🍄 Chat bot for recognizing edible mushrooms.

A machine-learning project to determine if a certain mushroom is edible or poisonous.

I explain how to export weights from a Keras model and import those weights in Keras.js, a JavaScript framework for running pre-trained neural networks in the browser. I show you later how to include the final result into a Phonegap Cordova mobile application.

Learn to use Support Vector Machines in Python(sklearn) and R

This project features AI models for identifying mushrooms and plants as poisonous or edible using image-based predictions. Both models are tested through an interactive Gradio interface, ensuring user-friendly and accurate identification for foragers and researchers.

Classifies mushrooms as poisonous or edible using machine learning algorithms

Naive Bayes and Decision Tree Classifiers implemented with Scikit-Learn and Graphviz visualization (Datasets - News, Mushroom, Income)

Wild mushrooms can be edible or poisonous. Many of them look very similar. For a normal person it is hard to tell which mushroom is safe and which is dangerous. The task is Given the physical features of a mushroom, predict if it is **edible** or **poisonous**. This is solved as a binary classification problem using Decision Tree and Random Forest.

As eating healthy is on the rise, how do we know if we can safely enjoy our fabulous fungi? This repository uses machine learning and visualizations to help classify which are the most dangerous and should be avoided.

🍄 Extract logical rules for mushroom edibility: Neural Networks; Genetic Algorithm + Decision Tree

Machine Learning Model to classify whether a Mushroom is Edible or Poisonous by its features

CAN YOU EAT IT is a web application that allows users to identify fungi by location & season.

A comprehensive guide, created in September 2023, for collecting and analyzing weather data from stations across the Appennino in Reggio Emilia and Modena. It provides tools for data acquisition, storage, and visualization, with a particular focus on identifying optimal meteorological conditions for mushroom hunting.

This project aims to accurately classify mushrooms as either poisonous or non-poisonous using supervised machine learning techniques.

Capstone project for WGU's Computer Science program.

Streamlit web app for binary classification of mushrooms using SVM, Logistic Regression, and Random Forest with hyperparameter tuning and model evaluation metrics.

Playground with mushrooms for testing divide and conquer algorithm that learns decision trees.

Advanced deep learning API for multi-feature mushroom prediction including species, family, edibility, habitat, and seasonal occurrence.