Credit-Card-Fraud-Detection

Credit Card Fraud Detection 🚨💳

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. It uses a dataset containing credit card transactions, where each transaction is labeled as either ‘Fraud’ or ‘Not Fraud’. The goal is to train a model to predict fraud based on transaction features.

Table of Contents

• Overview
• Requirements
• Getting Started
• Installation
• Results
• Contributing
• License

Overview

In this project, we use a Random Forest Classifier to classify credit card transactions as fraudulent or not. The project includes steps like:

• Data Preprocessing
• Model Training
• Evaluation (Confusion Matrix, Classification Report, ROC-AUC)
• Feature Importance Analysis

Requirements 📦

The following libraries are required to run this project:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Getting Started 🚀

Prerequisites

• Python 3.8 or higher
• Required libraries installed (pip install -r requirements.txt)

Installation

1. Clone the repository: ```bash git clone https://github.com/QuantumCoderrr/CreditCardFraudDetection.git cd CreditCardFraudDetection
2. Install dependencies: ```bash pip install -r requirements.txt

Results 📊

Below are the visuals showing the Confusion Matrix and Feature Importance.

Confusion Matrix

The confusion matrix visualizes the performance of the classification model, showing the true positives, true negatives, false positives, and false negatives.

Feature Importance

Feature importance indicates the relative importance of each feature in the model’s decision-making process. Higher values indicate features that play a greater role in determining the prediction.

Dataset 📂

The dataset used for this project can be accessed via the following Google Drive link:
Credit Card Fraud Detection Dataset

Contributing 🤝

We welcome contributions! Please follow the contributing guidelines to submit changes.

License 📝

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

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Thanks for checking out the project! Let’s work together to make fraud detection more efficient! 🚀

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