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bank-marketing

bank-marketing

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Author: Paulo Cortez, Sérgio Moro Source: 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. Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt 1. Title: Bank Marketing 2. Sources Created by: Paulo Cortez (Univ. Minho) and Sérgio Moro (ISCTE-IUL) @ 2012 3. Past Usage: The full dataset was described and analyzed in: 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. 4. Relevant Information: 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). 5. 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
V9nominal3 unique values
0 missing
V16nominal4 unique values
0 missing
V15numeric41 unique values
0 missing
V14numeric559 unique values
0 missing
V13numeric48 unique values
0 missing
V12numeric1573 unique values
0 missing
V11nominal12 unique values
0 missing
V10numeric31 unique values
0 missing
V1numeric77 unique values
0 missing
V8nominal2 unique values
0 missing
V7nominal2 unique values
0 missing
V6numeric7168 unique values
0 missing
V5nominal2 unique values
0 missing
V4nominal4 unique values
0 missing
V3nominal3 unique values
0 missing
V2nominal12 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.
0.84
Average class difference between consecutive instances.
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
0.1
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
0.43
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
0.81
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
0.1
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
0.43
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
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
0.1
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
0.43
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
0.52
Entropy of the target attribute values.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
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
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
88.3
Percentage of instances belonging to the most frequent class.
39922
Number of instances belonging to the most frequent class.
3.06
Maximum entropy among attributes.
4506.86
Maximum kurtosis among attributes of the numeric type.
1362.27
Maximum of means among attributes of the numeric type.
0.04
Maximum mutual information between the nominal attributes and the target attribute.
12
The maximum number of distinct values among attributes of the nominal type.
41.85
Maximum skewness among attributes of the numeric type.
3044.77
Maximum standard deviation of attributes of the numeric type.
1.42
Average entropy of the attributes.
673.03
Mean kurtosis among attributes of the numeric type.
245.82
Mean of means among attributes of the numeric type.
0.01
Average mutual information between the nominal attributes and the target attribute.
94.44
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4.6
Average number of distinct values among the attributes of the nominal type.
8.81
Mean skewness among attributes of the numeric type.
489.54
Mean standard deviation of attributes of the numeric type.
0.13
Minimal entropy among attributes.
-1.06
Minimum kurtosis among attributes of the numeric type.
0.58
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.09
Minimum skewness among attributes of the numeric type.
2.3
Minimum standard deviation of attributes of the numeric type.
11.7
Percentage of instances belonging to the least frequent class.
5289
Number of instances belonging to the least frequent class.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4
Number of binary attributes.
23.53
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
41.18
Percentage of numeric attributes.
58.82
Percentage of nominal attributes.
0.79
First quartile of entropy among attributes.
0.32
First quartile of kurtosis among attributes of the numeric type.
2.76
First quartile of means among attributes of the numeric type.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.68
First quartile of skewness among attributes of the numeric type.
3.1
First quartile of standard deviation of attributes of the numeric type.
1.18
Second quartile (Median) of entropy among attributes.
18.15
Second quartile (Median) of kurtosis among attributes of the numeric type.
40.2
Second quartile (Median) of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3.14
Second quartile (Median) of skewness among attributes of the numeric type.
10.62
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.28
Third quartile of entropy among attributes.
140.75
Third quartile of kurtosis among attributes of the numeric type.
258.16
Third quartile of means among attributes of the numeric type.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
8.36
Third quartile of skewness among attributes of the numeric type.
257.53
Third 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 1
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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.trees.REPTree -L 2
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
3.98
Standard deviation of the number of distinct values among attributes of the nominal type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.13
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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