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.

DataSet

Kaggle: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud