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bank-marketing-propensity

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train · 41.2k rows

age int64 17 98	job stringclasses 12 values	marital stringclasses 4 values	education stringclasses 8 values	default stringclasses 3 values	housing stringclasses 3 values	loan stringclasses 3 values	contact stringclasses 2 values	month stringclasses 10 values	day_of_week stringclasses 5 values	duration int64 0 4.92k	campaign int64 1 56	pdays int64 0 999	poutcome stringclasses 3 values	emp.var.rate float64 -3.4 1.4	cons.price.idx float64 92.2 94.8	cons.conf.idx float64 -50.8 -26.9	euribor3m float64 0.63 5.05	nr.employed float64 4.96k 5.23k	y stringclasses 2 values
56	housemaid	married	basic.4y	no	no	no	telephone	may	mon	261	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
57	services	married	high.school	unknown	no	no	telephone	may	mon	149	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
37	services	married	high.school	no	yes	no	telephone	may	mon	226	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
40	admin.	married	basic.6y	no	no	no	telephone	may	mon	151	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
56	services	married	high.school	no	no	yes	telephone	may	mon	307	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
45	services	married	basic.9y	unknown	no	no	telephone	may	mon	198	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
59	admin.	married	professional.course	no	no	no	telephone	may	mon	139	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	blue-collar	married	unknown	unknown	no	no	telephone	may	mon	217	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
24	technician	single	professional.course	no	yes	no	telephone	may	mon	380	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
25	services	single	high.school	no	yes	no	telephone	may	mon	50	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	blue-collar	married	unknown	unknown	no	no	telephone	may	mon	55	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
25	services	single	high.school	no	yes	no	telephone	may	mon	222	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
29	blue-collar	single	high.school	no	no	yes	telephone	may	mon	137	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
57	housemaid	divorced	basic.4y	no	yes	no	telephone	may	mon	293	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
35	blue-collar	married	basic.6y	no	yes	no	telephone	may	mon	146	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
54	retired	married	basic.9y	unknown	yes	yes	telephone	may	mon	174	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
35	blue-collar	married	basic.6y	no	yes	no	telephone	may	mon	312	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
46	blue-collar	married	basic.6y	unknown	yes	yes	telephone	may	mon	440	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
50	blue-collar	married	basic.9y	no	yes	yes	telephone	may	mon	353	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
39	management	single	basic.9y	unknown	no	no	telephone	may	mon	195	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
30	unemployed	married	high.school	no	no	no	telephone	may	mon	38	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	blue-collar	married	basic.4y	unknown	yes	no	telephone	may	mon	262	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	retired	single	high.school	no	yes	no	telephone	may	mon	342	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	technician	single	high.school	no	yes	no	telephone	may	mon	181	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
37	admin.	married	high.school	no	yes	no	telephone	may	mon	172	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
35	technician	married	university.degree	no	no	yes	telephone	may	mon	99	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
59	technician	married	unknown	no	yes	no	telephone	may	mon	93	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
39	self-employed	married	basic.9y	unknown	no	no	telephone	may	mon	233	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
54	technician	single	university.degree	unknown	no	no	telephone	may	mon	255	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	unknown	married	university.degree	unknown	unknown	unknown	telephone	may	mon	362	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
46	admin.	married	unknown	no	no	no	telephone	may	mon	348	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
59	technician	married	unknown	no	yes	no	telephone	may	mon	386	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
49	blue-collar	married	unknown	no	no	no	telephone	may	mon	73	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
54	management	married	basic.4y	unknown	yes	no	telephone	may	mon	230	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
54	blue-collar	divorced	basic.4y	no	no	no	telephone	may	mon	208	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	unknown	married	basic.4y	unknown	yes	no	telephone	may	mon	336	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
34	services	married	high.school	no	no	no	telephone	may	mon	365	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
52	technician	married	basic.9y	no	yes	no	telephone	may	mon	1,666	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	admin.	married	university.degree	no	yes	no	telephone	may	mon	577	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
56	technician	married	basic.4y	no	yes	no	telephone	may	mon	137	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
58	management	unknown	university.degree	no	yes	no	telephone	may	mon	366	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
32	entrepreneur	married	high.school	no	yes	no	telephone	may	mon	314	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
38	admin.	single	professional.course	no	no	no	telephone	may	mon	160	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
57	admin.	married	university.degree	no	no	yes	telephone	may	mon	212	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
44	admin.	married	university.degree	unknown	yes	no	telephone	may	mon	188	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
42	technician	single	professional.course	unknown	no	no	telephone	may	mon	22	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
57	admin.	married	university.degree	no	yes	yes	telephone	may	mon	616	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
40	blue-collar	married	basic.9y	no	no	yes	telephone	may	mon	178	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
35	admin.	married	university.degree	no	yes	no	telephone	may	mon	355	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
45	blue-collar	married	basic.9y	no	yes	no	telephone	may	mon	225	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
54	admin.	married	high.school	no	no	no	telephone	may	mon	160	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
39	housemaid	married	basic.4y	no	no	yes	telephone	may	mon	266	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
60	admin.	married	high.school	no	no	no	telephone	may	mon	253	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
53	admin.	single	professional.course	no	no	no	telephone	may	mon	179	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	blue-collar	married	basic.4y	unknown	no	no	telephone	may	mon	269	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	technician	married	professional.course	unknown	yes	no	telephone	may	mon	135	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
50	management	married	university.degree	unknown	no	yes	telephone	may	mon	161	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
45	services	married	high.school	unknown	yes	no	telephone	may	mon	787	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	unemployed	married	professional.course	unknown	yes	yes	telephone	may	mon	145	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
25	technician	single	university.degree	no	yes	no	telephone	may	mon	174	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
47	entrepreneur	married	university.degree	unknown	no	no	telephone	may	mon	449	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
51	blue-collar	married	basic.9y	no	yes	no	telephone	may	mon	812	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
42	blue-collar	married	basic.6y	unknown	yes	no	telephone	may	mon	164	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
42	blue-collar	married	basic.6y	unknown	no	no	telephone	may	mon	366	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
48	admin.	married	high.school	no	no	no	telephone	may	mon	357	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
37	admin.	married	university.degree	no	no	no	telephone	may	mon	232	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
44	blue-collar	single	basic.9y	no	yes	no	telephone	may	mon	91	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
33	admin.	married	unknown	no	yes	no	telephone	may	mon	273	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
56	admin.	married	basic.9y	no	yes	no	telephone	may	mon	158	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
44	blue-collar	single	basic.4y	unknown	yes	yes	telephone	may	mon	177	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	management	married	basic.6y	no	no	no	telephone	may	mon	200	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
44	management	divorced	university.degree	no	yes	no	telephone	may	mon	172	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
47	admin.	married	university.degree	unknown	yes	no	telephone	may	mon	176	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
57	unknown	married	unknown	unknown	no	no	telephone	may	mon	211	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
37	admin.	married	university.degree	unknown	yes	no	telephone	may	mon	214	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	blue-collar	divorced	basic.4y	unknown	yes	no	telephone	may	mon	1,575	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	yes
55	technician	married	university.degree	no	no	no	telephone	may	mon	349	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
33	services	married	high.school	unknown	yes	no	telephone	may	mon	337	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
55	management	married	unknown	unknown	yes	no	telephone	may	mon	272	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
42	blue-collar	married	basic.9y	unknown	no	no	telephone	may	mon	208	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
50	blue-collar	married	basic.4y	unknown	yes	no	telephone	may	mon	193	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
51	blue-collar	married	basic.4y	unknown	unknown	unknown	telephone	may	mon	212	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
38	admin.	married	high.school	unknown	no	no	telephone	may	mon	165	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
49	entrepreneur	married	university.degree	unknown	yes	no	telephone	may	mon	1,042	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	yes
38	technician	single	university.degree	no	no	yes	telephone	may	mon	20	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
31	admin.	divorced	high.school	no	no	no	telephone	may	mon	246	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
41	management	married	basic.6y	no	no	no	telephone	may	mon	529	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
39	admin.	married	university.degree	no	yes	yes	telephone	may	mon	192	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
49	technician	married	basic.9y	no	no	no	telephone	may	mon	1,467	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	yes
34	admin.	married	high.school	no	yes	no	telephone	may	mon	188	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
35	admin.	married	university.degree	no	yes	no	telephone	may	mon	180	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
57	unknown	married	unknown	unknown	yes	no	telephone	may	mon	48	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
60	admin.	married	unknown	unknown	no	yes	telephone	may	mon	213	2	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
33	unemployed	married	basic.9y	no	no	no	telephone	may	mon	545	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
42	blue-collar	married	basic.6y	no	no	yes	telephone	may	mon	583	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
45	services	married	professional.course	no	yes	no	telephone	may	mon	221	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
42	management	married	university.degree	no	no	no	telephone	may	mon	426	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
53	admin.	divorced	university.degree	unknown	no	no	telephone	may	mon	287	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
37	technician	single	professional.course	no	no	no	telephone	may	mon	197	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no
44	blue-collar	married	basic.6y	no	no	no	telephone	may	mon	257	1	999	nonexistent	1.1	93.994	-36.4	4.857	5,191	no

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Introduction

This project explores several classification techniques as applied to a bank's marketing campaign data. The classification goal is to predict whether the client will subscribe a term deposit (variable y).
Source: https://archive.ics.uci.edu/ml/datasets/bank+marketing

It's recommended that the viewer read the Jupyter Notebook in NBViewer: https://nbviewer.jupyter.org/github/sgus1318/marketing_propensity/blob/master/Bank_DirectMarketing_Propensity.ipynb

Data Dictionary:

Bank client data:

1 - age (numeric)
2 - job : type of job (categorical: 'admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown')
3 - marital : marital status (categorical: 'divorced', 'married', 'single', 'unknown'; note: 'divorced' means divorced or widowed)
4 - education (categorical: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 - housing: has housing loan? (categorical: 'no','yes','unknown')
7 - loan: has personal loan? (categorical: 'no','yes','unknown')

Campaign Data:

8 - contact: contact communication type (categorical: 'cellular','telephone')
9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
11 - duration: last contact duration, in seconds (numeric). IMPORTANT NOTE: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Other:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14 - previous: number of contacts performed before this campaign and for this client (numeric)
15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

Macroeconomic variables:

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)
17 - cons.price.idx: consumer price index - monthly indicator (numeric)
18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)
19 - euribor3m: euribor 3 month rate - daily indicator (numeric)
20 - nr.employed: number of employees - quarterly indicator (numeric)

Created Variables:

21 - prev_campaign_contact - whether or not an individual was contacted during a previous marketing campaign
22 - prev_call - whether or no an individual was contacted already during this campaign
23 - white collar - whether an individual's occupation falls into the category 'entrepreneaur, management, or admin"
24 - age_sq - individual's age squared
25 - age_sqrt - square root of the individual's age
26 - age_ln - the natural log of the individual's age
27 - age_sq - individual's age squared
28 - emp.var.rate_sq - employment variation rate squared
29 - emp.var.rate_sqroot - square root of employment variation rate
30 - emp.var.rate_recip - reciprocal of employment variation rate
31 - emp.var.rate_ln - natural log of employment variation rate
32 - cons.price.idx_sq - consumer price index squared
33 - cons.price.idx_sqroot - square root of consumer price index
34 - cons.price.idx_recip - reciprocal of consumer price index
35 - cons.price.idx_ln - natural log of consumer price index
36 - cons.conf.idx_sq - consumer confidence index squared
37 - cons.conf.idx_sqroot - square root of consumer confidence index
38 - cons.conf.idx_recip - reciprocal of consumer confidence index
39 - cons.conf.idx_ln - natural log of consumer confidence index
40 - euribor3m_sq - euribor 3 month rate squared
41 - euribor3m_sqroot - square root of euribor 3 month rate
42 - euribor3m_recip - reciprocal of euribor 3 month rate
43 - euribor3m_ln - natural log of euribor 3 month rate
44 - nr.employed_sq - number of employees squared
45 - nr.employed_sqroot - square root of number of employees
46 - nr.employed_recip - reciprocal of number of employees
47 - nr.employed_ln - natural log of number of employees

Output variable (target):

Made_Deposit - has the client subscribed a term deposit? (binary: 1:yes, 0:no)

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