Abstract
Red Discord Bot is an open-source AI & Machine Learning project. A multi-function Discord bot. Red is a fully modular bot – meaning all features and commands can be enabled/disabled to your liking, making it completely customizable. This is a self-hosted bot – meaning you will need to host and maintain your own instance. It is built using Python. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
Red is a fully modular bot – meaning all features and commands can be enabled/disabled to your liking, making it completely customizable. This is a self-hosted bot – meaning you will need to host and maintain your own instance. You can turn Red into an admin bot, music bot, trivia bot, new best friend or all of these together!
Red is built for Discord, a popular VOIP and instant messaging platform. It's best suited for use in guilds (also known as servers), where it utilizes Discord's well-documented API to communicate and deliver its many features. Discord offers its API to encourage developers to explore their creativity by building programs, tools, and services that enhance the Discord experience.
Music, Moderation, Trivia, Stream Alerts and Fully Modular.
2. Objective
A multi-function Discord bot
This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/Cog-Creators/Red-DiscordBot.git
cd Red-DiscordBot- Most major linux distributions
Full setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by Cog-Creators and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.
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