This repository contains a machine learning model for online payment fraud detection. The model is built using the Random Forest algorithm, a powerful technique for classification tasks. The primary objective of this model is to identify and flag fraudulent online payment transactions while minimizing false positives.
The dataset used for training and evaluating the fraud detection model is not included in this repository. If you are interested in obtaining access to the dataset, please contact us via email at ashiksanyo@gmail.com
Before using this fraud detection model, make sure you have the following prerequisites in place:
Python: You should have Python installed on your system (Python 3.x recommended).
Required Libraries: Ensure you have the necessary Python libraries installed. You can install them using pip:
pip install pandas scikit-learn
##Usage
Clone the repository:
git clone https://github.com/yourusername/online-payment-fraud-detection.git
Navigate to the project directory:
cd online-payment-fraud-detection
Run the Jupyter notebook or Python script to use the model for fraud detection. Make sure to provide your dataset or modify the data loading code accordingly.
Model Performance The Random Forest model's exceptional performance in identifying fraudulent transactions makes it a reliable tool for enhancing the security of online payment systems. Please feel free to reach out for further details or assistance.
After rigorous testing and evaluation using K-fold cross-validation, the Random Forest model demonstrated exceptional performance. It achieved the highest score among all models, with an Area Under the Curve (AUC) of 0.999. This exceptional AUC score indicates that the model has a strong ability to distinguish between fraudulent and non-fraudulent payments, with a 99.9% accuracy rate.
Disclaimer: This model is provided for educational and demonstration purposes. It is essential to adapt and fine-tune the model according to your specific business requirements and data.