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Fraud Transaction Detection

Overview

This project focuses on the development of a machine learning model aimed at detecting fraudulent transactions based on transaction details. The model is designed to analyze various features of a transaction and determine whether it is normal or fraudulent.

Features

  • Utilizes transaction details as inputs for fraud detection.
  • Performs meticulous data preparation on a dataset with 154 features.
  • Includes processes such as:
    • Correlation analysis
    • Incorporation of domain-specific data
    • Handling null values
    • Creation of new features through feature engineering

Methodology

  • Developed and fine-tuned a machine learning model to achieve nearly 94% accuracy.
  • Employed various machine learning algorithms and compared their performance to identify the most effective approach.

Repository Structure

  • Online_fraud_detection_practice.ipynb: Jupyter Notebook containing data preparation processes, training and evaluating machine learning models.
  • requirements.txt: List of dependencies required to run the project.
  • README.md: Overview of the project and instructions for usage.

Usage

  1. Clone the repository: 
    git clone https://github.com/revant-kumar/Fraud-Detection-Model.git
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Run the Jupyter Notebook (Online_fraud_detection_practice.ipynb) to execute the data preparation and model training processes.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests with suggestions, improvements, or bug fixes.