Fraud Detection Model using Machine Learning

This repository contains a Python script that demonstrates building and evaluating fraud detection models using various machine learning algorithms. The code uses popular libraries like pandas, scikit-learn, and imbalanced-learn.

Getting Started

To use this code:

1. Clone the repository:

    git clone https://github.com/vishalshell/fraud-detection.git

2. Install the required Python packages:

   pip install pandas scikit-learn imbalanced-learn matplotlib

3. Place your dataset file named fraud_detection.csv in the same directory as the script.

4. Run the Python script:

   python fraud_detection_model.py

Features

• Loads and preprocesses fraud detection dataset.
• Implements Logistic Regression, Decision Tree, and Random Forest models.
• Uses SMOTE to handle class imbalance.
• Evaluates models' performance with Accuracy, Precision, Recall, and F1-score.
• Displays evaluation results and Accuracy graph.

Dependencies

• pandas
• scikit-learn
• imbalanced-learn
• matplotlib

datasource Link

Its from Kaggle and can be found at https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data

Contributing

Contributions are welcome! Feel free to open a pull request with your enhancements or bug fixes.

Acknowledgments

This code serves as a demonstration for building fraud detection models. Further enhancements and real-world data considerations are encouraged.