Classification of Iris Flower Dataset using Different Algorithms

S.A. Mithy¹ , S. Hossain² , S. Akter³ , U. Honey⁴ , S.B. Sogir⁵

1. Center for Community Health and Research, Gonoshasthaya Samaj Vittik Medical College, Savar, Dhaka-1344, Bangladesh.
2. Center for Multidisciplinary Research, Gono Bishwabidyalay, Savar, Dhaka-1344, Bangladesh.
3. Dept. of Economics, Gono Bishwabidyalay. Savar, Dhaka-1344, Bangladesh.
4. Dept. of Computer Science and Engineering, Gono Bishwabidyalay. Savar, Dhaka-1344, Bangladesh.
5. Dept. of Statistics, Jahangirnagar University, Dhaka-1342, Bangladesh..

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.6 , pp.1-10, Dec-2022

Online published on Dec 31, 2022

Copyright © S.A. Mithy, S. Hossain, S. Akter, U. Honey, S.B. Sogir . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The Iris dataset is one of the most famous dataset containing data on four attributes named as Sepal.length, Sepal.width, Petal.length, Petal.width and three classes or subspecies named as Sentosa,Versicolor and Virginic each class has 50 samples. The measurement of four attributes in CM (centimeters). This data set was developed by Ronald Fisher in 1936. This is available on UCI data set. In this study we want to show that how to solve the classification problem using some algorithms like K-means clustering, Random Forest decision, SVM, Logistic Regression, KNN, K-medoids. In addition, we also worked on four features to a advanced feature. The scikit tool we use for implementation. In this study applies classification and regression algorithms on the iris dataset by discovering and analyzing the patterns.

Key-Words / Index Term :
Iris dataset, Logistic Regression, k-nearest neighbors, Support Vector Machine, Random Forest, K-means clustering, K-medoids.

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