Data
bank-marketing

bank-marketing

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael Gomes Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_218 study_241 study_50 study_99 study_271 study_240 study_226
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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

107 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.
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.
2.28
Third quartile of entropy among attributes.
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
34.95
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
41.85
Maximum skewness among attributes of the numeric type.
2.3
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
140.75
Third quartile of kurtosis among attributes of the numeric type.
0.84
Average class difference between consecutive instances.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3044.77
Maximum standard deviation of attributes of the numeric type.
11.7
Percentage of instances belonging to the least frequent class.
41.18
Percentage of numeric attributes.
258.16
Third quartile of means among attributes of the numeric type.
0.81
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.42
Average entropy of the attributes.
5289
Number of instances belonging to the least frequent class.
58.82
Percentage of nominal attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
0.1
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
673.03
Mean kurtosis among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.79
First quartile of entropy among attributes.
8.36
Third quartile of skewness among attributes of the numeric type.
0.43
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
245.82
Mean of means among attributes of the numeric type.
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
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.
0.81
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.48
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.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.76
First quartile of means among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.1
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
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
94.44
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.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.43
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
3.98
Standard deviation of the number of distinct values among attributes of the nominal type.
0.1
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.
0.68
First quartile of skewness among attributes of the numeric type.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.81
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
8.81
Mean skewness among attributes of the numeric type.
3.1
First quartile of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.1
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
0.13
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
88.3
Percentage of instances belonging to the most frequent class.
489.54
Mean standard deviation of attributes of the numeric type.
1.18
Second quartile (Median) of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.43
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
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
39922
Number of instances belonging to the most frequent class.
0.13
Minimal entropy among attributes.
18.15
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.52
Entropy of the target attribute values.
3.06
Maximum entropy among attributes.
-1.06
Minimum kurtosis among attributes of the numeric type.
40.2
Second quartile (Median) of means among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4506.86
Maximum kurtosis among attributes of the numeric type.
0.58
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.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1362.27
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
3.14
Second quartile (Median) of skewness among attributes of the numeric type.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
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.
10.62
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.69
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.

28 tasks

40225 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 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature:
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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
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