Machine Learning Model Selection: A Guide to Class Balancing

Comprehensive tutorial on class balancing for anonymized data for machine learning model selection

I’m bringing you a Machine Learning Model Selection project for Multivariate Analysis with Anonymized Data.

This is a comprehensive project where we’ll go from start to finish — from defining the business problem to the model deployment (though we’ll leave the deployment for another time).

There will be two full tutorials for this project, and I want to walk you through a range of techniques, including the added complexity of working with anonymized data — something increasingly common in the job market due to data privacy concerns.

So, what’s the big challenge with working on this type of data? It’s that you don’t have any information on what each variable represents.

Now, that’s tricky, isn’t it? You’ll receive the data, and without knowing what each variable stands for, you’ll need to develop a machine learning model from that.