From Exploration to Modeling: When ML Models Fail in Action — A Case Study

I was asked to explore a car insurance premium dataset and predict a “fair” premium for each policyholder. The task included exploratory data analysis, feature engineering, model selection and comparison, and suggestions for model improvement. It was an interesting and insightful task, as our expectations sometimes fail and we can’t tackle the problem in a routine and straightforward way. I touch upon the exploratory analysis and model training. I will illustrate that machine learning models may not always meet our initial expectations. However, through a shift in perspective and a thorough analysis of the problem, we can still utilize their potential effectively.

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