Open AccessIRIS Species Predictor
Author(s) -
Damera Rajkumar
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40097
Subject(s) - artificial intelligence , computer science , categorical variable , machine learning , data set , iris recognition , identification (biology) , iris (biosensor) , artificial neural network , set (abstract data type) , class (philosophy) , pattern recognition (psychology) , biometrics , biology , botany , programming language
In Machine Learning, we are using semi-automated extraction of knowledge of data for identifying IRIS flower species. Classification is a supervised learning in which the response is categorical that is its values are in finite unordered set. To simply the problem of classification, scikit learn tools have been used. This paper focuses on IRIS flower classification using Machine Learning with scikit tools. Here the problem concerns the identification of IRIS flower species on the basis of flowers attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS flower and how the prediction was made from analyzing the pattern to from the class of IRIS flower. In this paper we train the machine learning model with data and when unseen data is discovered the predictive model predicts the species using what it has been learnt from the trained data. Keywords: MATLAB, Machine learning, Neural Network.