Credit Card Fraud Detection Data analysis using Python

Tools and libraries used:

NumPy for numerical data processing

Pandas for data cleaning and exploration

Matplotlib for visualizing transaction patterns and anomalies

The project involved exploratory data analysis (EDA) to identify unusual transaction behaviors that could indicate fraudulent activity. By analyzing the dataset, I was able to uncover insights into common fraud patterns and how they differ from legitimate transactions.

This kind of analysis helps lay the groundwork for future applications of machine learning models in fraud detection