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