This project aims to develop an AI-based system for fraud detection. The system utilizes machine learning techniques to identify fraudulent activities in financial transactions.
The AI-Based Fraud Detection project leverages advanced machine learning algorithms to detect fraudulent transactions. By analyzing a given dataset, the system learns patterns and characteristics associated with fraudulent activities, allowing it to classify new transactions as either fraudulent or legitimate.
The project includes several key components:
- Data preprocessing: The dataset undergoes cleaning, normalization, and feature engineering to prepare it for model training.
- Model training: Machine learning and deep learning models, such as XGBOOST or ANN, are trained using the preprocessed data.
- Evaluation: The trained models are evaluated using various metrics, such as precision, recall, AUC, and F1 score.
- Fraud detection: The system applies the trained model to new, unseen data to identify potential fraudulent transactions.
To install and set up the AI-Based Fraud Detection project, follow these steps:
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Clone the repository:
git clone https://github.com/Tek-nr/AI-Based-Fraud-Detection.git
The datasets used within the project were researched from Kaggle, Google Dataset Search, UCI Machine Learning Repository, OpenML, DataHub, Papers with Code, EU Open Data Portal, Awesome Public Dataset resources and selected among the most preferred ones in the literature. Each dataset offers unique characteristics and can be utilized for different purposes. Below is a summary of the datasets:
| Dataset Name | Dataset Link | Number of Samples | Number of Features |
|---|---|---|---|
| Credit Card Fraud Detection | Link | 284,807 | 31 |
| Credit Card Transactions Fraud Detection | Link | 1,048,576 | 23 |
| Fraud E-commerce | Link | 150,000 | 11 |
| Synthetic data from a financial payment system | Link | 594,643 | 10 |
Within the scope of the project, the most used machine learning and deep learning algorithms in the literature for this project were examined comparatively.
| Machine Learning | Deep Learning |
|---|---|
| XGBClassifier | ANN |
| CatBoostClassifier | CNN |
| AdaBoostClassifier | RNN |
| GradientBoostingClassifier | LSTM |
| LGBMClassifier | Autoencoder |
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Clone the repository:
git clone https://github.com/YourUsername/YourRepository.git
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Navigate to the project directory:
cd YourRepository -
Access the datasets by referring to their respective links provided in the table above.
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Download the datasets and place them in the appropriate directory within your project.
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Use the datasets in your machine learning or data analysis tasks as needed.
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Make sure to properly attribute the datasets by including the relevant citations or credits to the dataset providers.
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If you make use of the datasets in your research or projects, consider providing a link or acknowledgment to the original dataset source in your work.
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If you perform any preprocessing or modifications to the datasets, clearly document the changes made in your project's documentation or README file.
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Ensure compliance with any licensing or usage restrictions associated with the datasets.
Contributions to this repository are welcome! If you have any additional datasets or improvements to the existing datasets, feel free to submit a pull request.
This repository and the datasets within it are licensed under the MIT License. So, feel free to modify this code to match your specific project and dataset details.