Mushroom hunting is gaining popularity as a leisure activity for the last couple of years. Modern studies suggest that some mushrooms can be useful to treat anemia, improve body immunity, fight diabetes and a few are even effective to treat cancer. But not all the mushrooms prove to be beneficial. Some mushrooms are poisonous as well and consumption of these may result in severe illnesses in humans and can even cause death. This study aims to examine the data and build different supervised machine learning models that will detect if the mushroom is edible or poisonous. Principal Component Analysis PCA algorithm is used to select the best features from the dataset. Different classifiers like Logistic Regression, Decision Tree, K Nearest Neighbor KNN , Support Vector Machine SVM , Naïve Bayes and Random Forest are applied on the dataset of UCI to classify the mushrooms as edible or poisonous. The performance of the algorithms is compared using Receiver Operating Characteristic ROC Curve. Kanchi Tank "A Comparative Study on Mushroom Classification using Supervised Machine Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42441.pdf Paper URL: https://www.ijtsrd.com/computer-science/embedded-system/42441/a-comparative-study-on-mushroom-classification-using-supervised-machine-learning-algorithms/kanchi-tank
International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 5, July-August 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD42441 | Volume – 5 | Issue – 5 | Jul-Aug 2021
Page 716
A Comparative Study on Mushroom Classification
using Supervised Machine Learning Algorithms
Kanchi Tank
Department of Information Technology, Bharati Vidyapeeth College of Engineering,
University of Mumbai, Navi Mumbai, Maharashtra, India
ABSTRACT
Mushroom hunting is gaining popularity as a leisure activity for the last
couple of years. Modern studies suggest that some mushrooms can be
useful to treat anemia, improve body immunity, fight diabetes and a few are
even effective to treat cancer. But not all the mushrooms prove to be
beneficial. Some mushrooms are poisonous as well and consumption of
these may result in severe illnesses in humans and can even cause death.
This study aims to examine the data and build different supervised machine
learning models that will detect if the mushroom is edible or poisonous.
Principal Component Analysis (PCA) algorithm is used to select the best
features from the dataset. Different classifiers like Logistic Regression,
Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine
(SVM), Naïve Bayes and Random Forest are applied on the dataset of UCI
to classify the mushrooms as edible or poisonous. The performance of the
algorithms is compared using Receiver Operating Characteristic (ROC)
Curve.
KEYWORDS: Mushroom Classification, Principal Component Analysis,
Logistic Regression, Decision Tree, K-Nearest Neighbor, Support Vector
Machine, Naïve Bayes, Random Forest
How to cite this paper: Kanchi Tank "A
Comparative Study on Mushroom
Classification using Supervised Machine
Learning
Algorithms"
Published
in
International Journal
of
Trend
in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-5 | Issue-5, August 2021,
pp.716-723,
URL:
www.