Data
bank-marketing

bank-marketing

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
1 likes downloaded by 16 people , 20 total downloads 1 issues 1 downvotes
  • derived study_144 study_52 study_7
Issue #Downvotes for this reason By
The R version comes without target, i.e.1User 975


Loading wiki
Help us complete this description Edit
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

107 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.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.52
Entropy of the target attribute values.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4000
Number of instances belonging to the most frequent class.
0.12
Minimal entropy among attributes.
12.53
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.07
Maximum entropy among attributes.
-1.04
Minimum kurtosis among attributes of the numeric type.
39.77
Second quartile (Median) of means among attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
88.39
Maximum kurtosis among attributes of the numeric type.
0.54
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1422.66
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2.77
Second quartile (Median) of skewness among attributes of the numeric type.
10.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
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.
23.53
Percentage of binary attributes.
2.27
Third quartile of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
41.77
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
12
The maximum number of distinct values among attributes of the nominal type.
0.09
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
52
Third quartile of kurtosis among attributes of the numeric type.
0.8
Average class difference between consecutive instances.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.6
Maximum skewness among attributes of the numeric type.
1.69
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
263.96
Third quartile of means among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3009.64
Maximum standard deviation of attributes of the numeric type.
11.52
Percentage of instances belonging to the least frequent class.
41.18
Percentage of numeric attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.42
Average entropy of the attributes.
521
Number of instances belonging to the least frequent class.
58.82
Percentage of nominal attributes.
5.88
Third quartile of skewness among attributes of the numeric type.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
28.19
Mean kurtosis among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.77
First quartile of entropy among attributes.
259.86
Third quartile of standard deviation of attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
255.26
Mean of means among attributes of the numeric type.
0.13
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.35
First quartile of kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.79
First quartile of means among attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
113.74
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
3.98
Standard deviation of the number of distinct values among attributes of the nominal type.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.6
Average number of distinct values among the attributes of the nominal type.
0.7
First quartile of skewness among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.36
Mean skewness among attributes of the numeric type.
3.11
First quartile of standard deviation of attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
88.48
Percentage of instances belonging to the most frequent class.
484.75
Mean standard deviation of attributes of the numeric type.
1.19
Second quartile (Median) of entropy among attributes.

8 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
0 runs - estimation_procedure: 50 times Clustering
Define a new task