Mushroom Classifier: Machine Learning-based Mushroom Identification

Welcome to the Mushroom Classifier repository, a machine learning-based solution for mushroom identification built using scikit-learn models in a Jupyter notebook. This project aims to accurately classify mushrooms as edible or poisonous, leveraging data analysis and preprocessing to fit the model.

Table of Contents

1. Introduction
2. Dataset
3. Setup & Installation
4. Usage
5. Contribution Guidelines
6. License

Introduction

The Mushroom Classifier is a machine learning project that focuses on accurate mushroom identification. Using scikit-learn models and Python in a Jupyter notebook, this project analyzes and processes a dataset from Kaggle to train a model that can classify mushrooms as edible or poisonous. The goal is to provide a reliable and accessible tool for mushroom enthusiasts and foragers.

Dataset

The dataset used in this project comes from Kaggle and contains information on various mushroom species, including features such as cap shape, cap color, gill attachment, and more. The dataset was thoroughly analyzed and preprocessed to ensure the best possible model fitting.

Dataset source: Kaggle Mushroom Classification Dataset

Setup & Installation

Follow these steps to set up and run the Mushroom Classifier on your local machine:

1. Clone the repository using git clone https://github.com/Briankim254/mushroom-classifier.git
2. Navigate to the project directory using cd mushroom-classifier
3. Create a virtual environment for the project using python3 -m venv venv
4. Activate the virtual environment using source venv/bin/activate (for Unix-based systems) or venv\Scripts\activate (for Windows)
5. Install the required dependencies using pip install -r requirements.txt
6. Launch Jupyter notebook using jupyter notebook

The Jupyter notebook will open in your default web browser, and you can now access and interact with the Mushroom Classifier notebook.

Usage

1. Open the Mushroom_Classifier.ipynb file in the Jupyter notebook interface
2. Execute the cells in sequence to load the dataset, perform data preprocessing, and train the model
3. Examine the model evaluation results and experiment with different scikit-learn models or hyperparameters if desired

Contribution Guidelines

We welcome contributions to improve and expand the Mushroom Classifier project. To contribute, please follow these steps:

1. Fork the repository and create a new branch for your changes
2. Make your changes or additions to the project, including updates to the Jupyter notebook or dataset
3. Create a pull request and wait for a review from a team member

Please ensure that your code adheres to best practices for code quality and documentation.

License

The Mushroom Classifier project is licensed under the MIT License. This allows for open collaboration and sharing of the project while ensuring that contributors retain ownership of their work.