https://github.com/mecha-aima/fake-bills-detection

This Python project implements a simple classification model comparison using scikit-learn to classify banknotes as either "Authentic" or "Counterfeit" based on four features
https://github.com/mecha-aima/fake-bills-detection

classification-model machine-learning model-selection scikit-learn

Last synced: 26 days ago
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This Python project implements a simple classification model comparison using scikit-learn to classify banknotes as either "Authentic" or "Counterfeit" based on four features

• Host: GitHub
• URL: https://github.com/mecha-aima/fake-bills-detection
• Owner: Mecha-Aima
• Created: 2024-09-04T19:31:49.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-04T19:48:04.000Z (2 months ago)
• Last Synced: 2024-10-13T03:22:18.753Z (26 days ago)
• Topics: classification-model, machine-learning, model-selection, scikit-learn
• Language: Python
• Homepage:
• Size: 22.5 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Banknote Classification Model Comparison

## Overview

This project implements a simple classification model comparison using scikit-learn to classify banknotes as either "Authentic" or "Counterfeit" based on four features: variance, skewness, curtosis, and entropy. The code reads data from a CSV file, preprocesses the data, trains four different models (Perceptron, SVM, Gaussian Naive Bayes, and K-Nearest Neighbors), makes predictions on a test set, and prints out the accuracy of each model.

## Data

The code expects a CSV file with the following structure:

| Feature   | Description                         |

|-----------|-------------------------------------|

| variance  | Variance of the banknote image      |

| skewness  | Skewness of the banknote image      |

| curtosis  | Curtosis of the banknote image      |

| entropy   | Entropy of the banknote image       |

| class     | Class label (0 for Authentic, 1 for Counterfeit) |

## Code

The code is written in Python and uses the scikit-learn library for machine learning tasks. It consists of the following steps:

1. **Data loading**: The code loads the banknote data from a CSV file.

2. **Data preprocessing**: The code splits the data into training and testing sets.

3. **Model training**: The code trains four different classification models (Perceptron, SVM, Gaussian Naive Bayes, and K-Nearest Neighbors).

4. **Model evaluation**: The code makes predictions on the test set using each trained model and calculates the accuracy of each model.

5. **Results**: The code prints out the accuracy of each model as a percentage.

## Usage

To run the code, simply execute the Python script. The code will load the data, train the models, and print out the accuracy of each model.

## Acknowledgments

I completed this exercise as part of CS50's Intro to AI Course. The course provided a comprehensive introduction to artificial intelligence and machine learning, and this project was a great opportunity to apply the concepts learned in the course to a real-world problem.