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

This repository shows the use of MLflow to track parameters, metrics and artifacts of a pipeline on a machine learning model.

Basic code for RBFN, MLP and KNN evaluated on the mushroom dataset.

Mushroom foraging can be risky, as certain species are highly toxic. This project leverages machine learning to classify mushrooms as edible or poisonous based on their features. The goal is to build an accurate classification model using modern data science techniques.

This project implements a Naive Bayes Classifier from scratch using Python. The classifier is used to classify mushrooms as either edible or poisonous based on various features such as cap shape, cap color, gill color, etc.

This project implements Naive Bayes Classifiers for two data types: Multinomial Naive Bayes Classifier and Gaussian Naive Bayes Classifier. Developed as part of the Probability Theory and Statistics course in the second year of Computer Science at AGH University of Krakow.

Custom implementation of Naïve Bayes and Decision Tree classifiers from scratch, applied to categorical datasets (Mushroom & Congressional Voting). No scikit-learn used—focus on core ML principles like entropy, Laplace smoothing, and tree traversal.

Apply machine-learning models on mushroom data to predict if a mushroom is edible or not

An implementation of decision trees in R

Tree Predictors for Binary Classification for Secondary Mushroom dataset

Proyecto de análisis y detección de hongos venenosos vs comestibles con ML.

Mushroom Dataset Visualization

This Machine Learning app classifies data using SVM, Logistic Regression and Random Forest presenting it in the form of a web app.