https://github.com/navp7/credit_card_defaulters

A machine learning application helps to detect potential credit defaulters based on their past credit card transactions.
https://github.com/navp7/credit_card_defaulters

Last synced: 9 days ago
JSON representation

A machine learning application helps to detect potential credit defaulters based on their past credit card transactions.

• Host: GitHub
• URL: https://github.com/navp7/credit_card_defaulters
• Owner: navp7
• Created: 2024-01-31T13:55:59.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-01-31T15:20:59.000Z (10 months ago)
• Last Synced: 2024-02-01T14:58:10.502Z (10 months ago)
• Language: Jupyter Notebook
• Size: 3.07 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# 🌐 Project: Credit Card Default Detection using Machine Learning

## 🚀 Problem

Credit card fraud and defaults pose significant financial challenges for both issuers and cardholders. Early detection of potential defaults can mitigate losses and improve overall financial stability.

## 💡 Solution

This project aims to develop a machine learning application for predicting credit card defaults based on historical transaction data. By analyzing spending patterns, account characteristics, and other relevant features, the model identifies customers at high risk of defaulting on their payments.

## 🛠️ Technology Stack

- **Programming Language:** Python

- **Machine Learning Libraries:** Scikit-learn

- **Data Processing Libraries:** Pandas, NumPy

- **Web Development Framework:** Flask 

## 📊 Methodology

1. **Data Acquisition and Preprocessing:** Collect and clean historical credit card transaction data, addressing missing values, outliers, and categorical variables.

2. **Feature Engineering:** Extract relevant features from the data that could potentially predict defaults, such as spending patterns, credit utilization, and delinquency history.

3. **Model Selection and Training:** Train various machine learning models (e.g., Logistic Regression, Random Forest, Gradient Boosting, AdaBoost Classifier, SVC, XGBoost Classifier, DecisionTree Classifier, K-Nearest Neighbors Classifier) on the prepared data and select the model with the best performance based on metrics like accuracy, recall, AUC-ROC, and PR-AUC score. 

4. **Hyperparameter Tuning:** Evaluate all model's performance on unseen data and refine it through hyperparameter tuning to improve accuracy and generalizability.

5. **Evaluation of Best Model:** To choose the best model, consider metrics beyond accuracy. As the dataset is slightly imbalanced, prioritize a high recall to ensure catching as many fraudulent transactions as possible.

6. **Deployment and Usage:** Integrate the model into a production environment (through Flask app) for real-time prediction on new transactions. This involves a user interface for interactive analysis.

## 🌟 Benefits

- **Early Detection:** Identify potential defaulters at an early stage, allowing for preventative measures like payment plans or credit line adjustments.

- **Reduced Risk:** Mitigate financial losses from defaults and improve overall portfolio risk management.

- **Informed Decisions:** Enhance credit-granting decisions by using objective data-driven insights alongside conventional credit scoring methods.

- **Personalized Customer Service:** Tailor communication and support for customers at high risk of default, potentially preventing financial hardship.

## 🔍 Future Work

- Explore deep learning architectures for potentially improved accuracy and feature representation.

- Incorporate real-time transaction data for more dynamic and immediate predictions.

- Develop explainable AI techniques to understand the model's decision-making process and build trust with stakeholders.

- Integrate the model with existing fraud detection systems for a comprehensive risk management approach.

## 🏁 Conclusion

This project demonstrates the potential of machine learning to address the challenge of credit card defaults. By leveraging data and innovative algorithms, we can create solutions that benefit both financial institutions and cardholders.