IRIS Flower Species Prediction Using Machine Learning and Web Based Interactive Tool for Non Technical Users

Authors- Assistant Professor Mrs I.Sravani, R.S.MD.Sahil, Y Jaya Krishna, G Murari, M Murali, P Raghu

Abstract-The Iris flower species prediction tool is a web-based application that leverages machine learning to classify Iris flowers into one of three species (Setosa, Versicolor, or Virginica) based on their physical measurements: sepal length, sepal width, petal length, and petal width. Using a classification model trained on the famous Iris dataset, the tool predicts the species of a flower given these inputs. The system is designed to be user-friendly and accessible for non-technical users. A simple web interface, built with Flask, allows users to input flower measurements and receive predictions in real-time. This web tool integrates the power of machine learning with an intuitive user experience, making it easy for anyone, regardless of their technical background, to interact with and benefit from the model1. The system also includes validation features and visualizations to further enhance user engagement and understanding. By deploying the model on cloud platforms like Heroku, this tool can be accessed globally, serving educational purposes or assisting botanists and enthusiasts in identifying Iris species based on simple measurements.

DOI: /10.61463/ijset.vol.13.issue1.180