Project-ML-1

Handwritten Digit Recognition

This project implements a handwritten digit recognition system using machine learning. The goal is to accurately classify handwritten digits (0-9) based on the famous MNIST dataset.

Project Overview

Handwritten digit recognition is a key application of image classification that has been widely studied and is useful in many real-world applications such as digitizing handwritten documents and creating automated systems for reading handwritten forms.

This project uses a deep learning approach with a Convolutional Neural Network (CNN) to achieve high accuracy in recognizing handwritten digits.

Features

• Data Preprocessing: Loads and normalizes the MNIST dataset.
• Model Architecture: A CNN built with layers optimized for image classification tasks.
• Training: Trains the model with training data and evaluates its performance on test data.
• Prediction: Classifies new, unseen images of handwritten digits.
• Visualization: Displays results, such as sample predictions and model accuracy graphs.

Tech Stack

• Programming Language: Python
• Libraries/Frameworks:
  • TensorFlow or PyTorch for building and training the neural network
  • NumPy for numerical operations
  • Matplotlib for visualizations
  • Scikit-learn for additional data processing and evaluation

Installation

1. Clone the Repository:

   
   git clone https://github.com/username/handwritten-digit-recognition.git
   cd handwritten-digit-recognition