Handwritten Digit Classification

Overview

This repository contains implementations for classifying handwritten digits using the MNIST dataset. The project explores various multiclass classification techniques, including Perceptron, Logistic Regression, and Support Vector Classification (SVC) with different strategies.

Key Analyses

Perceptron Model:

Multiclass classification using the Perceptron model with the one-vs-one strategy.

Logistic Regression Model:

Multiclass classification using Logistic Regression with the softmax function.

Support Vector Classification (SVC) Model:

Multiclass classification using the SVC model with the one-vs-one strategy.

Requirements

Python 3.x scikit-learn numpy pandas matplotlib

License

This project is licensed under the MIT License. See the LICENSE file for details.