Getting started (Part1) : Iris classification project

This section will introduce you to the Iris classification project , an iconic ML project. We are going to learn how to create it with SmartPredict , in a few simple steps.

Project description

The Iris classification project is a great machine learning classic for becoming familiar with the several steps involved in a ML modeling project.

It encompasses most of the classification basics, therefore, it is suitable for getting started with SmartPredict' s classification modules.

Iris flowers species (Van Zyverden)

The problem consists of categorizing iris flowers according to a definite set of features which are the dimensions of their botanical parts in centimeters:

  • petal length,

  • petal width,

  • sepal length,

  • sepal width.

To prepare for modeling, we are going to use a dataset file containing a certain number of iris flowers .

This information will then be exploited for identifying by deduction which type a random Iris flower belongs to, given its size.

We are going to demonstrate how easy a task it is with the help of SmartPredict, and invite you to try experimenting yourself with its intuitive ML palette of tools.

👣 Aim of this getting-started tutorial

In this getting-started tutorial , you will learn how to create, model and deploy a simple Iris classification project end-to-end.

The step-by-step guidelines will get you started by exploring the workbench and master the nuts and bolts of modeling a machine learning project with the help of flowcharts .

The iris classification project is available as a template project. This is quite an easy model but with enough complexity to illustrate the assembling of a ML project from beginning to end .

As you get used to the general workflow, you may train increasingly difficult models to improve your skills.