Handwritten Digit Classification Using Artificial Neural Networks

A machine learning project that classifies handwritten digits (0-9) using Artificial Neural Networks (ANN). This project demonstrates the implementation of a feedforward neural network trained on the MNIST dataset with high accuracy.

📋 Table of Contents

• Overview
• Features
• Dataset
• Architecture
• Prerequisites
• Installation
• Usage
• Model Training
• Results
• Project Structure
• Dependencies
• Performance Metrics
• Contributing
• License

🎯 Overview

This project implements an Artificial Neural Network (ANN) to classify handwritten digits from the MNIST dataset. The model achieves high accuracy by learning patterns and features from 60,000 training images and is validated on 10,000 test images.

Key Objective: Build a neural network that can accurately recognize handwritten digits (0-9) with minimal error rate.

✨ Features

• ✅ Complete ANN Implementation - Feedforward neural network with backpropagation
• ✅ MNIST Dataset - 70,000 high-quality handwritten digit images
• ✅ Data Preprocessing - Normalization and data augmentation
• ✅ Model Evaluation - Accuracy, confusion matrix, and detailed metrics
• ✅ Visualization - Training curves, predictions visualization, and confusion matrix plots
• ✅ Easy to Use - Simple interface for training and prediction
• ✅ Scalable - Can be extended with different architectures
• ✅ Well Documented - Comprehensive comments and documentation

📊 Dataset

MNIST Dataset Information

• Total Images: 70,000
  • Training Set: 60,000 images
  • Test Set: 10,000 images
• Image Dimensions: 28×28 pixels (784 features)
• Classes: 10 (digits 0-9)
• Format: Grayscale images with pixel values 0-255
• Data Source: MNIST Database