{"id":29285798,"url":"https://github.com/quantumcoderrr/credit-card-fraud-detection","last_synced_at":"2025-08-30T10:36:10.402Z","repository":{"id":268139398,"uuid":"903438684","full_name":"QuantumCoderrr/Credit-Card-Fraud-Detection","owner":"QuantumCoderrr","description":"💴 A machine learning project that detects fraudulent credit card transactions using classification algorithms. Includes data preprocessing, EDA, model training \u0026 evaluation with techniques like Random Forest, Logistic Regression, and SMOTE for class imbalance. 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It uses a dataset containing credit card transactions, where each transaction is labeled as either 'Fraud' or 'Not Fraud'. The goal is to train a model to predict fraud based on transaction features.\n\n## Table of Contents\n- [Overview](#overview)\n- [Requirements](#requirements)\n- [Getting Started](#getting-started)\n- [Installation](#installation)\n- [Results](#results)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Overview\nIn this project, we use a **Random Forest Classifier** to classify credit card transactions as fraudulent or not. The project includes steps like:\n- Data Preprocessing\n- Model Training\n- Evaluation (Confusion Matrix, Classification Report, ROC-AUC)\n- Feature Importance Analysis\n\n## Requirements 📦\nThe following libraries are required to run this project:\n- pandas\n- numpy\n- matplotlib\n- seaborn\n- scikit-learn\n\n## Getting Started 🚀\n\n### Prerequisites\n- Python 3.8 or higher\n- Required libraries installed (`pip install -r requirements.txt`)\n\n### Installation\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/QuantumCoderrr/CreditCardFraudDetection.git\n   cd CreditCardFraudDetection\n2. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   \n## Results 📊\nBelow are the visuals showing the **Confusion Matrix** and **Feature Importance**.\n\n### Confusion Matrix\nThe confusion matrix visualizes the performance of the classification model, showing the true positives, true negatives, false positives, and false negatives.\n\n![Confusion Matrix](images/confusion_matrix.png)\n\n### Feature Importance\nFeature importance indicates the relative importance of each feature in the model's decision-making process. Higher values indicate features that play a greater role in determining the prediction.\n\n![Feature Importance](images/feature_importance.png)\n\n## Dataset 📂\nThe dataset used for this project can be accessed via the following Google Drive link:  \n[Credit Card Fraud Detection Dataset](https://drive.google.com/file/d/15ky1Zn1BTtSR2GCHJCPSVEMtmIO4wXPl/view?usp=drive_link)\n\n## Contributing 🤝\nWe welcome contributions! Please follow the [contributing guidelines](CONTRIBUTING.md) to submit changes.\n\n## License 📝\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\nThanks for checking out the project! Let's work together to make fraud detection more efficient! 🚀\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantumcoderrr%2Fcredit-card-fraud-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantumcoderrr%2Fcredit-card-fraud-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantumcoderrr%2Fcredit-card-fraud-detection/lists"}