unique-divine / banknote-fraud-detection

Fraudulent banknote detection with decision trees built from scratch.

Banknote Fraud Detection - Decision Trees from Scratch

Summary:

The goal of this project was to develop accurate predictive models to solve a binary classification problem: detecting fraudulent banknotes. I figured that learning to write decision tree algorithms from scratch could serve as an effective technical exercise. Unexpectedly, this from-scratch implementation ended up outperforming the default trees from Scikit-learn.

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Data Description:

The data was downloaded from this source.

Extracted from images were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400 x 400 pixels. Due to the object lens and distance to the investigated object, gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tools were used to extract features from images.

Dataset Attributes:

1. Variance of Wavelet Transformed image (continuous)
2. Skewn of Wavelet Transformed image (continuous)
3. Curtosis of Wavelet Transformed image (continuous)
4. Entropy of image (continuous)
5. Class (integer): Whether or not the banknote is real or fake.