BNOTE

Hidden

Authenticate Bank Notes

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3
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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

🕵️ Introduction

We love it when our challenges bring out your hidden sides. This time we want to see that hidden detective in you. So come along Sherlock! Let's take a ride down the counterfeit lane!

We give you information describing `images` of `bank notes`, predict if they are `forged or not`!

Understand with code! Here is getting started code for you.😄

💾 Dataset

The data given to you is extracted from images that were taken for the evaluation of an authentication procedure for banknotes. Data was extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera which is popular for print inspection was used. The final images are `400 by 400` in dimensions. Wavelet Transformation tools were used to extract features from images. The attributes included in the dataset are:

The text in square bracket describes about the value type of an attribute

• Variance of Wavelet Transformed image (continuous)

• Skewness of Wavelet Transformed image (continuous)

• Curtosis of Wavelet Transformed image (continuous )

• Average Information of image ( continuous )

• Class (`0` if note is forged and `1` if it is genuine)

📁 Files

• `train.csv` - (`1097` samples) This csv file contains the attributes describing an image of bank note along with the binary value denoting whether or not the note is forged.

• `test.csv` - (`276` samples) File that will be used for actual evaluation for the leaderboard score but does not have the binary value denoting whether or not the note is forged.

🚀 Submission

• Prepare a csv containing header as `label` and predicted value as digit `1` if bank notes are genuine and digit `0` for forged notes with name as `submission.csv`.
• Sample submission format available at `sample_submission.csv`.

Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

📚 Refrences

• Owner of database - Volker Lohweg, University of Applied Sciences, Ostwestfalen-Lippe
• Donor of database - Helene DÃ¶rksen, University of Applied Sciences, Ostwestfalen-Lippe
• Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository][http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.