AI FRAUD DETECTION

🧠 AI Fraud Detection System

An intelligent system that leverages machine learning to detect fraudulent activities in transactional data. This project aims to reduce financial fraud by identifying anomalous patterns in real-time.

📌 Table of Contents

• About
• Tech Stack
• Features
• Dataset
• Model Architecture
• Installation
• Usage
• Results
• Demo
• Contributing
• License

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🧾 About

Fraud detection is a critical component in finance, banking, and e-commerce. This AI-powered solution uses supervised learning algorithms to identify fraudulent transactions by analyzing historical patterns. The system improves accuracy through data preprocessing, feature engineering, and optimized model selection.

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🛠 Tech Stack

• Programming Language: Python 🐍
• Libraries:
  • pandas, numpy – Data manipulation
  • scikit-learn – ML models
  • matplotlib, seaborn – Visualization
  • imbalanced-learn – Handling class imbalance
  • xgboost / lightgbm – Advanced ML models
• Deployment (Optional): Flask / FastAPI + Streamlit

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✨ Features

• Preprocessing and normalization of transaction data
• Handling imbalanced classes with SMOTE or undersampling
• Training and evaluation of multiple models
• Real-time fraud detection demo
• Model performance metrics: accuracy, precision, recall, F1-score, ROC-AUC
• Visualization of fraud vs. normal transactions

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📊 Dataset

• Name: Credit Card Fraud Detection Dataset
• Size: 284,807 transactions
• Fraudulent Transactions: 492 (Highly imbalanced)
• Features: Time, Amount, anonymized V1-V28, Class (0 = legit, 1 = fraud)

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🧠 Model Architecture

We explored and compared multiple models:

• Logistic Regression
• Random Forest
• XGBoost / LightGBM
• Isolation Forest (optional for unsupervised setting)
• Neural Networks (for deep learning variant)

Model tuning was done using GridSearchCV and Cross-validation.

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🚀 Installation

1. Clone the repo

git clone https://github.com/your-username/ai-fraud-detection.git
cd ai-fraud-detection