{"id":23055141,"url":"https://github.com/sudarshanc00/mnist-digit-classification","last_synced_at":"2026-04-12T17:52:21.550Z","repository":{"id":263389265,"uuid":"819463222","full_name":"SudarshanC00/MNIST-Digit-Classification","owner":"SudarshanC00","description":"This project uses a machine learning model to classify handwritten digits (0-9) from the MNIST dataset, a popular collection of grayscale images for image classification benchmarking. 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The MNIST dataset is a collection of grayscale images of handwritten digits, widely used for benchmarking image classification models. This project provides a Jupyter Notebook that trains a neural network to recognize and classify digits with high accuracy.\n\n## Features\n- **Data Loading and Preprocessing**: Loads and preprocesses the MNIST dataset to prepare it for model training.\n- **Model Training**: Trains a neural network on the MNIST data for digit classification.\n- **Evaluation**: Evaluates the model’s performance and displays accuracy metrics.\n\n## File Structure\n- **MNIST.ipynb**: Jupyter Notebook containing code to load, preprocess, train, and evaluate a digit classification model using the MNIST dataset.\n\n## Installation\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/SudarshanC00/MNIST-Digit-Classification.git\n   ```\n2. Navigate to the project directory:\n   ```bash\n   cd MNIST-Digit-Classification\n   ```\n3. Install dependencies (e.g., TensorFlow, Keras, etc.):\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Usage\n1. Open the Jupyter Notebook:\n   ```bash\n   jupyter notebook MNIST.ipynb\n   ```\n2. Run the cells in the notebook to:\n   - Load and preprocess the MNIST data.\n   - Define and train the neural network model.\n   - Evaluate the model's performance and view accuracy metrics.\n\n## Model Details\n- **Dataset**: MNIST dataset containing 60,000 training images and 10,000 test images of handwritten digits.\n- **Model Architecture**: A neural network suitable for image classification tasks, optimized for the MNIST dataset.\n- **Evaluation Metrics**: Accuracy and loss metrics are used to evaluate model performance.\n\n## Dependencies\n- **Jupyter Notebook**: To run and explore the code interactively.\n- **TensorFlow/Keras**: For building and training the neural network model.\n- **NumPy**: For handling numerical data.\n- **Matplotlib**: For visualizing data and model performance.\n\n## License\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsudarshanc00%2Fmnist-digit-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsudarshanc00%2Fmnist-digit-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsudarshanc00%2Fmnist-digit-classification/lists"}