Online Payment Fraud Detection Classification Project

This project aims to detect online payment fraud using machine learning algorithms, specifically Logistic Regression, Decision Tree, and Random Forest. The project was developed using Jupyter Notebook as the primary software tool.

Table of Contents

• Introduction
• Steps
• Data
• Methods
• Results
• License

Introduction

Online payment fraud is a significant concern in today's digital world. This project aims to develop a fraud detection system using machine learning algorithms. Three primary classification algorithms have been used: Logistic Regression, Decision Tree, and Random Forest.

Steps

The following steps are involved in the project.

• Preprocess and explore the dataset.
• Train and evaluate the machine learning models.
• Visualize the results and model performance.

Data

The project uses a dataset for online payment fraud detection. Visit https://drive.google.com/file/d/1qrQrLu9F8mw8__bedSm946SuunYQx_K4/view?usp=drive_link for the dataset.

Methods

Three classification algorithms are used in this project:

1. Logistic Regression
2. Decision Tree
3. Random Forest

Each algorithm's implementation and performance evaluation are present in the notebook.

Results

The results of the project are available in the Jupyter Notebooks file. You can analyze each classification algorithm's model performance, accuracy, and other relevant met.

License

This project is licensed under the MIT License - see the LICENSE file for details.