Credit Card Fraud Detection

Project Overview

This project focuses on identifying fraudulent credit card transactions using machine learning models. The objective is to detect fraudulent activities accurately and reduce financial losses.

Key Features

The project involves the following key steps:

• Data preprocessing and handling imbalanced data
• Feature selection and engineering
• Model training using algorithms like Logistic Regression and Random Forest
• Model evaluation using metrics like precision, recall, and F1-score
• Deployment of the model for real-time fraud detection

Technologies Used

The project utilizes the following technologies:

• Python
• Pandas and NumPy for data manipulation
• Scikit-learn for machine learning
• Matplotlib and Seaborn for data visualization

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