Banknotes Authentication

In this python project, I am trying to build a Classification Machine Learning model to predict banknotes are genuine or forged.

Purpose

In real world this could mainly be any of the followings.

• Fraud detection
• counterfeit detection
• quality control
• authentication of banknotes

Business Impact

There are several valuable Business Impacts and Pottential Benefits which we can define here.

1. Reducing finantial losses
2. Improve customer trust
3. Enhancing operational efficiency
4. or meeting any regulatory requirements

Stackholders

For this project stackholders possibly be

• any Banking system
• finantial institutions
• law enforcement agencies
• or any regulatory bodies

Data Sources

This project is based on Bank Notes Authentication UCI dataset dataset. I'm using the Kaggle's version of it.

Technology and Tools

I will be using

• Machine Learning Algorithms for classification banknotes.
• Various Python libraries to visualize different insights along the way

Analytic Techniques

Descriptive Statistics wil be used to derive valueable insights from the data.

Following Machine Learning algoritms will be evaluated and select the best performing model as the final model.

1. Logistic Regression
2. Random Forest
3. KNN Classifire
4. Support Vector Machine Classifire

Installation

I have used pyforest library bundle for this project.

  !pip install pyforest

🏆 Lessons Learned

1. Hyperparameter tuning using GridSearchCV()
2. Foundation Methodology for Machine Learning Project

Demo

Try it on my profile