Fraud Detection Project

This project aims to develop a fraud detection system for financial transactions using machine learning techniques. The model is designed to classify transactions as fraudulent or non-fraudulent based on various features.

Table of Contents

• Overview
• Dataset
• Installation
• Usage
  • Model Training
  • Prediction
• Results
• Contributing
• License

Overview

Credit card fraud is a significant issue worldwide, posing financial losses and security concerns for individuals and institutions. This project focuses on detecting fraudulent transactions in real-time by leveraging machine learning models trained on historical transaction data.

Dataset

The dataset used for this project is from Kaggle and contains the following columns:

• step: Time step of the transaction
• type: Type of transaction (e.g., PAYMENT, TRANSFER, CASH_OUT, etc.)
• amount: Transaction amount
• nameOrig: Customer who initiated the transaction
• oldbalanceOrg: Initial balance before the transaction
• newbalanceOrig: New balance after the transaction
• nameDest: Recipient of the transaction
• oldbalanceDest: Initial balance of the recipient before the transaction
• newbalanceDest: New balance of the recipient after the transaction
• isFraud: Whether the transaction is fraudulent (1) or not (0)
• isFlaggedFraud: Whether the transaction is flagged as fraudulent by the system (1) or not (0)

Installation

Clone the repository:

git clone https://github.com/OtaoDavis/Fraudify.git
cd fraud-detection

Install Requirements

pip install -r requirements.txt

Usage

Model Training

To train the model, run the following command:

jupyter notebook train.ipynb

Results

The model achieves high accuracy in detecting fraudulent transactions with a low false positive rate. Detailed results and performance metrics can be found in the results directory.