Best algorithm to measure similarity between one client and a list of clients with mixed data (categorical and numeric). Given that the goal is to find a similar client to get the pricing what would be the best way to do this?

Thanks!

likehelpfulfunny
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I found some answers on other forums. For those having the same problem, this is a list of leads:

- kmeans but only with numeric data
- kmodes with mixed data
- if all data is binary use and each property only occurs once, use Jaccard index
- if a property can occur several times and frequency is important use cosine index
- if you want to compare continuous data, use euclidian distance
- if you have mixed data, you can also use Gower’s general similarity measure

helpful

Just FYI, i’ve tried turning all data into categorical so i defined some ranges for the numeric and i did a Euclidian distance calculation. It’s okayish but it doesn’t really factor automatically that some categories influence more the pricing than others. I added manual weights on each category but not sure how to calibrate all of this.

Why TF would anyone give this away for free?

To prove you’re an Excel genius, obviously.

Why are you doing this? Maybe submit it to alyx, or the lumen.audit.pm email if it's audit related

QI Macros statistical testing across multiple variables 🤷‍♀️

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