Credit Card Fraud Detection Model with SMOTE and MLPC

A machine learning project focused on detecting fraudulent credit card transactions using advanced neural network techniques and data balancing methods.

Project Overview

This project was inspired by a challenge posted on the Kaggle platform, from which the dataset used for training and testing the model was also obtained.

Kaggle Challenge Link: https://www.kaggle.com/mlg-ulb/creditcardfraud

Model Architecture

The target model chosen was a unidirectional neural network based on Multi-Layer Perceptrons (MLP). The implementation was carried out in Python, utilizing several key libraries for data processing, visualization, and machine learning.

Technologies Used

• pandas - Data manipulation and analysis
• numpy - Numerical computing and array operations
• seaborn - Statistical data visualization
• matplotlib - Plotting and data visualization
• sklearn - Machine learning algorithms and tools
• joblib - Model serialization and persistence
• imblearn - Imbalanced dataset handling with SMOTE

Key Features

SMOTE Integration

• Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance
• Generates synthetic examples of minority class (fraudulent transactions)
• Improves model performance on underrepresented fraud cases

Multi-Layer Perceptron Classifier (MLPC)

• Neural network architecture optimized for binary classification
• Multiple hidden layers for complex pattern recognition
• Backpropagation training with optimized hyperparameters

Data Processing Pipeline

• Feature scaling and normalization for optimal neural network performance
• Train-test split with stratified sampling to maintain class distribution
• Cross-validation for robust model evaluation

Model Performance

The fraud detection system demonstrates:

• High accuracy in identifying fraudulent transactions
• Balanced precision and recall through SMOTE balancing
• Real-time prediction capability for transaction monitoring
• Robust performance on imbalanced financial datasets

Applications

This fraud detection model can be applied to:

• Credit card transaction monitoring
• Real-time fraud alert systems
• Financial risk assessment
• Banking security infrastructure
• E-commerce payment protection

The project showcases practical application of machine learning techniques to solve real-world financial security challenges, demonstrating expertise in handling imbalanced datasets and implementing neural network solutions for fraud detection.