https://github.com/facundomuruchi/credit-card-fraud-detection
https://github.com/facundomuruchi/credit-card-fraud-detection
classification credit-card-fraud jupyter-notebook machine-learning matplotlib numpy pandas python scikit-learn seaborn supervised-learning
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/facundomuruchi/credit-card-fraud-detection
- Owner: FacundoMuruchi
- Created: 2025-03-15T00:04:19.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-15T00:29:57.000Z (about 2 months ago)
- Last Synced: 2025-03-15T01:25:57.856Z (about 2 months ago)
- Topics: classification, credit-card-fraud, jupyter-notebook, machine-learning, matplotlib, numpy, pandas, python, scikit-learn, seaborn, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 127 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Credit Card Fraud Detection
This project uses **supervised learning** techniques to detect credit card fraud, utilizing a dataset of credit card transactions. The goal is to identify fraudulent transactions based on labeled data.
## Project Description
In this project, **supervised learning** is applied to detect fraud in credit card transactions. The dataset includes labeled transactions, with each transaction being classified as either fraudulent(1) or legitimate(0). Machine learning algorithms are used to train a model to predict this target.
### Objectives:
- Classify transactions as fraudulent or legitimate.
- Apply data preprocessing techniques.
- Implement supervised learning models for fraud detection, including algorithms like **Logistic Regression**, **Random Forest**, and **XGBoost**.
## Technologies Used
- **Python**: Main programming language.
- **Pandas**: For data manipulation and analysis.
- **Scikit-learn**: For implementing machine learning algorithms.
- **Matplotlib / Seaborn**: For data visualization.
- **Numpy**: For handling arrays and mathematical operations.
- **Jupyter Notebook**: For interactive code exploration.