Project Request

In this mushroom classification problem, the objective is to create a robust machine-learning model that can accurately determine the edibility of a mushroom-based on its binary attributes. The model should be trained on a comprehensive dataset containing information about different mushroom species and their corresponding labels of being edible or poisonous. The classification model will enable users to make informed decisions about consuming mushrooms while ensuring their safety.The model will be fully deployed using flask.

Field	Description
About	This project aims to develop a machine-learning model that can accurately classify mushrooms as edible or poisonous based on their binary attributes.
Github	Atharva-Malode
Email	atharvamalode21@gmail.com
Label	Machhine-Learning, Decision Tree

https://github.com/Atharva-Malode

Define You

[ x] GSSOC Participant

Mushroom Classification

Description

This project aims to develop a machine-learning model to classify mushrooms as edible or poisonous based on binary attributes. The expected outcome is an accurate tool for identifying safe-to-consume mushrooms, benefiting individuals and promoting safer mushroom consumption practices.

Scope

This project aims to develop a machine-learning model for mushroom classification, with the objective of accurately distinguishing between edible and poisonous mushrooms based on binary attributes. The deliverables include a trained model and accompanying documentation, while constraints involve the availability of a comprehensive dataset and limited computational resources.

Timeline

The Project will be submitted within 2-3 days of assigning. It will be deployed using Flask.

adithya-s-k / World-of-AI