# Project description

<img src="https://1833277725-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lyc1OXsKqB2S62LxsOR%2F-Lyx36Pt5QIlQOnQp777%2F-Lyx3CtBwhiCLVfr970l%2FSmart-avatar.png?alt=media&#x26;token=c36593ab-06ce-4448-bd32-977121380463" alt="" data-size="line">   The **Iris classification project** is a great machine learning classic for becoming familiar with the several steps involved in a ML modeling project.&#x20;

It encompasses most of the classification basics, therefore, it is suitable for getting started with **SmartPredict**' s classification modules.&#x20;

![Iris flowers species (Van Zyverden)](https://1833277725-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lyc1OXsKqB2S62LxsOR%2F-LyxWzsgmZyvgLcO30_M%2F-LyxXNS92zHK-RjTjDwy%2Fvan-zyverden-flower-bulbs-21516-64_400_compressed.jpg?alt=media\&token=a7ac3af7-9070-4cc2-bb11-0af3e9c7a643)

The problem consists of **categorizing iris flowers** according to a definite set of features which are the dimensions of their botanical parts in centimeters:&#x20;

* petal length,&#x20;
* petal width,&#x20;
* sepal length,
* sepal width.&#x20;

To prepare for modeling, we are going to use a dataset file containing a certain number of iris flowers  .                                        &#x20;

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.

## :footprints: 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**.&#x20;

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 .                     &#x20;

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