A simple machine learning app for detecting edible mushrooms

Introduction

This application uses the Mushroom Classification dataset from https://www.kaggle.com for detecting if the mushrooms are poisonous or edible. pycaret library is used to preprocess the data and train the model. The model training is done using a jupyter notebook.

FastAPI is used to server the model on the REST API.

REST API can be tested using REST Client extension in VS Code. Extension can be found here: https://marketplace.visualstudio.com/items?itemName=humao.rest-client

Technology stack

Library and packages

• pycaret
• FastApi
• Pandas
• Jupyter Notebook

IDE

Visual Studio Code

setup

Dependencies for running this application can be installed using the requirements.txt file.

pip install -r requirements.txt

Train model

The model can be trained by running the jupyter notebook. Name of the notebook is mushroom_detection_101.ipynb

Serve the model

The api for serving this model is implemented using FastAPI. FastAPI server can be started using the following command

uvicorn main:app --reload

Acknowledgements

Dataset: https://www.kaggle.com/uciml/mushroom-classification

Pycaret: https://github.com/pycaret/pycaret