{"id":15063936,"url":"https://github.com/mecha-aima/fake-bills-detection","last_synced_at":"2026-01-27T19:49:50.103Z","repository":{"id":255570991,"uuid":"852426212","full_name":"Mecha-Aima/Fake-bills-detection","owner":"Mecha-Aima","description":"This Python project implements a simple classification model comparison using scikit-learn to classify banknotes as either \"Authentic\" or \"Counterfeit\" based on four features","archived":false,"fork":false,"pushed_at":"2024-09-04T19:48:04.000Z","size":23,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-22T19:11:52.726Z","etag":null,"topics":["classification-model","machine-learning","model-selection","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mecha-Aima.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-04T19:31:49.000Z","updated_at":"2024-09-04T19:48:07.000Z","dependencies_parsed_at":"2024-09-06T04:35:40.579Z","dependency_job_id":null,"html_url":"https://github.com/Mecha-Aima/Fake-bills-detection","commit_stats":null,"previous_names":["mecha-aima/testing-ml-models"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mecha-Aima%2FFake-bills-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mecha-Aima%2FFake-bills-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mecha-Aima%2FFake-bills-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mecha-Aima%2FFake-bills-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mecha-Aima","download_url":"https://codeload.github.com/Mecha-Aima/Fake-bills-detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243835942,"owners_count":20355611,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification-model","machine-learning","model-selection","scikit-learn"],"created_at":"2024-09-25T00:08:57.615Z","updated_at":"2026-01-27T19:49:50.052Z","avatar_url":"https://github.com/Mecha-Aima.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Banknote Classification Model Comparison\n\n## Overview\nThis 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.\n\n## Data\nThe code expects a CSV file with the following structure:\n\n| Feature   | Description                         |\n|-----------|-------------------------------------|\n| variance  | Variance of the banknote image      |\n| skewness  | Skewness of the banknote image      |\n| curtosis  | Curtosis of the banknote image      |\n| entropy   | Entropy of the banknote image       |\n| class     | Class label (0 for Authentic, 1 for Counterfeit) |\n\n## Code\nThe code is written in Python and uses the scikit-learn library for machine learning tasks. It consists of the following steps:\n\n1. **Data loading**: The code loads the banknote data from a CSV file.\n2. **Data preprocessing**: The code splits the data into training and testing sets.\n3. **Model training**: The code trains four different classification models (Perceptron, SVM, Gaussian Naive Bayes, and K-Nearest Neighbors).\n4. **Model evaluation**: The code makes predictions on the test set using each trained model and calculates the accuracy of each model.\n5. **Results**: The code prints out the accuracy of each model as a percentage.\n\n## Usage\nTo 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.\n\n## Acknowledgments\nI 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmecha-aima%2Ffake-bills-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmecha-aima%2Ffake-bills-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmecha-aima%2Ffake-bills-detection/lists"}