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https://github.com/allanotieno254/mnist-digit-classification

building a machine learning model to classify handwritten digits using the MNIST dataset.
https://github.com/allanotieno254/mnist-digit-classification

computer-vision deep-learning imageclassification machine-learning minst-database neural-networks notebook-jupyter python pytorch tensorflow

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building a machine learning model to classify handwritten digits using the MNIST dataset.

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README 

        
# MNIST Digit Classification



## Description

This project focuses on building a machine learning model to classify handwritten digits using the MNIST dataset. The MNIST dataset is a well-known dataset in the field of image classification, containing 60,000 training images and 10,000 test images of handwritten digits from 0 to 9. The goal is to create a robust model capable of accurately predicting the digit in an unseen image.



![img1_output](https://github.com/user-attachments/assets/0472bec3-30fe-4adb-aa2f-1403cd729dc7)



![img2_output](https://github.com/user-attachments/assets/c3689d15-b90e-4b1a-b14d-0db965fc5009)



## Table of Contents

1. [Introduction](#introduction)

2. [Project Structure](#project-structure)

3. [Installation](#installation)

4. [Usage](#usage)

5. [Model Overview](#model-overview)

6. [Results](#results)

7. [Contributing](#contributing)

8. [License](#license)



## Introduction

The MNIST Digit Classification project is a deep learning-based approach to recognizing and classifying handwritten digits. Using various deep learning techniques, such as Convolutional Neural Networks (CNNs), the project demonstrates a step-by-step approach to achieving high accuracy in digit classification tasks.



## Project Structure

- **data/**: Contains the raw and processed dataset files.

- **notebooks/**: Jupyter Notebooks that document the exploratory data analysis, preprocessing steps, model training, and evaluation processes.

- **src/**: Python scripts for data processing, model building, training, and evaluation.

- **models/**: Directory where trained models are saved.

- **results/**: Plots and other outputs generated from the training and evaluation processes.

- **README.md**: Overview and instructions for the project.

- **requirements.txt**: Dependencies required to run the project.

- **setup.py**: Installation script (if applicable).

- **LICENSE**: License information for the project.



## Installation

To run this project, you'll need to have Python 3.x installed. Clone the repository and install the required dependencies:



```bash

git clone https://github.com/yourusername/mnist-digit-classification.git

cd mnist-digit-classification

pip install -r requirements.txt



## Usage

You can start by exploring the data and training the model using the Jupyter notebooks provided in the notebooks/ directory.



For instance, to run the data exploration notebook:



jupyter notebook notebooks/01_data_exploration.ipynb



To train the model, execute the following script:

python src/train.py



 #Model Overview

The model used in this project is a Convolutional Neural Network (CNN) with the following layers:



Input layer: Takes 28x28 pixel images as input.

Convolutional layers: Extract features from the input images.

Pooling layers: Downsample the feature maps.

Fully connected layers: Map the features to the output space.

Output layer: A softmax layer with 10 units corresponding to the 10 digit classes (0-9)



# Results

The model achieved an accuracy of X% on the test dataset. Below are some of the key metrics:



Accuracy: X%

Precision: X%

Recall: X%



# Contributing

Contributions are welcome! Please fork the repository and submit a pull request if you have any improvements or new features to add.



# License

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



### Explanation:

- **Sections**: The `README.md` file is structured with various sections that cover the essential details of your project.

- **Table of Contents**: It provides a quick navigation to different parts of the README.

- **Usage Instructions**: Step-by-step instructions for running your project.

- **Model Overview and Results**: These sections summarize the model architecture and key results from your project.



You can copy this text directly into your `README.md` file in your GitHub repository.



**a.** Would you like to generate the `requirements.txt` file based on typical libraries used for MNIST projects?  

**b.** Need help with setting up a specific part of the project (e.g., model architecture in `model.py`)?