Regression Model for Salary Prediction

Hey everyone!

Explanation

I’ve just started to get into Knime and found an interesting dataset on salary data.

(Source: A real-world, messy dataset to practice on | R-bloggers)

Now I’m trying to train a regression model to predict salary based on different nominal and ordinal parameters (e.g. Industry, Age-Class, US-State, etc.).

I’ve tried a bunch of different regression types such as Linear, Polynomial, Tree Ensemble, Gradient Boosted and Random forrest as well as feature selection loops on all of them.

But I can’t get an R^2 value over 0,3 and a mean absolute percentage error of under 35%.

Question

So I wanted to ask if maybe someone more experienced can take a look at my workflow and see if there’s anything I could improve? The dataset is probably less than Ideal, considering it has no numeric data expect for salary and doesn’t include (usable) data on job-titles, but maybe there is something I could do to improve my results?

Here’s the link to my workflow :slight_smile:

drive.google.com

I’m looking forward to your feedback!

Best regards

Karim

PS: I’m still super new so please excuse any rookie mistakes :')