libraries

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