Authenticate Bank Notes


πŸ›  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.

πŸ•΅οΈ 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.πŸ˜„

πŸ’Ύ 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)

To know about Skewness and Curtosis click here.

πŸ“ 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.

πŸš€ 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 πŸš€ !!

πŸ–Š Evaluation Criteria

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

πŸ”— Links

πŸ“± Contact

πŸ“š Refrences

  • Owner of database - Volker Lohweg, University of Applied Sciences, Ostwestfalen-Lippe
  • Donor of database - Helene Dârksen, 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.

Getting Started

Latest Submissions

darthgera123 graded
ashivani graded