💳 Credit Card Fraud Detection

📌 Overview

This project uses Machine Learning techniques to detect fraudulent credit card transactions. The dataset is preprocessed, analyzed, and used to train classification models to identify fraudulent activities accurately.

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🚀 Features

• Data preprocessing and cleaning
• Fraud vs. Normal transaction analysis
• Class distribution visualization
• Model training using:
  • Logistic Regression
  • Random Forest Classifier
• Confusion Matrix visualization
• Performance comparison of models

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

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📂 Project Structure

CreditCardFraudDetection/
│
├── fraud_detection.py
├── class_distribution.png
├── confusion_matrix.png
├── performance_comparison.png
├── .gitignore
└── README.md

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📊 Output

• Class Distribution Graph
• Confusion Matrix
• Performance Comparison Graph

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▶️ How to Run

1. Clone the repository

git clone https://github.com/neelinikhila/Credit_Card_Fraud_Detection.git

2. Install the required libraries

pip install pandas numpy matplotlib scikit-learn

3. Run the project

python fraud_detection.py

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📁 Dataset

The dataset is not included in this repository because it exceeds GitHub's file size limit.

You can download the Credit Card Fraud Detection dataset from Kaggle and place the creditcard.csv file inside a dataset folder.

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📈 Future Improvements

• Deep Learning-based fraud detection
• XGBoost implementation
• Flask web application
• Real-time fraud prediction
• Interactive dashboard

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👩‍💻 Author

Neeli Nikhila

B.Tech – Artificial Intelligence & Data Science

GitHub: https://github.com/neelinikhila