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Andrzej Wójtowicz 2016-04-16 14:22:23 +02:00
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114
README.md
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@ -3,7 +3,7 @@ Andrzej Wójtowicz
Document generation date: 2016-04-16 13:55:00. Document generation date: 2016-04-16 14:20:08.
@ -28,13 +28,13 @@ Document generation date: 2016-04-16 13:55:00.
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
**Files**: **Source data files**:
* [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 ```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 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**:
@ -64,14 +64,19 @@ Document generation date: 2016-04-16 13:55:00.
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:--------------|---------:| |:--------------|---------:|
|factor | 6| |factor | 6|
|integer | 3| |integer | 3|
|numeric | 5| |numeric | 5|
|ordered factor | 3| |ordered factor | 3|
**Class imbalance**: 11% / 89% (4254 / 33973) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 11 % | 89 % |
| 4254 | 33973 |
--- ---
@ -81,7 +86,7 @@ Document generation date: 2016-04-16 13:55:00.
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)
**Files**: **Source data files**:
* [wdbc.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data) * [wdbc.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data)
* [wdbc.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names) * [wdbc.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names)
@ -132,11 +137,16 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|numeric | 30| |numeric | 30|
**Class imbalance**: 37% / 63% (212 / 357) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 37 % | 63 % |
| 212 | 357 |
--- ---
@ -146,7 +156,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29)
**Files**: **Source data files**:
* [breast-cancer-wisconsin.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data) * [breast-cancer-wisconsin.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data)
* [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)
@ -175,11 +185,16 @@ O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|integer | 9| |integer | 9|
**Class imbalance**: 35% / 65% (239 / 444) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 35 % | 65 % |
| 239 | 444 |
--- ---
@ -189,7 +204,7 @@ O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Cardiotocography) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/Cardiotocography)
**Files**: **Source data files**:
* [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)
@ -237,14 +252,19 @@ Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of C
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:--------------|---------:| |:--------------|---------:|
|factor | 9| |factor | 9|
|integer | 17| |integer | 17|
|numeric | 2| |numeric | 2|
|ordered factor | 1| |ordered factor | 1|
**Class imbalance**: 22% / 78% (471 / 1655) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 22 % | 78 % |
| 471 | 1655 |
--- ---
@ -254,7 +274,7 @@ Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of C
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients)
**Files**: **Source data files**:
* [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)
@ -296,12 +316,17 @@ Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|factor | 3| |factor | 3|
|integer | 20| |integer | 20|
**Class imbalance**: 22% / 78% (6636 / 23364) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 22 % | 78 % |
| 6636 | 23364 |
--- ---
@ -311,7 +336,7 @@ Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset)) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset))
**Files**: **Source data files**:
* [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)
@ -341,13 +366,18 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|factor | 1| |factor | 1|
|integer | 4| |integer | 4|
|numeric | 5| |numeric | 5|
**Class imbalance**: 29% / 71% (167 / 416) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 29 % | 71 % |
| 167 | 416 |
--- ---
@ -357,7 +387,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope)
**Files**: **Source data files**:
* [magic04.data](https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.data) * [magic04.data](https://archive.ics.uci.edu/ml/machine-learning-databases/magic/magic04.data)
* [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)
@ -388,11 +418,16 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|numeric | 10| |numeric | 10|
**Class imbalance**: 35% / 65% (6688 / 12332) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 35 % | 65 % |
| 6688 | 12332 |
--- ---
@ -402,7 +437,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/seismic-bumps) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/seismic-bumps)
**Files**: **Source data files**:
* [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)
@ -436,12 +471,17 @@ Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of d
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|factor | 4| |factor | 4|
|integer | 11| |integer | 11|
**Class imbalance**: 7% / 93% (170 / 2414) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 7 % | 93 % |
| 170 | 2414 |
--- ---
@ -451,7 +491,7 @@ Sikora M., Wrobel L.: Application of rule induction algorithms for analysis of d
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Spambase) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/Spambase)
**Files**: **Source data files**:
* [spambase.DOCUMENTATION](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.DOCUMENTATION) * [spambase.DOCUMENTATION](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.DOCUMENTATION)
* [spambase.data](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data) * [spambase.data](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data)
@ -530,12 +570,17 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|integer | 2| |integer | 2|
|numeric | 55| |numeric | 55|
**Class imbalance**: 39% / 61% (1813 / 2788) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 39 % | 61 % |
| 1813 | 2788 |
--- ---
@ -545,7 +590,7 @@ https://archive.ics.uci.edu/ml/citation_policy.html
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Wine+Quality) **Details**: [link](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)
**Files**: **Source data files**:
* [winequality-red.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv) * [winequality-red.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv)
* [winequality-white.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv) * [winequality-white.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv)
@ -578,12 +623,17 @@ P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferen
**Predictors**: **Predictors**:
|Class | Frequency| |Type | Frequency|
|:-------|---------:| |:-------|---------:|
|factor | 1| |factor | 1|
|numeric | 11| |numeric | 11|
**Class imbalance**: 37% / 63% (2384 / 4113) **Class imbalance**:
| class A | class B |
|:-------:|:--------:|
| 37 % | 63 % |
| 2384 | 4113 |
--- ---

2
data-collection/bank-marketing/config.yaml
View File

@ -7,4 +7,4 @@ urls:
- https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip - https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip
cite: > cite: >
[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 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

21
readme-make.Rmd
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@ -44,7 +44,7 @@ for (dir.name in dir(PATH_DATASETS))
cat(paste0("**Details**: [link](", config.yaml$info, ")\n\n")) cat(paste0("**Details**: [link](", config.yaml$info, ")\n\n"))
cat(paste("**Files**:\n\n")) cat(paste("**Source data files**:\n\n"))
for (file.url in config.yaml$urls) for (file.url in config.yaml$urls)
{ {
cat(paste0("* [", URLdecode(basename(file.url)), "](", file.url, ")\n")) cat(paste0("* [", URLdecode(basename(file.url)), "](", file.url, ")\n"))
@ -68,7 +68,7 @@ for (dir.name in dir(PATH_DATASETS))
df.pred = data.frame(table(sapply(dataset[, 1:(ncol(dataset)-1)], df.pred = data.frame(table(sapply(dataset[, 1:(ncol(dataset)-1)],
function(f){paste(class(f), collapse=" ")}))) function(f){paste(class(f), collapse=" ")})))
colnames(df.pred) = c("Class", "Frequency") colnames(df.pred) = c("Type", "Frequency")
cat(knitr::kable(df.pred, format="markdown"), sep="\n") cat(knitr::kable(df.pred, format="markdown"), sep="\n")
cat("\n") cat("\n")
@ -76,12 +76,17 @@ for (dir.name in dir(PATH_DATASETS))
perc.classes = sort(round(100*as.numeric( perc.classes = sort(round(100*as.numeric(
table(dataset[, ncol(dataset)]))/nrow(dataset), 0)) table(dataset[, ncol(dataset)]))/nrow(dataset), 0))
num.classes = sort(as.numeric(table(dataset[, ncol(dataset)]))) num.classes = sort(as.numeric(table(dataset[, ncol(dataset)])))
cat(paste("**Class imbalance**:",
paste0(perc.classes[1], "% / ", cat("**Class imbalance**:\n\n")
perc.classes[2], "% (",
num.classes[1], " / ", cat(knitr::kable(data.frame(A=c(paste(perc.classes[1], "%"), num.classes[1]),
num.classes[2], ")\n\n"))) B=c(paste(perc.classes[2], "%"), num.classes[2])),
cat("---\n\n") format="markdown", col.names=c("class A", " class B"),
align=c("c", "c")),
sep="\n")
cat("\n---\n\n")
} }
``` ```