Credit Card Fraud Detection

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. The system analyzes transaction data from the Credit Card Fraud Detection dataset to identify suspicious activities and distinguish fraudulent transactions from legitimate ones.

The project includes the complete machine learning workflow, starting from data preprocessing and exploratory data analysis to model training, evaluation, and performance comparison.

Several machine learning algorithms were implemented and tested, including:

• Logistic Regression

• K-Nearest Neighbors (KNN)

• Random Forest

Each model was trained and evaluated using appropriate metrics to measure performance in detecting fraudulent transactions. Among the tested models, Random Forest achieved the best performance due to its ability to capture complex nonlinear relationships within the dataset.

To better understand the model behavior and results, additional analysis was performed including:

• Feature importance analysis

• ROC curve evaluation

• Performance comparison between models

The results demonstrate the effectiveness of machine learning methods in detecting financial fraud and highlight the potential of AI systems in improving transaction security and preventing fraudulent activities.

Technologies Used

Python

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn

Machine Learning Algorithms