Bank Marketing

Frame, Acquire, Explore, Refine steps are all the same from the previous notebook.

Exercise Load the train and test dataset



In [ ]:



In [1]:



In [2]:

    
head(train)









    Out[2]:





age job marital education default balance housing loan contact day month duration campaign pdays previous poutcome deposit

	1 58 management married tertiary no 2143 yes no unknown 5 may 261 1 -1 0 unknown no
	2 44 technician single secondary no 29 yes no unknown 5 may 151 1 -1 0 unknown no
	3 33 entrepreneur married secondary no 2 yes yes unknown 5 may 76 1 -1 0 unknown no
	4 47 blue-collar married unknown no 1506 yes no unknown 5 may 92 1 -1 0 unknown no
	5 33 unknown single unknown no 1 no no unknown 5 may 198 1 -1 0 unknown no
	6 28 management single tertiary no 447 yes yes unknown 5 may 217 1 -1 0 unknown no



In [3]:

    
head(test)









    Out[3]:





age job marital education default balance housing loan contact day month duration campaign pdays previous poutcome deposit

	1 38 self-employed single secondary no 677 yes no cellular 14 may 114 2 -1 0 unknown no
	2 58 blue-collar married primary no 5445 yes no cellular 14 apr 391 1 -1 0 unknown no
	3 55 retired married secondary no 5 no no unknown 20 jun 108 1 -1 0 unknown no
	4 26 management single secondary no 63 no no cellular 28 jul 76 4 -1 0 unknown no
	5 48 technician divorced tertiary no 907 no yes cellular 4 aug 103 1 -1 0 unknown no
	6 33 technician married tertiary no 525 yes yes unknown 28 may 139 1 -1 0 unknown no

5. Model

Decision Tree Model



In [6]:

    
library(party)









    



Loading required package: grid
Loading required package: mvtnorm
Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: sandwich



In [16]:

    
model_DT <- ctree(deposit ~., data=train, controls=ctree_control(maxdepth=2))



In [17]:

    
print(model_DT)









    



	 Conditional inference tree with 4 terminal nodes

Response:  deposit 
Inputs:  age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome 
Number of observations:  35211 

1) duration <= 472; criterion = 1, statistic = 5454.5
  2) poutcome == {success}; criterion = 1, statistic = 4525.232
    3)*  weights = 975 
  2) poutcome == {failure, other, unknown}
    4)*  weights = 29606 
1) duration > 472
  5) duration <= 800; criterion = 1, statistic = 174.671
    6)*  weights = 3119 
  5) duration > 800
    7)*  weights = 1511



In [18]:

    
plot(model_DT)



In [19]:

    
plot(model_DT, type="simple")



In [20]:

    
testPrediction <- predict(model_DT, test, type="response")

Exercise

Compute the error and accuracy metrics:

Precision
Recall
AUC
TPR and FPR



In [ ]:

Exercise

Now, set maxdepth to 6 and measure accuracy metrics

Set maxdepth to 0 and measure accuracy metrics



In [ ]:

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	poutcome	deposit
1	58	management	married	tertiary	no	2143	yes	no	unknown	5	may	261	1	-1	unknown	no
2	44	technician	single	secondary	no	29	yes	no	unknown	5	may	151	1	-1	unknown	no
3	33	entrepreneur	married	secondary	no	2	yes	yes	unknown	5	may	76	1	-1	unknown	no
4	47	blue-collar	married	unknown	no	1506	yes	no	unknown	5	may	92	1	-1	unknown	no
5	33	unknown	single	unknown	no	1	no	no	unknown	5	may	198	1	-1	unknown	no
6	28	management	single	tertiary	no	447	yes	yes	unknown	5	may	217	1	-1	unknown	no

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	poutcome	deposit
1	38	self-employed	single	secondary	no	677	yes	no	cellular	14	may	114	2	-1	unknown	no
2	58	blue-collar	married	primary	no	5445	yes	no	cellular	14	apr	391	1	-1	unknown	no
3	55	retired	married	secondary	no	5	no	no	unknown	20	jun	108	1	-1	unknown	no
4	26	management	single	secondary	no	63	no	no	cellular	28	jul	76	4	-1	unknown	no
5	48	technician	divorced	tertiary	no	907	no	yes	cellular	4	aug	103	1	-1	unknown	no
6	33	technician	married	tertiary	no	525	yes	yes	unknown	28	may	139	1	-1	unknown	no