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bank-marketing

bank-marketing

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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
V9nominal3 unique values
0 missing
V16nominal4 unique values
0 missing
V15numeric24 unique values
0 missing
V14numeric292 unique values
0 missing
V13numeric32 unique values
0 missing
V12numeric875 unique values
0 missing
V11nominal12 unique values
0 missing
V10numeric31 unique values
0 missing
V1numeric67 unique values
0 missing
V8nominal2 unique values
0 missing
V7nominal2 unique values
0 missing
V6numeric2353 unique values
0 missing
V5nominal2 unique values
0 missing
V4nominal4 unique values
0 missing
V3nominal3 unique values
0 missing
V2nominal12 unique values
0 missing
Show all 17 features

107 properties

NumberOfInstances
4521
Number of instances (rows) of the dataset.
NumberOfFeatures
17
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
7
Number of numeric attributes.
NumberOfSymbolicFeatures
10
Number of nominal attributes.
AutoCorrelation
0.8
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.52
Entropy of the target attribute values.
DecisionStumpAUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
41.77
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
88.48
Percentage of instances belonging to the most frequent class.
MajorityClassSize
4000
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
3.07
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
88.39
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1422.66
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.04
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
12
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
6.6
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
3009.64
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.42
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
28.19
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
255.26
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.01
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
113.74
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
4.6
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.36
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
484.75
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.12
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.04
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.54
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.09
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.69
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
11.52
Percentage of instances belonging to the least frequent class.
MinorityClassSize
521
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
23.53
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
41.18
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
58.82
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.77
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.35
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
2.79
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.7
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.11
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.19
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
12.53
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
39.77
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
2.77
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
10.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
2.27
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
52
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
263.96
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
5.88
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
259.86
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
3.98
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on bank-marketing
1228 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on bank-marketing
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on bank-marketing
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
Supervised Classification on bank-marketing
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Clustering on bank-marketing
0 runs
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
Clustering on bank-marketing
0 runs - estimation_procedure: 50 times Clustering
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