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Banknote Authentication/README.md
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
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**Level:** Advanced <br/>
**Recommended Use:** Classification Models<br/>
**Domain:** Banking/Finance<br/>
## Bank Note Authentication Data Set
### Predict if a note is genuine
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![](983.jpg)
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This *advanced* level data set has 1372 rows and 5 columns.
Data were extracted from images that were taken for the evaluation of an authentication procedure for banknotes.
Data were extracted from images that 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 400x 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 tool were used to extract features from images.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **classification modelling techniques**.
It also allows you to practice with large number of features. Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
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### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |----------------------------------------- |----------------------------------------- |-------------- |----------------------------- |--------------- |
| 1 | Variance of Wavelet Transformed Image | Variance of Wavelet Transformed Image | Quantitative | 1.8993, 1.7939, 0.65497 | 0 |
| 2 | Skewness of Wavelet Transformed Image | Skewness of Wavelet Transformed Image | Quantitative | 7.6625, -1.1174, 5.1815 | 0 |
| 3 | Curtosis of Wavelet Transformed Image | Curtosis of Wavelet Transformed Image | Quantitative | 0.15394, 1.5454, 1.0673 | 0 |
| 4 | Entropy of Image | Entropy of Image | Quantitative | -3.1108, -0.26079, -0.42113 | 0 |
| 5 | Class | Class (1: genuine, 0: forged) | Quantitative | 1, 0 | 0 |
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### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Bank Note Authentication Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/banknote+authentication).
The UCI page mentions the following publication as the source of the data set:
*Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe, volker.lohweg '@' hs-owl.de)*
*Helene Dörksen (University of Applied Sciences, Ostwestfalen-Lippe, helene.doerksen '@' hs-owl.de)*
  • Stvine @stvinesmith96 commented

    I’m Danny Deniel, and I’m genuinely excited to dive into these fascinating conversations about software, AI, and the future of tech. Every discussion feels like unlocking a new perspective, and I can’t wait to share more of these insights with you.

  • Stvine @stvinesmith96 commented

    I’ve been noticing how fast AI editing tools are evolving. It’s crazy that just a few years ago face swap felt impossible without pro software, and now AI Photo tools make it happen in seconds. Do these platforms rely more on deep learning models or traditional editing algorithms?

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