Bank Note Authentication — Classification Project

Project Overview

This repository contains a Ml classification project to detect forged banknotes using the provided BankNote_Authentication.csv dataset. The analysis and model training are implemented in code.ipynb (Jupyter Notebook).

Files

• BankNote_Authentication.csv — Dataset (features extracted from images of banknotes).
• code.ipynb — Notebook with data exploration, preprocessing, model training, evaluation, and example visualizations.
• README.md — This file.

Dataset

The dataset contains features computed from images of genuine and forged banknotes. Typical features include variance, skewness, curtosis, and entropy. The target column indicates whether a banknote is authentic (0) or forged (1).

Dependencies

The notebook requires Python 3.8+ and the following packages:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• jupyter

Install dependencies with:

pip install pandas numpy scikit-learn matplotlib seaborn jupyter

Quick Start

1. Open the project directory in your terminal or IDE.
2. Start Jupyter Notebook or Jupyter Lab:

jupyter notebook

3. Open code.ipynb and run the cells sequentially. The notebook includes sections for:
   • Data loading and inspection
   • Exploratory data analysis (visualizations)
   • Preprocessing and feature scaling
   • Model training (examples include Logistic Regression, SVM, Random Forest)
   • Evaluation (accuracy, confusion matrix, classification report)

Reproducing Results

• Ensure BankNote_Authentication.csv is in the same folder as code.ipynb.
• Run all cells in the notebook. Models are trained using scikit-learn and results (metrics and plots) are generated inline.

Author

Project created for a Bank Note Authentication classification task.