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How to get started with prediction modeling in Dataiku?

Parul_ch
Level 3
Level 3
How to get started with prediction modeling in Dataiku?

I'm working on a prediction modeling in Dataiku. I've got my final dataset with the target variable, so my next steps would be Feature selection, outlier treatment, missing values treatment, checking correlation...What features are available in Dataiku to do these steps.

This being my 1st project in Dataiku DSS, so require some guidance to proceed further.

Thanks,

Parul.

 

(Topic title edited by moderator to be more descriptive. Original title "Using Dataiku")

4 Replies
CoreyS
Community Manager
Community Manager

Hi @Parul_chthank you for your post. In terms of ensuring you receive a timely response we suggest that you provide some context with your post title. Here are some Community Resources you may find helpful:

We hope this helps!

Looking for more resources to help you use Dataiku effectively and upskill your knowledge? Check out these great resources: Dataiku Academy | Documentation | Knowledge Base

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ClaudiusH
Community Manager
Community Manager

A great starting point to familiarize yourself with how to perform these tasks is the "Core Designer" learning path in the Dataiku Academy. I'd recommend to get started there.

Parul_ch
Level 3
Level 3
Author

Hi ClaudiusH,

Have already done this training, was apprehensive abt the feature / data wrangling methods available in Dataiku.

Thanks,

Parul.

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taraku
Dataiker
Dataiker

Hi @Parul_ch,

One way to get started learning about Dataiku’s visual machine learning and data preparation features is to visit the Knowledge Base.

You’ll find articles on:

and a hands-on tutorial for creating a correlation matrix.

If you have not already, you can take your Core Designer learning one step further and register for the ML Practitioner learning path. You’ll notice that many of the same lessons in the ML Practitioner learning path are also found in the Knowledge Base. Finally, you can also try browsing the catalog in the Academy for specific lessons and topics.

Happy learning!