saidrmansour / eminst_digit_machinelearning

EMNIST Digit Classifier

This project is an exploration into machine learning with a focus on classifying handwritten digits from the Extended Modified National Institute of Standards and Technology (EMNIST) dataset. Utilizing both the RandomForestClassifier algorithm and a Neural Network model, this Python-based project demonstrates the process of loading and preparing data, selecting, training, and finally evaluating their performance.

Getting Started

These instructions will guide you through setting up and running the project on your local machine for development and testing purposes.

Prerequisites

Before you begin, ensure you have the following software installed on your system:

• Python 3.x
• Pip (Python package installer)

Installation

First, clone the repository to your local machine:

git clone https://github.com/<your_username>/<project_name>.git
cd <project_name>

Then, install the required Python packages:

pip install pandas numpy scikit-learn matplotlib tensorflow

Running the Project

To run the RandomForest model, execute the following command:

python RandomForest_Model.py

To run the Neural Network model, execute the following command:

python NeuralNetwork_Model.py

This script will:

• Load and prepare the EMNIST dataset.
• Train the specified model (RandomForestClassifier or Neural Network).
• Evaluate the model's accuracy on the test dataset.

Data Download

The datasets for this project can be downloaded from the following Google Drive links:

• Train set: Download Train Set
• Test set: Download Test Set

Please download the datasets and place them in the appropriate directory before running the project scripts.

Project Structure

• emnist-digits-train.csv: Training data file.
• emnist-digits-test.csv: Test data file.
• RandomForest_Model: Main Python script for training and evaluating the random forest model
• NeuralNetwork_Model: Main Python script for training and evaluating the neural network model

Built With

• Pandas - For data manipulation and analysis.
• NumPy - For handling large, multi-dimensional arrays and matrices.
• Scikit-learn - For machine learning.
• Matplotlib - For creating static, interactive, and animated visualizations in Python.
• TensorFlow and Keras - For building and training the neural network model.