Credit Card Fraud Detection Project

Table of Contents

Project Overview
Dataset
Installation
Results
Dependencies
Scope for Improvement
License

This project focuses on detecting fraudulent credit card transactions using machine learning techniques.
By analyzing transaction data, the goal is to build a robust model that can accurately identify fraudulent activities while minimizing false positives.

Project Overview

Credit card fraud is a significant issue in the financial industry. This project aims to address this problem by building a machine learning model capable of identifying fraudulent transactions. The project emphasizes:

Handling severe class imbalance.
Feature selection and importance analysis.
Model evaluation and hyperparameter tuning.

Dataset

The dataset contains anonymized credit card transaction data, including:

Features: 30 numerical features (V1-V28, Time, Amount).
Target: Class (0 = Normal, 1 = Fraud).

Getting Started

Prerequisites

Python 3.9 or higher

Installation

Clone the repository:

git clone https://github.com/kavin/cc-fraud-detection.git
cd cc-fraud-detection

Install dependencies:
```
pip install -r requirements.txt
```

Run the Jupyter Notebook:

jupyter notebook cc_fraud_detection.ipynb

Results

Balanced Dataset: Achieved a 1:1 ratio using SMOTE.
Model Performance:
- High precision and recall for fraud detection.
- Improved accuracy after hyperparameter tuning.
Feature Importance: Identified key features contributing to fraud detection.

Dependencies

Category	Dependency
Analysis	`numpy`, `pandas`
Visualization	`matplotlib`, `seaborn`
Preprocessing	`imblearn` (SMOTE)
Model Inference	`xgboost`
Model Selection	`scikit-learn` (GridSearchCV)
Model Evaluation	`scikit-learn` (accuracy_score, classification_report)

Scope for Improvement

Explore additional models like Random Forest and Neural Networks.
Implement real-time fraud detection pipelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

kavin81/cc-fraud-detection