Datasets:

mstz
/

bank

age int64	job string	marital_status string	education_level int64	has_defaulted bool	account_balance int64	has_housing_loan bool	has_personal_loan bool	month_of_last_contact string	number_of_calls_in_ad_campaign int64	days_since_last_contact_of_previous_campaign int64	successful_subscription int64
58	management	married	3	false	2,143	true	false	may	1	-1	0
44	technician	single	2	false	29	true	false	may	1	-1	0
33	entrepreneur	married	2	false	2	true	true	may	1	-1	0
47	blue-collar	married	0	false	1,506	true	false	may	1	-1	0
33	unknown	single	0	false	1	false	false	may	1	-1	0
35	management	married	3	false	231	true	false	may	1	-1	0
28	management	single	3	false	447	true	true	may	1	-1	0
42	entrepreneur	divorced	3	true	2	true	false	may	1	-1	0
58	retired	married	1	false	121	true	false	may	1	-1	0
43	technician	single	2	false	593	true	false	may	1	-1	0
41	admin.	divorced	2	false	270	true	false	may	1	-1	0
29	admin.	single	2	false	390	true	false	may	1	-1	0
53	technician	married	2	false	6	true	false	may	1	-1	0
58	technician	married	0	false	71	true	false	may	1	-1	0
57	services	married	2	false	162	true	false	may	1	-1	0
51	retired	married	1	false	229	true	false	may	1	-1	0
45	admin.	single	0	false	13	true	false	may	1	-1	0
57	blue-collar	married	1	false	52	true	false	may	1	-1	0
60	retired	married	1	false	60	true	false	may	1	-1	0
33	services	married	2	false	0	true	false	may	1	-1	0
28	blue-collar	married	2	false	723	true	true	may	1	-1	0
56	management	married	3	false	779	true	false	may	1	-1	0
32	blue-collar	single	1	false	23	true	true	may	1	-1	0
25	services	married	2	false	50	true	false	may	1	-1	0
40	retired	married	1	false	0	true	true	may	1	-1	0
44	admin.	married	2	false	-372	true	false	may	1	-1	0
39	management	single	3	false	255	true	false	may	1	-1	0
52	entrepreneur	married	2	false	113	true	true	may	1	-1	0
46	management	single	2	false	-246	true	false	may	2	-1	0
36	technician	single	2	false	265	true	true	may	1	-1	0
57	technician	married	2	false	839	false	true	may	1	-1	0
49	management	married	3	false	378	true	false	may	1	-1	0
60	admin.	married	2	false	39	true	true	may	1	-1	0
59	blue-collar	married	2	false	0	true	false	may	1	-1	0
51	management	married	3	false	10,635	true	false	may	1	-1	0
57	technician	divorced	2	false	63	true	false	may	1	-1	0
25	blue-collar	married	2	false	-7	true	false	may	1	-1	0
53	technician	married	2	false	-3	false	false	may	1	-1	0
36	admin.	divorced	2	false	506	true	false	may	1	-1	0
37	admin.	single	2	false	0	true	false	may	1	-1	0
44	services	divorced	2	false	2,586	true	false	may	1	-1	0
50	management	married	2	false	49	true	false	may	2	-1	0
60	blue-collar	married	0	false	104	true	false	may	1	-1	0
54	retired	married	2	false	529	true	false	may	1	-1	0
58	retired	married	0	false	96	true	false	may	1	-1	0
36	admin.	single	1	false	-171	true	false	may	1	-1	0
58	self-employed	married	3	false	-364	true	false	may	1	-1	0
44	technician	married	2	false	0	true	false	may	2	-1	0
55	technician	divorced	2	false	0	false	false	may	1	-1	0
29	management	single	3	false	0	true	false	may	1	-1	0
54	blue-collar	married	2	false	1,291	true	false	may	1	-1	0
48	management	divorced	3	false	-244	true	false	may	1	-1	0
32	management	married	3	false	0	true	false	may	1	-1	0
42	admin.	single	2	false	-76	true	false	may	1	-1	0
24	technician	single	2	false	-103	true	true	may	1	-1	0
38	entrepreneur	single	3	false	243	false	true	may	1	-1	0
38	management	single	3	false	424	true	false	may	1	-1	0
47	blue-collar	married	0	false	306	true	false	may	1	-1	0
40	blue-collar	single	0	false	24	true	false	may	1	-1	0
46	services	married	1	false	179	true	false	may	1	-1	0
32	admin.	married	3	false	0	true	false	may	1	-1	0
53	technician	divorced	2	false	989	true	false	may	1	-1	0
57	blue-collar	married	1	false	249	true	false	may	1	-1	0
33	services	married	2	false	790	true	false	may	1	-1	0
49	blue-collar	married	0	false	154	true	false	may	1	-1	0
51	management	married	3	false	6,530	true	false	may	1	-1	0
60	retired	married	3	false	100	false	false	may	1	-1	0
59	management	divorced	3	false	59	true	false	may	1	-1	0
55	technician	married	2	false	1,205	true	false	may	2	-1	0
35	blue-collar	single	2	false	12,223	true	true	may	1	-1	0
57	blue-collar	married	2	false	5,935	true	true	may	1	-1	0
31	services	married	2	false	25	true	true	may	1	-1	0
54	management	married	2	false	282	true	true	may	1	-1	0
55	blue-collar	married	1	false	23	true	false	may	1	-1	0
43	technician	married	2	false	1,937	true	false	may	1	-1	0
53	technician	married	2	false	384	true	false	may	1	-1	0
44	blue-collar	married	2	false	582	false	true	may	1	-1	0
55	services	divorced	2	false	91	false	false	may	1	-1	0
49	services	divorced	2	false	0	true	true	may	1	-1	0
55	services	divorced	2	true	1	true	false	may	1	-1	0
45	admin.	single	2	false	206	true	false	may	1	-1	0
47	services	divorced	2	false	164	false	false	may	1	-1	0
42	technician	single	2	false	690	true	false	may	1	-1	0
59	admin.	married	2	false	2,343	true	false	may	1	-1	1
46	self-employed	married	3	false	137	true	true	may	1	-1	0
51	blue-collar	married	1	false	173	true	false	may	2	-1	0
56	admin.	married	2	false	45	false	false	may	1	-1	1
41	technician	married	2	false	1,270	true	false	may	1	-1	1
46	management	divorced	2	false	16	true	true	may	2	-1	0
57	retired	married	2	false	486	true	false	may	2	-1	0
42	management	single	2	false	50	false	false	may	1	-1	0
30	technician	married	2	false	152	true	true	may	2	-1	0
60	admin.	married	2	false	290	true	false	may	1	-1	0
60	blue-collar	married	0	false	54	true	false	may	1	-1	0
57	entrepreneur	divorced	2	false	-37	false	false	may	1	-1	0
36	management	married	3	false	101	true	true	may	1	-1	0
55	blue-collar	married	2	false	383	false	false	may	1	-1	0
60	retired	married	3	false	81	true	false	may	1	-1	0
39	technician	married	2	false	0	true	false	may	1	-1	0
46	management	married	3	false	229	true	false	may	1	-1	0

End of preview. Expand in Data Studio

Bank

The Bank dataset from the UCI ML repository. Potential clients are contacted by a bank during a second advertisement campaign. This datasets records the customer, the interaction with the AD campaign, and if they subscribed to a proposed bank plan or not.

Configurations and tasks

Configuration	Task	Description
encoding		Encoding dictionary showing original values of encoded features.
subscription	Binary classification	Has the customer subscribed to a bank plan?

Usage

from datasets import load_dataset


dataset = load_dataset("mstz/bank", "subscription")["train"]

Features

Name	Type
`age`	`int64`
`job`	`string`
`marital_status`	`string`
`education`	`int8`
`has_defaulted`	`int8`
`account_balance`	`int64`
`has_housing_loan`	`int8`
`has_personal_loan`	`int8`
`month_of_last_contact`	`string`
`number_of_calls_in_ad_campaign`	`string`
`days_since_last_contact_of_previous_campaign`	`int16`
`number_of_calls_before_this_campaign`	`int16`
`successfull_subscription`	`int8`

Downloads last month: 45

Size of downloaded dataset files:

2.62 MB

Size of the auto-converted Parquet files:

555 kB

Number of rows:

45,211