🏦 Bank Note Authentication using Machine Learning

📌 Overview

This project implements a binary classification system to identify whether a banknote is genuine or forged using statistical and machine learning techniques. The analysis is based on image-derived features obtained from wavelet-transformed banknote images.

The project focuses on:

• Exploratory data analysis
• Feature-based classification
• Training and evaluation of classical machine learning models
• Performance comparison using standard metrics

This repository is structured to be fully reproducible and GitHub-ready, with clean scripts and relative file paths.

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🎯 Objectives

• Understand feature separability between genuine and forged banknotes
• Train and compare multiple classification models
• Evaluate models using accuracy and confusion matrices
• Identify the best-performing classifier
• Present results in a clear and interpretable manner

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📂 Dataset Details

The dataset is sourced from the UCI Machine Learning Repository.

Variables

Feature	Description
variance	Variance of the wavelet-transformed image
skewness	Skewness of the wavelet-transformed image
curtosis	Curtosis of the wavelet-transformed image
entropy	Entropy of the wavelet-transformed image
class	Target variable (0 = genuine, 1 = forged)

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🛠 Tools & Technologies

• Language: R

• Libraries:

  • tidyverse
  • caret
  • ggplot2
  • e1071

• Methods:

  • Logistic Regression
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machine (SVM)
  • (Optional) Random Forest

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📁 Repository Structure

bank note authentication/
│
├── data/
│   └── banknote_auth.csv
│
├── notebooks/
│   └── banknote_authentication.ipynb
│
├── scripts/
│   └── html.py
│
├── results/
│   ├── banknote_authentication.html
│   └── banknote_authentication.pdf
│
├── LICENSE
└── README.md

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📊 Results Summary

• The dataset shows strong feature separability between genuine and forged banknotes.
• Support Vector Machine (RBF kernel) achieved the highest classification accuracy.
• Logistic Regression provides a strong and interpretable baseline model.
• Distance-based models such as k-Nearest Neighbors (k-NN) are sensitive to feature scaling.
• Detailed plots and evaluation outputs are saved in the results/ directory.

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🧠 Key Insights

• Classical machine learning models perform extremely well on this dataset.
• Non-linear decision boundaries significantly improve classification performance.
• Proper preprocessing and evaluation are crucial, even for relatively clean datasets.
• The project demonstrates a complete machine learning workflow without relying on black-box pipelines.

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📜 License

This project is licensed under the MIT License.
See the LICENSE file for details.

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📌 Notes

• This project is intended for educational and portfolio purposes.
• Code is written to prioritize clarity, reproducibility, and interpretability.
• Possible future extensions include:
  • Hyperparameter tuning
  • Cross-validation strategies

🧾 Author Mr Rup GitHub: https://github.com/Mr-Rup

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