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

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Binary-Classification-Web-App

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

Dataset

This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy.

Installations

Navigate to the working directory and run streamlit run app.py

Note

You should have streamlit installed to run this app pip install streamlit

About

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

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