Data
bank-marketing

bank-marketing

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
1 likes downloaded by 22 people , 31 total downloads 0 issues 0 downvotes
  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_50 study_99
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Paulo Cortez, Sérgio Moro Source: [UCI](https://archive.ics.uci.edu/ml/datasets/bank+marketing) Please cite: S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS. Bank Marketing The data is related with 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 (or not) subscribed. The classification goal is to predict if the client will subscribe a term deposit (variable y). ### Attribute information For more information, read [Moro et al., 2011]. Input variables: - 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")

17 features

Class (target)nominal2 unique values
0 missing
V1numeric77 unique values
0 missing
V2nominal12 unique values
0 missing
V3nominal3 unique values
0 missing
V4nominal4 unique values
0 missing
V5nominal2 unique values
0 missing
V6numeric7168 unique values
0 missing
V7nominal2 unique values
0 missing
V8nominal2 unique values
0 missing
V9nominal3 unique values
0 missing
V10numeric31 unique values
0 missing
V11nominal12 unique values
0 missing
V12numeric1573 unique values
0 missing
V13numeric48 unique values
0 missing
V14numeric559 unique values
0 missing
V15numeric41 unique values
0 missing
V16nominal4 unique values
0 missing

62 properties

45211
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
10
Number of nominal attributes.
23.53
Percentage of binary attributes.
10.62
Second quartile (Median) of standard deviation of attributes of the numeric type.
3.06
Maximum entropy among attributes.
-1.06
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
2.28
Third quartile of entropy among attributes.
4506.86
Maximum kurtosis among attributes of the numeric type.
0.58
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
140.75
Third quartile of kurtosis among attributes of the numeric type.
1362.27
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
41.18
Percentage of numeric attributes.
258.16
Third quartile of means among attributes of the numeric type.
0.04
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
58.82
Percentage of nominal attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
12
The maximum number of distinct values among attributes of the nominal type.
0.09
Minimum skewness among attributes of the numeric type.
0.79
First quartile of entropy among attributes.
8.36
Third quartile of skewness among attributes of the numeric type.
41.85
Maximum skewness among attributes of the numeric type.
2.3
Minimum standard deviation of attributes of the numeric type.
0.32
First quartile of kurtosis among attributes of the numeric type.
257.53
Third quartile of standard deviation of attributes of the numeric type.
3044.77
Maximum standard deviation of attributes of the numeric type.
11.7
Percentage of instances belonging to the least frequent class.
2.76
First quartile of means among attributes of the numeric type.
3.98
Standard deviation of the number of distinct values among attributes of the nominal type.
1.42
Average entropy of the attributes.
5289
Number of instances belonging to the least frequent class.
0
First quartile of mutual information between the nominal attributes and the target attribute.
673.03
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
0.68
First quartile of skewness among attributes of the numeric type.
245.82
Mean of means among attributes of the numeric type.
3.1
First quartile of standard deviation of attributes of the numeric type.
0.84
Average class difference between consecutive instances.
0.01
Average mutual information between the nominal attributes and the target attribute.
1.18
Second quartile (Median) of entropy among attributes.
0.52
Entropy of the target attribute values.
94.44
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
18.15
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
4.6
Average number of distinct values among the attributes of the nominal type.
40.2
Second quartile (Median) of means among attributes of the numeric type.
34.95
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8.81
Mean skewness among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
88.3
Percentage of instances belonging to the most frequent class.
489.54
Mean standard deviation of attributes of the numeric type.
3.14
Second quartile (Median) of skewness among attributes of the numeric type.
39922
Number of instances belonging to the most frequent class.
0.13
Minimal entropy among attributes.

16 tasks

35487 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
22645 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature:
0 runs - target_feature: Class
1300 runs - target_feature: Class
1299 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
Define a new task