SHROOMLEARNING

Your guide to understanding mushrooms through machine learning

Developed with the software and tools below.

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Collaborative work from Costes Sylvain & Amiar Abd Rezak

Main Interface

Description: This screenshot shows the main interface of the web application, where users can enter characteristics of a mushroom to predict its category.

Results Page

Quick Links

• Overview
• Features
• Repository Structure
• Getting Started
• Project Roadmap
• Contributing
• License
• Acknowledgments

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Overview

ShroomLearning is a machine learning project designed to classify mushrooms as edible, poisonous, or inedible based on their physical characteristics like color, shape, and surface texture. This project utilizes data from Ultimate Mushroom and aims to educate users on mushroom types. It's important to note that this tool is not a substitute for professional advice from a mycologist or pharmacist.

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Features

• Data Collection: Utilizes web scraping to gather data on mushrooms.
• Machine Learning Models: Includes models like SVM and Decision Trees to predict mushroom categories.
• Interactive Web Interface: Features a web interface where users can input mushroom characteristics and receive predictions.
• Educational Tool: Provides insights into data science and machine learning through practical application.

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Repository Structure

└── ShroomLearning/
    ├── 2ejalon.ipynb
    ├── ShroomLearning.py
    ├── arbre
    │   └── arbre_decision.dot
    ├── data
    │   ├── champignons.csv
    │   └── champignons_processed.csv
    ├── model
    │   ├── modele_svm.joblib
    │   ├── modele_svm_scaled.joblib
    │   ├── modele_tree.joblib
    │   └── modele_tree_scaled.joblib
    ├── projetExpress
    │   ├── formulaire.html
    │   ├── index.js
    │   ├── package.json
    │   └── public
    │       ├── Edible.png
    │       ├── Inedible.png
    │       ├── Poisonous.png
    │       ├── favicon.ico
    │       ├── jdza.png
    │       ├── js
    │       │   └── css.js
    │       ├── mushroom.jpg
    │       ├── sh.png
    │       └── style.css
    └── script
        └── predict.py

Getting Started

To get started with ShroomLearning, you will need to have Python installed along with the libraries listed in requirements.txt.

Installation

git clone https://github.com/LZ1337-1/ShroomLearning
cd ShroomLearning
pip install -r requirements.txt

cd projetExpress
node index.js

Project Roadmap

• Initial web scraping implementation.
• Basic machine learning model integration.
• Enhance data preprocessing and model accuracy.
• Expand dataset to include more mushroom types.
• Implement additional features based on user feedback.

Contributing

Contributions are welcome! Here’s how you can help:

• Submit Pull Requests: Review open PRs or submit your own.
• Report Issues: Submit bug reports or feature requests.
• Enhance Documentation: Help improve or expand the documentation.

How to Contribute

1. Fork the repository on GitHub.
2. Clone your forked repository to your local machine.
3. Create a new branch for your changes.
4. Make your changes and commit them.
5. Push your changes to GitHub.
6. Submit a pull request to the main repository.

License

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

Acknowledgments

• Grateful for the data provided by Ultimate Mushroom.
• Inspired by the vibrant Python community.

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