Banknotes are one of the most important assets of a country. Some miscreants introduce fake notes which bear a resemblance to original note to create discrepancies of the money in the financial market. It is difficult for humans to tell true and fake banknotes apart especially because they have a lot of similar features.
source: leftover currency
Motivation
Despite a decrease in the use of currency due to the recent growth in the use of electronic transactions, cash transactions remain very important in the global market. Banknotes are used to carry out financial activities. To continue with smooth cash transactions, entry of forged banknotes in circulation should be preserved. There has been a drastic increase in the rate of fake notes in the market. Fake money is an imitation of the genuine notes and is created illegally for various motives. These fake notes are created in all denominations which brings the financial market of the country to a low level. The various advancements in the field of scanners and copy machines have led the miscreants to create copies of banknotes. It is difficult for human-eye to recognize a fake note because they are created with great accuracy to look alike a genuine note. Security aspects of banknotes have to be considered and security features are to be introduced to mitigate fake currency. Hence, there is a dire need in banks and ATM machines to implement a system that classifies a note as genuine or fake.
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Source of Information:
A research paper on Analysis of Banknote Authentication System using Machine Learning Techniques by Sumeet Shahani, Aisa Jagiasi and Priya RL at International Journal of Computer Applications (0975 – 8887) Volume 179 – No.20, February 2018]
Objective
Being a Data Science Enthusiast, you committed yourself to use the power of Data Science and come up with an efficient model that accurately predicts if a note is genuine or not.
Evaluation Criteria
Submissions are evaluated using
Accuracy Score
. How do we do it?
Once you generate and submit the target variable predictions on the test dataset, your submissions will be compared with the true values of the target variable.
The True or Actual values of the target variable are hidden on the DPhi platform so that we can evaluate your model's performance on unseen data. Finally, an accuracy score for your model will be generated and displayed.
Submission Deadline: 6th September 2020
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