invalid_inputs:
thin is the example book with its licence dates dropped. To rate it, write each missing fact as a scenario,
and rate the study with the program:
Find the facts the book lacks
Eachinvalid_inputs row’s reason names the rater field it lacks, then the book column that field
comes from. Count the rows by that column:
prepare derives years_licensed from each driver’s first_licensed_date, so that column is the
fact to supply. A program traces book columns from its example book. A reason that ends at the
rater field counts as NaN, so read the field at the start of that reason. A refused row names the carrier rule that declines it, in reason_code.
program.rater.inputs marks each field the rater requires. An optional field means what its RSL
says: a filed default, such as 12,000 annual miles, or an absent coverage.
Write each assumption as a scenario
Rate the study
baseline is the book as reported, so it keeps its unrated rows. In each scenario, 281
submissions stay unrated: 186 have a driver with a blank birth date, so the scenario leaves
drivers.first_licensed_date blank, and 95 have a vehicle with a blank garaging ZIP
(vehicles.garaging_postal_code).
Compare the assumptions
dropna keeps the policies that every scenario rates, so each total compares the same policies.
premium is each policy’s annual premium. A later licence age gives fewer years licensed, so the
premium rises. The assumption moves the book’s total by 9.5% from age 16 to age 21.