lift-2.py

#Actual Outcome (y=1 converted to customer, y=0 did not convert to customer
convert_actual = y
#Predicted Probability that lead converts into a customer
convert_prob = clf.predict_proba(x)
cols = ['CONVERT_ACTUAL','CONVERT_PROB']
data = [convert_actual,convert_prob[:,1]]
df = pd.DataFrame(dict(zip(cols,data)))

#Sort Ascending based on Predicted Probability y=1
df.sort_values('CONVERT_PROB',ascending=False)

#Create 10 Bins where First Bin has Leads with the
#Highest Predicted Probability that y = 1
df['GROUP'] = pd.qcut(df['CONVERT_PROB'],10,labels=False)