Classifies mushrooms as poisonous or edible using machine learning algorithms
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Updated
Dec 15, 2016 - Jupyter Notebook
Classifies mushrooms as poisonous or edible using machine learning algorithms
Learn to use Support Vector Machines in Python(sklearn) and R
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.
Jupyter notebook and data for this blog post
Naive Bayes and Decision Tree Classifiers implemented with Scikit-Learn and Graphviz visualization (Datasets - News, Mushroom, Income)
Python script to prepare training data for a Mushroom Classifier
Webapp for mushroom edibility prediction based on its characteristics.
🍄 Extract logical rules for mushroom edibility: Neural Networks; Genetic Algorithm + Decision Tree
Support Vector Machines for classification in the famous Iris and Mushroom datasets. Kernels used for the SVM are linear, polynomial, and radial.
this Mini project was a requirement to pass my Introduction to data science and big data analytics course in university, I did this project in December 2019.
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.
A machine-learning project to determine if a certain mushroom is edible or poisonous.
Performing classification on the edibility of mushrooms from a dataset - analyzing and making classification trees
Contains Docker-Compose files for deploying Mushroom-Classification images
MushPy project is a Data Science project carried out as part of the Data Scientist training at DataScientest. It has the main objective to create recognition models to classify mushrooms
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