Fraud Detection System

A machine learning-based fraud detection system designed to identify potentially fraudulent transactions.

Overview

This project implements a fraud detection model that analyzes transaction data to flag suspicious activities. The system uses machine learning algorithms to classify transactions as legitimate or potentially fraudulent.

Project Structure

• fraud_detection.py - Main Python script containing the fraud detection model
• fraud_detection.ipynb - Jupyter notebook for exploratory data analysis and model development
• dataset.csv - Training/testing dataset with transaction records
• README.md - Project documentation

Features

• Transaction data preprocessing and feature engineering
• Machine learning model for fraud classification
• Performance metrics and evaluation
• Data visualization and analysis

Installation

# Clone the repository
git clone https://github.com/Arjun-Regmi-Chhetri/fraud-detection-using-machine-learning.git
cd fraud-detection-using-machine-learning

# Create virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install required dependencies
pip install -r requirements.txt

Usage

Running the Python script:

python fraud_detection.py

Using the Jupyter notebook:

jupyter notebook fraud_detection.ipynb

Current Limitations

⚠️ Important Notice: This model is not 100% effective in its current state. The detection accuracy has room for improvement, and there may be:

• False positives (legitimate transactions flagged as fraud)
• False negatives (fraudulent transactions not detected)
• Performance variations across different transaction types

Future improvements planned:

• Enhanced feature engineering
• Hyperparameter optimization
• Ensemble methods and advanced algorithms
• Improved data preprocessing techniques
• Increased training dataset size and quality

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.

Note: This is an ongoing project under active development. The model will be continuously improved to enhance detection accuracy and reduce errors.