Mooncake: Improving predictors for fraudulent transactions

Bachelor's final project.

This project is optimised for python 3+

This Data Science-like project is aimed on analyzing a bunch of techniques for False Negatives (Frauds classified as Non-Frauds) in a extreme imbalanced dataset - Financial Fraud and improving the predictors performance.

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Dependencies

For developers, python requirements could be find in the project's root. For installing the requirements, in your venv or anaconda env, just run the following command:

pip install -r requirements.txt

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Project's Structure

.
└── mooncake
    ├── data
    │   ├── entropy
    │   │   ├── tree_entropy_2020-03-04.png
    │   │   └── tree_entropy_2020-03-05.png
    │   ├── datasource.csv
    │   ├── validation-2020-02-27.csv
    │   └── validation-2020-03-24.csv
    ├── docs
    │   ├── confusion-matrix.xlsx
    │   ├── Ficha.pdf
    │   ├── PGT 04.17.docx
    │   ├── PGT 05.11.docx
    │   ├── PGT 05.23.docx
    │   ├── PGT 05.26.docx
    │   ├── PGT 05.28.docx
    │   ├── PGT 05.29.docx
    │   └── PGT 05.29.pdf
    ├── mooncake
    │   ├── __init__.py
    │   ├── helpers.py
    │   ├── models.py
    │   ├── plotting.ipynb
    │   ├── ros.py
    │   ├── smote.ipynb
    │   └── smote.py
    ├── tests
    │   └── unittests
    │       ├── __init__.py
    │       ├── ...
    │       └── test_helpers.py
    ├── .gitignore
    ├── LICENSE
    ├── README.md
    ├── requirements.txt
    └── setup.py

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References

• Activation Functions: link;

• ANN in java: Sorting_Hat

• Benchmark's article: Expertise areas

• Old Project's name inspiration: Tree-Shrews;

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