# lets take a look at our coefficients from earlier.
# we can see that the features that impact survival positively are 'Contract_One year', 'Contract_Two year',
# 'PaymentMethod_Bank transfer (automatic)', 'PaymentMethod_Credit card (automatic)'. Beyond these the results are
# insignificant. Lets compare customers with the features to understand the best place to spend money.
upgrades = ['Contract_One year',
'Contract_Two year',
'PaymentMethod_Bank transfer (automatic)',
'PaymentMethod_Credit card (automatic)']
results_dict = {}
for customer in tqdm(values.index):
actual = data.loc[[customer]]
change = data.loc[[customer]]
results_dict[customer] = [cph.predict_median(actual)]
for upgrade in upgrades:
change[upgrade] = 1 if list(change[upgrade]) == [0] else 0
results_dict[customer].append(cph.predict_percentile(actual, p=likelihood_cutoff))
change[upgrade] = 1 if list(change[upgrade]) == [0] else 0
results_df = pd.DataFrame(results_dict).T
results_df.columns = ['baseline'] + upgrades
actions = values.join(results_df).drop([likelihood_cutoff], axis=1)
# now we can calculate the difference between applying different features from the baseline
actions['CreditCard Diff'] = (
actions['PaymentMethod_Credit card (automatic)'] -
actions['baseline']
) * actions['MonthlyCharges']
actions['BankTransfer Diff'] = (
actions['PaymentMethod_Bank transfer (automatic)'] -
actions['baseline']
) * actions['MonthlyCharges']
actions['1yrContract Diff'] = (
