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README.md
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@ -3,7 +3,7 @@ Andrzej Wójtowicz
Document generation date: 2016-04-16 00:40:29. Document generation date: 2016-04-16 01:01:04.
# Bank Marketing # Bank Marketing
@ -17,13 +17,13 @@ Document generation date: 2016-04-16 00:40:29.
* [bank-additional.zip](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip) * [bank-additional.zip](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip)
**Cite**: **Cite**:
``` ```nohighlight
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014 [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 38227 obs. of 18 variables: 'data.frame': 38227 obs. of 18 variables:
$ age : int 56 57 37 40 56 45 59 24 25 25 ... $ age : int 56 57 37 40 56 45 59 24 25 25 ...
$ job : Factor w/ 11 levels "admin.","blue-collar",..: 4 8 8 1 8 8 1 10 8 8 ... $ job : Factor w/ 11 levels "admin.","blue-collar",..: 4 8 8 1 8 8 1 10 8 8 ...
@ -121,13 +121,13 @@ https://archive.ics.uci.edu/ml/citation_policy.html
* [breast-cancer-wisconsin.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names) * [breast-cancer-wisconsin.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names)
**Cite**: **Cite**:
``` ```nohighlight
O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 683 obs. of 10 variables: 'data.frame': 683 obs. of 10 variables:
$ Clump Thickness : int 5 5 3 6 4 8 1 2 2 4 ... $ Clump Thickness : int 5 5 3 6 4 8 1 2 2 4 ...
$ Uniformity of Cell Size : int 1 4 1 8 1 10 1 1 1 2 ... $ Uniformity of Cell Size : int 1 4 1 8 1 10 1 1 1 2 ...
@ -157,13 +157,13 @@ O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
* [CTG.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls) * [CTG.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls)
**Cite**: **Cite**:
``` ```nohighlight
Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318 Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 2126 obs. of 30 variables: 'data.frame': 2126 obs. of 30 variables:
$ LB : int 120 132 133 134 132 134 134 122 122 122 ... $ LB : int 120 132 133 134 132 134 134 122 122 122 ...
$ AC : int 0 4 2 2 4 1 1 0 0 0 ... $ AC : int 0 4 2 2 4 1 1 0 0 0 ...
@ -213,13 +213,13 @@ Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of C
* [default of credit card clients.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls) * [default of credit card clients.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls)
**Cite**: **Cite**:
``` ```nohighlight
Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480. Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 30000 obs. of 24 variables: 'data.frame': 30000 obs. of 24 variables:
$ LIMIT_BAL : int 20000 120000 90000 50000 50000 50000 500000 100000 140000 20000 ... $ LIMIT_BAL : int 20000 120000 90000 50000 50000 50000 500000 100000 140000 20000 ...
$ SEX : Factor w/ 2 levels "1","2": 2 2 2 2 1 1 1 2 2 1 ... $ SEX : Factor w/ 2 levels "1","2": 2 2 2 2 1 1 1 2 2 1 ...
@ -263,14 +263,14 @@ Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for
* [Indian Liver Patient Dataset (ILPD).csv](https://archive.ics.uci.edu/ml/machine-learning-databases/00225/Indian%20Liver%20Patient%20Dataset%20(ILPD).csv) * [Indian Liver Patient Dataset (ILPD).csv](https://archive.ics.uci.edu/ml/machine-learning-databases/00225/Indian%20Liver%20Patient%20Dataset%20(ILPD).csv)
**Cite**: **Cite**:
``` ```nohighlight
https://archive.ics.uci.edu/ml/citation_policy.html https://archive.ics.uci.edu/ml/citation_policy.html
@misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" }
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 583 obs. of 11 variables: 'data.frame': 583 obs. of 11 variables:
$ Age : int 65 62 62 58 72 46 26 29 17 55 ... $ Age : int 65 62 62 58 72 46 26 29 17 55 ...
$ Gender : Factor w/ 2 levels "Female","Male": 1 2 2 2 2 2 1 1 2 2 ... $ Gender : Factor w/ 2 levels "Female","Male": 1 2 2 2 2 2 1 1 2 2 ...
@ -302,14 +302,14 @@ https://archive.ics.uci.edu/ml/citation_policy.html
* [magic04.names](https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.names) * [magic04.names](https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.names)
**Cite**: **Cite**:
``` ```nohighlight
https://archive.ics.uci.edu/ml/citation_policy.html https://archive.ics.uci.edu/ml/citation_policy.html
@misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" }
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 19020 obs. of 11 variables: 'data.frame': 19020 obs. of 11 variables:
$ fLength : num 28.8 31.6 162.1 23.8 75.1 ... $ fLength : num 28.8 31.6 162.1 23.8 75.1 ...
$ fWidth : num 16 11.72 136.03 9.57 30.92 ... $ fWidth : num 16 11.72 136.03 9.57 30.92 ...
@ -340,13 +340,13 @@ https://archive.ics.uci.edu/ml/citation_policy.html
* [seismic-bumps.arff](https://archive.ics.uci.edu/ml/machine-learning-databases/00266/seismic-bumps.arff) * [seismic-bumps.arff](https://archive.ics.uci.edu/ml/machine-learning-databases/00266/seismic-bumps.arff)
**Cite**: **Cite**:
``` ```nohighlight
Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines. Archives of Mining Sciences, 55(1), 2010, 91-114. Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines. Archives of Mining Sciences, 55(1), 2010, 91-114.
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 2584 obs. of 16 variables: 'data.frame': 2584 obs. of 16 variables:
$ seismic : Factor w/ 2 levels "a","b": 1 1 1 1 1 1 1 1 1 1 ... $ seismic : Factor w/ 2 levels "a","b": 1 1 1 1 1 1 1 1 1 1 ...
$ seismoacoustic: Factor w/ 3 levels "a","b","c": 1 1 1 1 1 1 1 1 1 1 ... $ seismoacoustic: Factor w/ 3 levels "a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
@ -384,14 +384,14 @@ Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of d
* [spambase.names](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.names) * [spambase.names](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.names)
**Cite**: **Cite**:
``` ```nohighlight
https://archive.ics.uci.edu/ml/citation_policy.html https://archive.ics.uci.edu/ml/citation_policy.html
@misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" }
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 4601 obs. of 58 variables: 'data.frame': 4601 obs. of 58 variables:
$ word_freq_make : num 0 0.21 0.06 0 0 0 0 0 0.15 0.06 ... $ word_freq_make : num 0 0.21 0.06 0 0 0 0 0 0.15 0.06 ...
$ word_freq_address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ... $ word_freq_address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ...
@ -471,13 +471,13 @@ https://archive.ics.uci.edu/ml/citation_policy.html
* [winequality.names](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality.names) * [winequality.names](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality.names)
**Cite**: **Cite**:
``` ```nohighlight
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009. P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
``` ```
**Dataset**: **Dataset**:
``` ```nohighlight
'data.frame': 6497 obs. of 13 variables: 'data.frame': 6497 obs. of 13 variables:
$ fixed acidity : num 7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ... $ fixed acidity : num 7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...
$ volatile acidity : num 0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ... $ volatile acidity : num 0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...

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readme-make.Rmd
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@ -38,7 +38,7 @@ for (dir.name in dir(PATH_DATASETS))
} }
cat("\n") cat("\n")
cat(paste0("**Cite**:\n```\n", config.yaml$cite, "\n```\n\n")) cat(paste0("**Cite**:\n```nohighlight\n", config.yaml$cite, "\n```\n\n"))
cat(paste("**Dataset**:\n\n")) cat(paste("**Dataset**:\n\n"))
@ -47,7 +47,7 @@ for (dir.name in dir(PATH_DATASETS))
dataset = readRDS(preprocessed.file.path) dataset = readRDS(preprocessed.file.path)
cat("```\n") cat("```nohighlight\n")
cat(str(dataset)) cat(str(dataset))
cat("\n```\n\n") cat("\n```\n\n")