🏦 Banknote Authentication – Machine Learning Project

This project focuses on building a machine learning model to classify banknotes as genuine or fake using the Banknote Authentication Dataset from the UCI Machine Learning Repository. The dataset contains 1,372 rows and 5 columns representing various statistical features extracted from banknote images.

🔧 Project Overview

The goal of this project is to explore, preprocess, and model the dataset using multiple machine learning algorithms, and then evaluate their performance using key metrics such as accuracy score, cross-validation score, and confusion matrix.

📊 Steps Involved

1. Import Libraries:

   • Used PyForest for convenient import of commonly used libraries.

2. Load Dataset:

   • Loaded and inspected the first five rows to understand the structure of the data.

3. Exploratory Data Analysis (EDA):

   • Performed basic preprocessing and visualizations to understand feature distributions and relationships.

4. Model Building:

   • Implemented multiple machine learning models:

     • Logistic Regression
     • Support Vector Machine (SVM) with different hyperparameters
     • Random Forest Classifier
     • K-Nearest Neighbors (KNN)
     • Multi-Layer Perceptron (MLP)

5. Model Evaluation:

   • Compared models using:

     • Confusion Matrix
     • Accuracy Score
     • Cross-Validation Score

🎯 Objective

The objective is to detect counterfeit banknotes with high accuracy by evaluating and comparing different machine learning models.

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For More Details check the Banknote Authentication Code.pdf