GitHub Projects
Desktop-Reminders
Description
This is a super simple script that allows you to get reminders on your desktop.
How to Use
- Clone the repository:
git clone https://github.com/joseph-c-mcguire/desktop-reminders.git cd desktop-reminders - Run the script:
Or just run with the default config found in the repo:python3 main.py -c <PATH TO CONFIG FILE>python3 main.py
Configuration
To modify the config, edit the message in the following format:
<MESSAGE ID>
wait: <TIME TO WAIT BETWEEN NOTIFICATIONS>
message: <THE BODY OF THE NOTIFICATION>
title: <THE TITLE OF THE NOTIFICATION>
max-number: <THE MAXIMUM NUMBER OF TIMES THE NOTIFICATION WILL BE POSTED>Additionally, supports extra parameters as supported by notify-py.
This Website
Description
The source code for my personal website, built using Jekyll and hosted on GitHub Pages.
Features
- Responsive design
- Blog posts
- Project showcase
- Contact form
How to Use
- Clone the repository:
git clone https://github.com/joseph-c-mcguire/joseph-c-mcguire.github.io.git cd joseph-c-mcguire.github.io - Install dependencies:
bundle install - Run the site locally:
bundle exec jekyll serve
Murphy's Machine Learning Notes
Description
A repository for my notebooks as I work through Murphy's Machine Learning: A Probabilistic Perspective.
How to Use
- Clone the repository:
git clone https://github.com/joseph-c-mcguire/Machine-Learning-Notes.git cd Machine-Learning-Notes - Open the notebooks using Jupyter Notebook or JupyterLab:
jupyter notebook
Math of Data Science Course Material
Description
Course materials and notes for the Math of Data Science course.
How to Use
- Clone the repository:
git clone https://github.com/joseph-c-mcguire/Math-of-data-science.git cd Math-of-data-science - Open the materials using Jupyter Notebook or JupyterLab:
jupyter notebook
Predictive Maintenance System
Description
The Predictive Maintenance System is a web application designed to perform Exploratory Data Analysis (EDA) and model diagnostics on a predictive maintenance dataset. The frontend is built using React and communicates with a backend server to fetch and display data. The application allows users to interact with the model, query it with new inputs, and view performance metrics.
How to Use
- Visit the deployed application: Predictive Maintenance System
- Clone the repository:
git clone https://github.com/joseph-c-mcguire/webapp-example-frontend.git cd webapp-example-frontend - Install dependencies:
npm install - Run the application locally:
npm start
Predictive Maintenance System Backend
Description
This project is a backend system for a predictive maintenance application. The system is designed for training, serving, and producing diagnostics for a set of models trained on a UCI dataset. This was a learning exercise to build a full-stack web application. The frontend repo can be found here.
The dataset is a predictive maintenance dataset hosted by the University of California, Irvine, and can also be found on Kaggle.com. Three sample models (Decision Tree, Gradient Boosting, and Random Forest) were trained to handle the multi-class classification problem. These models were selected for their consistent class structure in Scikit-Learn, making it easier to retrieve attributes like feature_importances_ and methods like predict_proba across different models.
How to Use
- Clone the repository:
git clone https://github.com/joseph-c-mcguire/webapp-example-backend.git cd webapp-example-backend - Install dependencies:
pip install -r requirements.txt - Run the server locally:
python run.py - For production, use:
python app.py
Feel free to explore the repositories and contribute if you find something interesting!