get churn probabilities and deciles for example churn analysis

churn_prob_deciles.py

## Get a Pandas Series that contains the predicted likelihood
  ## (probability) that each customer in the test set would churn
churn_probs = []
for obsv in rf.predict_proba(X_test_tf):
    churn_probs.append(obsv[1])
churn_probs = pd.Series(data=churn_probs)

## Reset the index of y_test set
y_test_ri = y_test.reset_index(drop=True)

## Create a new DataFrame that includes our predictors (X) from the
  ## test set, our target (y) from the test set, and the probability
  ## of churn output by the model for each customer 
prob_df = X_test.copy().reset_index(drop=True)
prob_df['Churn'] = y_test_ri
prob_df['churn_prob'] = churn_probs

## Split the test set into deciles based on predicted churn probability
prob_df['decile_rank'] = pd.qcut(prob_df['churn_prob'],
                                 10, labels=False)