Data
bank-marketing

bank-marketing

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
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  • derived study_144 study_52 study_7
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The R version comes without target, i.e.1User 975


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Author: Paulo Cortez, Sérgio Moro Source: [original] (http://www.openml.org/d/1461) - UCI 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. * Dataset: Reduced version (10 % of the examples) of bank-marketing dataset.

17 features

Class (target)nominal2 unique values
0 missing
V1numeric67 unique values
0 missing
V2nominal12 unique values
0 missing
V3nominal3 unique values
0 missing
V4nominal4 unique values
0 missing
V5nominal2 unique values
0 missing
V6numeric2353 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
V12numeric875 unique values
0 missing
V13numeric32 unique values
0 missing
V14numeric292 unique values
0 missing
V15numeric24 unique values
0 missing
V16nominal4 unique values
0 missing

62 properties

4521
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.
3009.64
Maximum standard deviation of attributes of the numeric type.
11.52
Percentage of instances belonging to the least frequent class.
0.35
First quartile of kurtosis among attributes of the numeric type.
259.86
Third quartile of standard deviation of attributes of the numeric type.
1.42
Average entropy of the attributes.
521
Number of instances belonging to the least frequent class.
2.79
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.
28.19
Mean kurtosis among attributes of the numeric type.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
255.26
Mean of means among attributes of the numeric type.
0.7
First quartile of skewness among attributes of the numeric type.
0.01
Average mutual information between the nominal attributes and the target attribute.
3.11
First quartile of standard deviation of attributes of the numeric type.
0.8
Average class difference between consecutive instances.
113.74
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.19
Second quartile (Median) of entropy among attributes.
0.52
Entropy of the target attribute values.
0
Number of attributes divided by the number of instances.
4.6
Average number of distinct values among the attributes of the nominal type.
12.53
Second quartile (Median) of kurtosis among attributes of the numeric type.
41.77
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.36
Mean skewness among attributes of the numeric type.
39.77
Second quartile (Median) of means among attributes of the numeric type.
88.48
Percentage of instances belonging to the most frequent class.
484.75
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
4000
Number of instances belonging to the most frequent class.
0.12
Minimal entropy among attributes.
2.77
Second quartile (Median) of skewness among attributes of the numeric type.
3.07
Maximum entropy among attributes.
-1.04
Minimum kurtosis among attributes of the numeric type.
23.53
Percentage of binary attributes.
10.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
88.39
Maximum kurtosis among attributes of the numeric type.
0.54
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
2.27
Third quartile of entropy among attributes.
1422.66
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
52
Third quartile of kurtosis 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.
41.18
Percentage of numeric attributes.
263.96
Third quartile of means among attributes of the numeric type.
12
The maximum number of distinct values among attributes of the nominal type.
0.09
Minimum skewness among attributes of the numeric type.
58.82
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
6.6
Maximum skewness among attributes of the numeric type.
1.69
Minimum standard deviation of attributes of the numeric type.
0.77
First quartile of entropy among attributes.
5.88
Third quartile of skewness among attributes of the numeric type.

7 tasks

73 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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