This page discusses about the prerequisites and preparation needed to have a better start with modeling with Smartpredict

📚 Prior knowledge

Before starting to model, we need to acquire a few basics .

Some notions in data analytics, reporting and interpretation are needed. That said, since SmartPredict is so easy to use, advanced proficiency in those fields is in no way compulsory. Thanks to its intuitive handling, even beginners in machine learning can utilize it very well.

Prior knowledge of Python language is necessary if you want to extend your project capacities with some specific functions by creating custom modules for instance or using the SmartPredict' s Notebook for fully coded projects.

As it is an online platform, there is no special hardware nor software requirements . However, for a smooth experience , it is always safer to ensure that you have a good internet connection.

🚦 Modeling basics

Modeling a machine learning project can be overwhelming without the right method, tools and process arrangement.

As you surely know, there are 4 great stages involved in the task of ML modeling,simply stated:

  1. Data processing (including pipeline processing)

  2. Model building, training, and fine tuning

  3. Model deployment

  4. and Model testing.

The user needs thus to acquire a few Machine Learning basics in advance . This is especially relevant for the choice of the appropriate algorithms and methods applied to a particular kind of project.

Anyway, tooltips are there to accompany the user with straightforward instructions, so there is no major need to worry about.