Data analysis and classification of counterfeit and genuine banknotes

This Python code utilizes the decision tree algorithm from the scikit-learn library to perform banknote authentication. The code aims to analyze the impact of different train-test split ratios and training set sizes on the accuracy and size of the learned decision tree.

Machine Learning / Data mining project in python. In this project, various classification algorithms such as Decision Tree, k-nearest neighbours, random forest and support vector machine have been implemented from scratch and have been applied on banknote authentication dataset. The goal of this project is to calculate and compare the accuracy o…

It is a web service that verifies the serial number of your Euro banknotes

This repository contains a Python implementation of a K-Nearest Neighbors (KNN) classifier from scratch. It's applied to the "BankNote_Authentication" dataset, which consists of four features (variance, skew, curtosis, and entropy) and a class attribute indicating whether a banknote is real or forged.

K-means clustering analysis of banknote dimensions for counterfeit detection. Using standardised length and diagonal measurements, the model successfully identifies three distinct clusters, with one clearly representing counterfeit bills based on their dimensional anomalies.

Trying different models of classification on Bank note authentication data-set from UCI repository, and comparing the performance of which one is better to predict the note is fake or not.

An End-to-End Data Science Project: Bank Note Authentication

Comparison of numerous supervised machine learning classifier models (Logistic Regression, K-Nearest Neighbors, Support Vector Machines and Decision Trees) predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes. (Python 3)

Our task is to predict whether a bank currency note is authentic or not based upon four attributes of the note i.e. skewness of the wavelet transformed image, the variance of the image, entropy of the image, and kurtosis of the image. This is a binary classification problem and we will use the Support Vector Machine algorithm to solve this probl…

UML dimensionality reduction and clustering models for predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes - practical exercise. (Python 3)

This repository demonstrates a complete machine learning workflow for banknote authentication using a hand coded Linear SVM model. It includes data preparation, training loop design, vectorized computation of gradients, model metrics, and a visualization module based on two selected features.