How to Create an Iris Flower Classifier in ML using Number Classifier

Introduction

The Animal Classifier is an Artificial Intelligence (AI) and Machine Learning (ML) based project developed using PictoBlox and its Teachable Machine Extension. The main objective of this project is to enable a computer system to recognize and classify different animals from images. By training a machine learning model with multiple images of various animals, the The Iris Flower Classifier is an Artificial Intelligence (AI) and Machine Learning (ML) project developed using PictoBlox. It is designed to classify different species of Iris flowers by analyzing their images through a trained machine learning model.

The Iris flower dataset is one of the most popular datasets used for learning machine learning concepts. In this project, the machine learning model is trained to recognize three different Iris flower species—Iris Setosa, Iris Versicolor, and Iris Virginica. After training, the model predicts the flower category whenever a new image is provided.

This activity helps learners understand the complete image classification workflow including data collection, model training, model testing, and prediction. It also demonstrates how Artificial Intelligence can recognize visual patterns and make intelligent decisions automatically.

Setting Up the Stage / Sprite

1. Open PictoBlox and create a new project and select block coding platform.
   
2. In Machine Learning environment, craete new project which name given as ” Iris Flower Classifier” and select project type is ” Numbers(C/R).
   
3. In new project, add number samples in dataset mode or upload dataset and create dataset.
   
4. After uploading dataset, now we training the model.
5. Enter the Sepal Length, Sepal Width, Petal Length, and Petal Width values from the selected dataset row into their respective input boxes, click the Predict button, and observe the predicted flower class and its score in the Output section.

Output

Conclusion

The Iris Flower Classifier project provides successfully created and tested an Iris Flower Classifier using numerical data in the PictoBlox Machine Learning Environment. By using Sepal Length, Sepal Width, Petal Length, and Petal Width as inputs, the trained model predicts the Iris flower category. This activity helps to understand how machine learning learns patterns from data and uses them to make predictions on new inputs.