MNIST Digit Recognition Project

Overview

This repository contains a complete MNIST digit recognition project that includes a Streamlit dashboard, a neural network model, and a Jupyter notebook. The project demonstrates the end-to-end process of training a neural network on the MNIST dataset and deploying it through a user-friendly interface.

Project Structure

app.py: Streamlit application for interactive digit recognition.
mnist_model.h5: Trained neural network model saved in HDF5 format.
mnist_digit_recognition_notebook.ipynb: Jupyter notebook for data exploration, model training, and evaluation.
requirements.txt: List of Python packages required to run the project.
data/: Directory for storing any dataset files (if needed).
images/: Directory for storing images like profile pictures.

Model Information

The MNIST Digit Recognition model is a feedforward neural network trained on the MNIST dataset, which consists of handwritten digits from 0 to 9. Key model details:

Model Type: Feedforward Neural Network
Architecture: 2 Hidden Layers
Activation Functions: ReLU (Hidden Layers), Softmax (Output Layer)
Training Epochs: 15
Batch Size: 200

How to Run the Dashboard

Clone the Repository:

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

Navigate to the Project Directory:
```
cd mnist-digit-recognition-project
```
Install Dependencies: Create a requirements.txt file with the following content:
```
streamlit
tensorflow
pillow
numpy
matplotlib
jupyter
```
Install the dependencies using pip:
```
pip install -r requirements.txt
```
Run the Streamlit App:
```
streamlit run app.py
```

How to Use the Jupyter Notebook

Install Jupyter Notebook (if not installed):
```
pip install jupyter
```

Open the Notebook:

jupyter notebook mnist_digit_recognition_notebook.ipynb

Run the Cells: Follow the instructions in the notebook to explore the data, train the model, and evaluate performance.

About Me

Ahmad Ali Rafique
AI & Machine Learning Specialist

I am an AI and Machine Learning specialist dedicated to developing innovative solutions using advanced machine learning techniques. My expertise includes building and deploying models for various applications, with a focus on creating impactful and user-friendly solutions.

Contact Information

Feel free to connect with me or reach out if you have any questions or opportunities for collaboration!

Ahmad-Ali-Rafique/MNIST-Digit-Recognition-Project