This repository contains code for a project on fraud detection in financial transactions using machine learning techniques. The project aims to develop a model that can effectively identify fraudulent transactions in a dataset containing various types of financial transactions.
Fraud detection in financial transactions is a critical task for banks and financial institutions to prevent financial losses and maintain trust with their customers. Traditional rule-based systems for fraud detection often struggle to keep pace with evolving fraudulent schemes. Machine learning algorithms offer a more flexible and scalable approach to detect fraudulent activities by learning patterns from historical transaction data.
- Exploratory Data Analysis (EDA): Understand the characteristics and distributions of the dataset.
- Machine Learning Models: Apply supervised learning algorithms such as Logistic Regression, Random Forest, etc., to classify transactions as fraudulent or legitimate.
- Evaluation Metrics: Assess the performance of the models using accuracy, precision, recall, F1-score, etc.
- Synthetic Data Generation: Generate synthetic datasets for testing and experimentation purposes.
- Clone the repository:
git clone https://github.com/yourusername/fraud-detection.git - Install the required dependencies:
pip install -r requirements.txt - Run the Jupyter notebooks to explore the code and datasets.
- Experiment with different machine learning algorithms and parameters to improve the model's performance.
The dataset used in this project contains synthetic financial transaction data with features relevant to fraud detection, including transaction type, amount, balance information, and whether the transaction is fraudulent or not.
Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or create a pull request.