Datasets:

Andyrasika
/

banking-marketing

age int64 18 95	job stringclasses 12 values	marital stringclasses 3 values	education stringclasses 4 values	default stringclasses 2 values	balance int64 -8,019 102k	housing stringclasses 2 values	loan stringclasses 2 values	contact stringclasses 3 values	day int64 1 31	month stringclasses 12 values	duration int64 0 4.92k	campaign int64 1 63	pdays int64 -1 871	poutcome stringclasses 4 values	y stringclasses 2 values
58	management	married	tertiary	no	2,143	yes	no	unknown	5	may	261	1	-1	unknown	no
44	technician	single	secondary	no	29	yes	no	unknown	5	may	151	1	-1	unknown	no
33	entrepreneur	married	secondary	no	2	yes	yes	unknown	5	may	76	1	-1	unknown	no
47	blue-collar	married	unknown	no	1,506	yes	no	unknown	5	may	92	1	-1	unknown	no
33	unknown	single	unknown	no	1	no	no	unknown	5	may	198	1	-1	unknown	no
35	management	married	tertiary	no	231	yes	no	unknown	5	may	139	1	-1	unknown	no
28	management	single	tertiary	no	447	yes	yes	unknown	5	may	217	1	-1	unknown	no
42	entrepreneur	divorced	tertiary	yes	2	yes	no	unknown	5	may	380	1	-1	unknown	no
58	retired	married	primary	no	121	yes	no	unknown	5	may	50	1	-1	unknown	no
43	technician	single	secondary	no	593	yes	no	unknown	5	may	55	1	-1	unknown	no
41	admin.	divorced	secondary	no	270	yes	no	unknown	5	may	222	1	-1	unknown	no
29	admin.	single	secondary	no	390	yes	no	unknown	5	may	137	1	-1	unknown	no
53	technician	married	secondary	no	6	yes	no	unknown	5	may	517	1	-1	unknown	no
58	technician	married	unknown	no	71	yes	no	unknown	5	may	71	1	-1	unknown	no
57	services	married	secondary	no	162	yes	no	unknown	5	may	174	1	-1	unknown	no
51	retired	married	primary	no	229	yes	no	unknown	5	may	353	1	-1	unknown	no
45	admin.	single	unknown	no	13	yes	no	unknown	5	may	98	1	-1	unknown	no
57	blue-collar	married	primary	no	52	yes	no	unknown	5	may	38	1	-1	unknown	no
60	retired	married	primary	no	60	yes	no	unknown	5	may	219	1	-1	unknown	no
33	services	married	secondary	no	0	yes	no	unknown	5	may	54	1	-1	unknown	no
28	blue-collar	married	secondary	no	723	yes	yes	unknown	5	may	262	1	-1	unknown	no
56	management	married	tertiary	no	779	yes	no	unknown	5	may	164	1	-1	unknown	no
32	blue-collar	single	primary	no	23	yes	yes	unknown	5	may	160	1	-1	unknown	no
25	services	married	secondary	no	50	yes	no	unknown	5	may	342	1	-1	unknown	no
40	retired	married	primary	no	0	yes	yes	unknown	5	may	181	1	-1	unknown	no
44	admin.	married	secondary	no	-372	yes	no	unknown	5	may	172	1	-1	unknown	no
39	management	single	tertiary	no	255	yes	no	unknown	5	may	296	1	-1	unknown	no
52	entrepreneur	married	secondary	no	113	yes	yes	unknown	5	may	127	1	-1	unknown	no
46	management	single	secondary	no	-246	yes	no	unknown	5	may	255	2	-1	unknown	no
36	technician	single	secondary	no	265	yes	yes	unknown	5	may	348	1	-1	unknown	no
57	technician	married	secondary	no	839	no	yes	unknown	5	may	225	1	-1	unknown	no
49	management	married	tertiary	no	378	yes	no	unknown	5	may	230	1	-1	unknown	no
60	admin.	married	secondary	no	39	yes	yes	unknown	5	may	208	1	-1	unknown	no
59	blue-collar	married	secondary	no	0	yes	no	unknown	5	may	226	1	-1	unknown	no
51	management	married	tertiary	no	10,635	yes	no	unknown	5	may	336	1	-1	unknown	no
57	technician	divorced	secondary	no	63	yes	no	unknown	5	may	242	1	-1	unknown	no
25	blue-collar	married	secondary	no	-7	yes	no	unknown	5	may	365	1	-1	unknown	no
53	technician	married	secondary	no	-3	no	no	unknown	5	may	1,666	1	-1	unknown	no
36	admin.	divorced	secondary	no	506	yes	no	unknown	5	may	577	1	-1	unknown	no
37	admin.	single	secondary	no	0	yes	no	unknown	5	may	137	1	-1	unknown	no
44	services	divorced	secondary	no	2,586	yes	no	unknown	5	may	160	1	-1	unknown	no
50	management	married	secondary	no	49	yes	no	unknown	5	may	180	2	-1	unknown	no
60	blue-collar	married	unknown	no	104	yes	no	unknown	5	may	22	1	-1	unknown	no
54	retired	married	secondary	no	529	yes	no	unknown	5	may	1,492	1	-1	unknown	no
58	retired	married	unknown	no	96	yes	no	unknown	5	may	616	1	-1	unknown	no
36	admin.	single	primary	no	-171	yes	no	unknown	5	may	242	1	-1	unknown	no
58	self-employed	married	tertiary	no	-364	yes	no	unknown	5	may	355	1	-1	unknown	no
44	technician	married	secondary	no	0	yes	no	unknown	5	may	225	2	-1	unknown	no
55	technician	divorced	secondary	no	0	no	no	unknown	5	may	160	1	-1	unknown	no
29	management	single	tertiary	no	0	yes	no	unknown	5	may	363	1	-1	unknown	no
54	blue-collar	married	secondary	no	1,291	yes	no	unknown	5	may	266	1	-1	unknown	no
48	management	divorced	tertiary	no	-244	yes	no	unknown	5	may	253	1	-1	unknown	no
32	management	married	tertiary	no	0	yes	no	unknown	5	may	179	1	-1	unknown	no
42	admin.	single	secondary	no	-76	yes	no	unknown	5	may	787	1	-1	unknown	no
24	technician	single	secondary	no	-103	yes	yes	unknown	5	may	145	1	-1	unknown	no
38	entrepreneur	single	tertiary	no	243	no	yes	unknown	5	may	174	1	-1	unknown	no
38	management	single	tertiary	no	424	yes	no	unknown	5	may	104	1	-1	unknown	no
47	blue-collar	married	unknown	no	306	yes	no	unknown	5	may	13	1	-1	unknown	no
40	blue-collar	single	unknown	no	24	yes	no	unknown	5	may	185	1	-1	unknown	no
46	services	married	primary	no	179	yes	no	unknown	5	may	1,778	1	-1	unknown	no
32	admin.	married	tertiary	no	0	yes	no	unknown	5	may	138	1	-1	unknown	no
53	technician	divorced	secondary	no	989	yes	no	unknown	5	may	812	1	-1	unknown	no
57	blue-collar	married	primary	no	249	yes	no	unknown	5	may	164	1	-1	unknown	no
33	services	married	secondary	no	790	yes	no	unknown	5	may	391	1	-1	unknown	no
49	blue-collar	married	unknown	no	154	yes	no	unknown	5	may	357	1	-1	unknown	no
51	management	married	tertiary	no	6,530	yes	no	unknown	5	may	91	1	-1	unknown	no
60	retired	married	tertiary	no	100	no	no	unknown	5	may	528	1	-1	unknown	no
59	management	divorced	tertiary	no	59	yes	no	unknown	5	may	273	1	-1	unknown	no
55	technician	married	secondary	no	1,205	yes	no	unknown	5	may	158	2	-1	unknown	no
35	blue-collar	single	secondary	no	12,223	yes	yes	unknown	5	may	177	1	-1	unknown	no
57	blue-collar	married	secondary	no	5,935	yes	yes	unknown	5	may	258	1	-1	unknown	no
31	services	married	secondary	no	25	yes	yes	unknown	5	may	172	1	-1	unknown	no
54	management	married	secondary	no	282	yes	yes	unknown	5	may	154	1	-1	unknown	no
55	blue-collar	married	primary	no	23	yes	no	unknown	5	may	291	1	-1	unknown	no
43	technician	married	secondary	no	1,937	yes	no	unknown	5	may	181	1	-1	unknown	no
53	technician	married	secondary	no	384	yes	no	unknown	5	may	176	1	-1	unknown	no
44	blue-collar	married	secondary	no	582	no	yes	unknown	5	may	211	1	-1	unknown	no
55	services	divorced	secondary	no	91	no	no	unknown	5	may	349	1	-1	unknown	no
49	services	divorced	secondary	no	0	yes	yes	unknown	5	may	272	1	-1	unknown	no
55	services	divorced	secondary	yes	1	yes	no	unknown	5	may	208	1	-1	unknown	no
45	admin.	single	secondary	no	206	yes	no	unknown	5	may	193	1	-1	unknown	no
47	services	divorced	secondary	no	164	no	no	unknown	5	may	212	1	-1	unknown	no
42	technician	single	secondary	no	690	yes	no	unknown	5	may	20	1	-1	unknown	no
59	admin.	married	secondary	no	2,343	yes	no	unknown	5	may	1,042	1	-1	unknown	yes
46	self-employed	married	tertiary	no	137	yes	yes	unknown	5	may	246	1	-1	unknown	no
51	blue-collar	married	primary	no	173	yes	no	unknown	5	may	529	2	-1	unknown	no
56	admin.	married	secondary	no	45	no	no	unknown	5	may	1,467	1	-1	unknown	yes
41	technician	married	secondary	no	1,270	yes	no	unknown	5	may	1,389	1	-1	unknown	yes
46	management	divorced	secondary	no	16	yes	yes	unknown	5	may	188	2	-1	unknown	no
57	retired	married	secondary	no	486	yes	no	unknown	5	may	180	2	-1	unknown	no
42	management	single	secondary	no	50	no	no	unknown	5	may	48	1	-1	unknown	no
30	technician	married	secondary	no	152	yes	yes	unknown	5	may	213	2	-1	unknown	no
60	admin.	married	secondary	no	290	yes	no	unknown	5	may	583	1	-1	unknown	no
60	blue-collar	married	unknown	no	54	yes	no	unknown	5	may	221	1	-1	unknown	no
57	entrepreneur	divorced	secondary	no	-37	no	no	unknown	5	may	173	1	-1	unknown	no
36	management	married	tertiary	no	101	yes	yes	unknown	5	may	426	1	-1	unknown	no
55	blue-collar	married	secondary	no	383	no	no	unknown	5	may	287	1	-1	unknown	no
60	retired	married	tertiary	no	81	yes	no	unknown	5	may	101	1	-1	unknown	no
39	technician	married	secondary	no	0	yes	no	unknown	5	may	203	1	-1	unknown	no
46	management	married	tertiary	no	229	yes	no	unknown	5	may	197	1	-1	unknown	no

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About Dataset

Context

Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing, and digital marketing.

Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call.

The data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe to a term deposit (variable y).

Content The data is related to the direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed by the customer or not. The data folder contains two datasets:-

train.csv: 45,211 rows and 18 columns ordered by date (from May 2008 to November 2010) test.csv: 4521 rows and 18 columns with 10% of the examples (4521), randomly selected from train.csv Detailed Column Descriptions bank client data:

1 - age (numeric)
2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services")
3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)
4 - education (categorical: "unknown","secondary","primary","tertiary")
5 - default: has credit in default? (binary: "yes","no")
6 - balance: average yearly balance, in euros (numeric)
7 - housing: has housing loan? (binary: "yes","no")
8 - loan: has personal loan? (binary: "yes","no")

related with the last contact of the current campaign:

9 - contact: contact communication type (categorical: "unknown","telephone","cellular")
10 - day: last contact day of the month (numeric)
11 - month: last contact month of year (categorical: "jan", "feb", "mar", …, "nov", "dec")
12 - duration: last contact duration, in seconds (numeric)

other attributes:

13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)
15 - previous: number of contacts performed before this campaign and for this client (numeric)
16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")

Output variable (desired target):

17 - y - has the client subscribed a term deposit? (binary: "yes","no")

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Size of downloaded dataset files:

834 kB

Size of the auto-converted Parquet files:

834 kB

Number of rows:

49,732