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Introduction to AI, risk management and regulation exercises

These exercises are mainly to get your laptop (or possibly Google Colab) environment set-up.

You are free to use python or R to complete any exercise of this workshop.

Data wrangling: big-claim-events.csv (*)

Load into your favorite data frame (R, pandas, polars, ...) the data ../notebooks/data/big-claim-events.csv, and for each of the four sub-populations defined by $\textrm{skin-cancer}=i, \textrm{depression}=j$, where $i,j \in {0, 1}$, calculate the ratio of big-claims to total claims for the sub-population.

Data quality: aggregate-claim.csv (*)

Load into some data frame ../notebooks/data/aggregate-claim.csv. Identify at least one potential data quality issue.

Hint: There are some convenient data profiling tools for both python (e.g. fg-data-profiling, formerly ydata-profiling, formerly-er pandas-profiling) and R (e.g. DataExplorer).