Fraud Prediction

This repository focuses on predicting fraudulent transactions using machine learning techniques. The primary objective is to build and evaluate models that can distinguish between legitimate and fraudulent transactions, helping organizations reduce financial losses and improve security.

Table of Contents

• Project Overview
• Dataset
• Features
• Models Used
• Installation
• Usage
• Results
• Contributing
• License
• Contact

Project Overview

Fraud detection is a critical issue in the finance industry. This project leverages machine learning algorithms to identify potentially fraudulent transactions based on transaction data. The project covers data preprocessing, exploratory data analysis, model building, evaluation, and visualization of results.

Dataset

The dataset used in this project contains anonymized transaction data, including features that help distinguish between genuine and fraudulent activities. The dataset should be placed in the data/ folder.

Features

• Transaction amount
• Transaction time
• Customer and merchant IDs (anonymized)
• Location
• Transaction type
• Other derived features

Models Used

The following machine learning models are implemented and compared:

• Logistic Regression(I USE THIS ALOGRITHM)
• Decision Tree
• Random Forest
• XGBoost
• Support Vector Machine (SVM)
• Neural Networks (optional/advanced)

Installation

1. Clone this repository:

   git clone https://github.com/Vivek-ML001/Fraud_prediction.git
   cd Fraud_prediction

2. Install required packages:

   pip install -r requirements.txt

3. Place the dataset in the data/ folder.

Usage

1. Run the main notebook to preprocess data, train models, and evaluate results:

   jupyter notebook Fraud_Prediction.ipynb

2. Follow the steps in the notebook to visualize results and compare model performance.

Results

• Performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are reported for each model.
• Confusion matrices and ROC curves are plotted for visualization.
• The best-performing model is highlighted and can be deployed for real-world fraud detection.

Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements, bug fixes, or new features.

License

This project is licensed under the MIT License.

Contact

For any questions or suggestions, feel free to contact Vivek-ML001.