Authors :
Prannov Jamadagni
Volume/Issue :
Volume 7 - 2022, Issue 2 - February
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3CO5C8p
DOI :
https://doi.org/10.5281/zenodo.6360895
Abstract :
Iris is a flowering plant having 5-6 sepals which
is a characteristic feature of classification of plant species.
To determine the species of this genus the number of
sepals are one of the factors and this work provides in an
easier way to classify the Iris species. Hence, this paper
explores the analysis of commonly used machine learning
supervised classification algorithms on classifying and
predicting three Iris flowering plant species i.e., Iris
setosa, Iris virginica, Iris versicolor from the iris flower
dataset present in UCI Machine Learning Repository
which was compiled by Ronald Fisher. The dataset
contains data of sepal length, sepal width, petal length,
and petal width which is used for predicting the required
species. Classification algorithms are a subset of
supervised learning. Support Vector Machines, Decision
Tree Classifier, and Logisitic Regression are the
algorithms used in this paper for the purpose. The dataset
is analyzed and preprocessed before fitting the algorithms
for the prediction using scikit learn library and the data
is analyzed using Python language. Machine Learning
libraries for python, which include, pandas, numpy,
matplotlib, and seaborn are used. The environment used
for this project is Google Collaboratory. The parameters
are adjusted for the dataset requirements. Finally, the
three algorithms are evaluated based on their accuracy
score and confusion matrix, where the SVM showed
higher accuracy compared to that of the other two
algorithms under these parameters. The significance of
the prediction helps in predicting the species as well as
eliminating the source of human error in separating
different species. This work may contribute to prediction
of more species of different genera. This paper comes
under the theme: Life Sciences, Biomedical Sciences and
Biotechnological aspects.
Keywords :
Iris, Classification Algorithm, SVM, Logistic Regression, Decision Tree Classifier, Machine Learning.
Iris is a flowering plant having 5-6 sepals which
is a characteristic feature of classification of plant species.
To determine the species of this genus the number of
sepals are one of the factors and this work provides in an
easier way to classify the Iris species. Hence, this paper
explores the analysis of commonly used machine learning
supervised classification algorithms on classifying and
predicting three Iris flowering plant species i.e., Iris
setosa, Iris virginica, Iris versicolor from the iris flower
dataset present in UCI Machine Learning Repository
which was compiled by Ronald Fisher. The dataset
contains data of sepal length, sepal width, petal length,
and petal width which is used for predicting the required
species. Classification algorithms are a subset of
supervised learning. Support Vector Machines, Decision
Tree Classifier, and Logisitic Regression are the
algorithms used in this paper for the purpose. The dataset
is analyzed and preprocessed before fitting the algorithms
for the prediction using scikit learn library and the data
is analyzed using Python language. Machine Learning
libraries for python, which include, pandas, numpy,
matplotlib, and seaborn are used. The environment used
for this project is Google Collaboratory. The parameters
are adjusted for the dataset requirements. Finally, the
three algorithms are evaluated based on their accuracy
score and confusion matrix, where the SVM showed
higher accuracy compared to that of the other two
algorithms under these parameters. The significance of
the prediction helps in predicting the species as well as
eliminating the source of human error in separating
different species. This work may contribute to prediction
of more species of different genera. This paper comes
under the theme: Life Sciences, Biomedical Sciences and
Biotechnological aspects.
Keywords :
Iris, Classification Algorithm, SVM, Logistic Regression, Decision Tree Classifier, Machine Learning.
