banking_notes_svm

Using Machine Learning (Scikit Learn) to building data analysis, training and evaluating a model Bank Note Authentication.

Getting Started

Dataset /src/banking_notes.csv

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.

UCI - Banknote Authentication

• Owner of database: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe, volker.lohweg '@' hs-owl.de)
• Donor of database: Helene Dörksen (University of Applied Sciences, Ostwestfalen-Lippe, helene.doerksen '@' hs-owl.de)

Date received: August, 2012

Attribute Information

1. Variance of Wavelet Transformed image (continuous)
2. Skewness of Wavelet Transformed image (continuous)
3. Curtosis of Wavelet Transformed image (continuous)
4. Entropy of image (continuous)
5. Class (integer)

Dependencies

• Docker
• Python3
• Pip
• Flask
• Flask_Cors
• Joblib
• Pandas
• Scikit Learn
• Matplotlib
• Seaborn

Clone this repository

git clone https://github.com/sunr00t/banking_notes_svm

Running

1. docker build -t banking_notes .
2. docker run -p 5000:5000 banking_notes
3. Access webservice on http://localhost:5000

Routes

1. / http://localhost:5000/
2. /validate http://localhost:5000/validade

Endpoint explain:

http://localhost:5000 [GET]

2023-06-16 00:43:14.414213

http://localhost:5000/validade [POST]

{
 "variance": -2.6864,
 "curtosis": -0.097265,
 "skewness": 0.61663,
 "entropy": 0.061192
}

RESPONSE

{
 "classification": "1"
}