Credit Card Fraud Detection

Project Overview

In the Credit Card Fraud Detection Project, we focus on the vital task of identifying and preventing fraudulent credit card transactions. Using a rich dataset from Kaggle, the project employs various machine learning models to analyze transaction data and detect fraudulent activities. This project not only serves to enhance the security of digital financial transactions but also provides valuable insights into the behavior and patterns associated with credit card fraud.

Technology Used

• Python: The primary programming language for data analysis and model development.
• Pandas and NumPy: For data manipulation and numerical computations.
• Matplotlib and Seaborn: For data visualization and exploratory data analysis (EDA).
• Scikit-learn: For implementing Logistic Regression and various model evaluation metrics.
• XGBoost: An optimized distributed gradient boosting library used for building the XGBoost classifier.
• PyTorch: A deep learning framework used for designing and training the custom Multilayer Perceptron (MLP).
• Kaggle Dataset: The Credit Card Fraud Detection Dataset 2023 provided a comprehensive set of features for training and testing the models.

Objectives

The main objectives of the project are:

1. Conduct an initial exploratory data analysis (EDA) to understand the dataset's structure and inherent patterns.
2. Develop and evaluate three different models for fraud detection:
   • Custom MLP with PyTorch.
   • XGBoost Classifier.
   • Logistic Regression.
3. Compare the performance of these models based on accuracy, precision, recall, and F1 score to identify the most effective approach for detecting credit card fraud.

Learning Outcomes

From this project, several key learning outcomes are achieved:

1. Gained proficiency in handling and preprocessing large datasets for machine learning tasks.
2. Developed a deeper understanding of various machine learning algorithms and their applications in real-world scenarios.
3. Enhanced skills in Python programming, especially in the context of data science and machine learning.
4. Learned to implement and tune advanced models like XGBoost and custom deep learning models using PyTorch.
5. Acquired practical experience in comparing and evaluating different models based on standard metrics to determine their effectiveness in a specific application.
6. Understood the challenges and nuances associated with fraud detection, including dealing with imbalanced datasets and interpreting model predictions.

This project stands as a comprehensive endeavor to address the pressing issue of credit card fraud using cutting-edge machine learning techniques, offering valuable insights and tools for enhancing financial security.