'seniority', 'company_id_12', 'company_id_9', 'company_id_10',
'company_id_6', 'company_id_7', 'company_id_8', 'company_id_1',
'dept_design', 'company_id_11', 'company_id_5'
],
axis=1)
data3.head()
data4 = data3.astype('int64')
data4.info()
data4.columns
cph.fit(data4,
duration_col='duration',
event_col='observed',
show_progress=True)
cph.print_summary()
# Finally! Got rid of enough variables -- had too many features for the model to converge.
# Seems like salary is a big predictor of people leaving -- the lower the salary, the more likely they will leave?
# And if they work for company 2
# And if you are an engineer you are also more likely to leave
# Well aspects of this model suggest this way of modeling is not the best -- I don't think we have a linear
# relationship so shouldn't use it...
cph.score_
cph.check_assumptions(data4)
import pkg_resources
import lifelines
pkg_resources.get_distribution("lifelines").version
