Mushroom Classification Project

This project is a web-based application designed to classify mushrooms as either edible or poisonous based on various features. The application is built using Python and Flask, with a machine learning model trained using scikit-learn.

Project Details

This project uses a trained machine learning model to predict whether a mushroom is edible or poisonous based on the following features:

• Bruises: Whether the mushroom has bruises or not.
• Odor: The smell of the mushroom.
• Gill Size: The size of the mushroom's gills.
• Gill Color: The color of the mushroom's gills.
• Stalk Shape: The shape of the mushroom's stalk.
• Spore Print Color: The color of the mushroom's spore print.
• Population: The distribution pattern of the mushroom population.

Users interact with the application by selecting options for each feature, and the model predicts the classification based on the selected inputs.

Implementation Details

The application is structured using Flask to serve HTML templates and handle user input. The machine learning model and encoders are loaded from pre-trained pickle files.

Project Structure

PoisonousMushroomClassification/
│
├── templates/
│   ├── index.html       # Main form for user input
│   ├── result.html      # Displays the classification result
│
├── static/
│   ├── style/
│   │   └── main.css      # Custom CSS for styling
│   ├── images/           # Optional images for visual representation (Future Implementation)
│
├── mushroom_classification_UI.py  # Main Flask application
├── finalModel.pkl       # Trained machine learning model
├── ecoders.pkl          # Encoders for categorical features
├── MushroomClassifier.ipynb   # Notebook outlining the model training process
└── README.md            # Project documentation

Required Python Modules

To run this project locally, you'll need to have Python installed along with the following modules:

Flask: A lightweight WSGI web application framework. Pandas: A powerful data manipulation and analysis library. Scikit-learn: A machine learning library for Python. You can install the necessary packages using pip:

pip install flask pandas scikit-learn

How it works

1. User Input: The user selects options for various mushroom features on the web page.
2. Data Processing: The selected options are encoded using pre-trained label encoders.
3. Prediction: The encoded data is passed to the machine learning model, which predicts whether the mushroom is edible or poisonous.
4. Result Display: The result is displayed back to the user on a new web page.

Developed By

This project was developed by Aditya Goyal and Sanyam Garg.