This repository contains code and resources for detecting fraudulent transactions using machine learning techniques. The project aims to build a robust model that can identify potentially fraudulent transactions in financial data, helping to prevent financial losses and protect users.
The dataset used for this project consists of transactional data, which includes features such as transaction amount, timestamp, and user information. The dataset is split into training and testing sets to evaluate the model's performance.
To get started with this project, clone the repository and install the necessary dependencies:
git clone https://github.com/yourusername/fraudulent-transaction-detection.git
cd fraudulent-transaction-detectionThe model is trained using various machine learning algorithms, including logistic regression, decision trees, and ensemble methods like random forests and gradient boosting. Hyperparameter tuning is performed to optimize the model's performance.
The model is evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Cross-validation is used to ensure the model's robustness and generalizability.
The results of the model, including performance metrics and visualizations, are documented in the evaluation.ipynb notebook. The model achieves a high level of accuracy and effectively identifies fraudulent transactions.
Contributions to this project are welcome! If you have suggestions, bug reports, or improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.