This project implements a machine learning model to identify and flag potential fraudulent operations.
It leverages historical transaction data to learn patterns associated with fraudulent activity.
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Table of Contents
First of all, you need to download python, jupter And Any code editorSo that you can run the code.
Using the terminal run the following command To download all libraries at once
- pip
pip install numpy pandas matplotlib seaborn torch torchvision xgboost scikit-learn joblib
We searched for a dataset to support our idea and found an excellent dataset on the Kaggle website
Check it out from here Kiggle
XGBoost: This gradient boosting model excels at handling large datasets and complex fraud patterns. It's highly accurate but can be a "black box" and requires tuning.
Decision Trees: Easy to understand and interpret, decision trees are relatively fast to train. They work well with various data types but might struggle with complex relationships and overfitting.
Feedforward Neural Networks: These deep learning models can capture intricate patterns in data, potentially leading to superior accuracy. However, they're computationally expensive, challenging to interpret, and prone to overfitting if not carefully regularized.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
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