Iris Flower Classification by using Random Forest in AWS

Abstract

"Order is an Artificial Intelligence (AI) strategy for predicting crowd enrolment for information events. Neural organisations are presented to improve on the issue of arrangement. This report focuses on IRIS plant orders that use the neutral network. The sepal and petal width of the IRIS plant are used to divide the class of the IRIS plant in the characterization of the IRIS plant. Using the examples of sepal length and petal length, the unclear information can be disguised all the more naturally in the coming years. Fake neural organisations are effectively used for events such as design grouping, work timing, future work advancement, and work recollection. The current study aims to identify the types of iris flowers by using a dataset that was prepared in advance by other biologists to study the flower types using data mining techniques and neural network classifiers. The real issue in this study is figuring out how to create a new way to classify iris flowers and identify their type in order to study their behaviour and help biologists with new machine learning (ML) techniques. This type of problem is known as a classification problem. This can be solved in the proposed work by using a supervised machine learning classification algorithm, the Random Forest- Classification Algorithm (Iris Datasets).The experimental results show that the base error rate was 0.01067, the preparation time was 0.691 ms (milliseconds), and the number of stowed neurons was four.