Project Overview

This project focuses on detecting fraudulent transactions in credit card data using machine learning algorithms. By analyzing transactional data and identifying anomalous patterns, the goal is to build a robust model that can accurately flag fraudulent activities and minimize financial losses for credit card companies and users.

Technologies Used

• Python
• Pandas
• Scikit-Learn
• logistic regression
• Matplotlib

Key Features

• Data preprocessing to handle imbalanced datasets and normalize features.
• Application of machine learning models such as Logistic Regression, Random Forest, and Neural Networks.
• Evaluation using metrics like Precision, Recall, and F1-score to assess model performance.

Results

The developed model achieved high accuracy of 93% in detecting fraudulent transactions, demonstrating its effectiveness in safeguarding credit card transactions against potential fraud.