KNIME tutorial: Kaggle Titanic machine learning problem data prep and cleaning (part 1)

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KNIME Machine Learning Tutorial

I  love to help people who are climbing the career ladder, looking to make a switch, or established in their fields, to learn to use more analytics and data science in their work.  One of the things that has fascinated me for years is how people say they want to learn, but immediately hedge saying, “there is no way I can learn to code.”  That is where my KNIME tutorial plays in; there is no coding required to build a machine learning model.

 

Helping people overcome that mental block is one of my inspirations to starting a series of tutorials on how to create machine learning models without coding by using KNIME.  My first tutorial utilizes the Kaggle Titanic: Machine Learning From Disaster problem.

 

I’ll walk everyone through setting up and cleaning the data for modeling, utilizing a random forest model to make the predictions, and then some basic feature engineering to improve the model.

 

This is the first of three videos that total around 30 minutes.  If you are serious about wanting to learn, shouldn’t you be willing to give up half a lunch break to get started?

 

This is not rocket science, you can learn the fundamentals.  After all, I figured it out, and I’m not exactly Ben Taylor or anything.