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Added generating summary of the datasets to readme;
Updated readme
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@ -20,3 +20,6 @@ vignettes/*.pdf
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data-collection/*/original/*
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data-collection/*/original/*
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data-collection/*/preprocessed/*
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data-collection/*/preprocessed/*
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# markdown outputs
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*.html
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62
README.Rmd
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62
README.Rmd
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@ -0,0 +1,62 @@
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---
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title: "UCI Machine Learning datasets for R"
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author: "Andrzej Wójtowicz"
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output:
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html_document:
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keep_md: yes
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number_sections: yes
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toc: yes
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---
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```{r global-options, include=FALSE}
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knitr::opts_chunk$set(comment="", echo=FALSE,
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warning=FALSE, message=FALSE)
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source('config.R')
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```
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Document generation date: `r Sys.time()`.
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```{r show-datasets, results='asis'}
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library(yaml)
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for (dir.name in dir(PATH_DATASETS))
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{
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config.yaml.file.path = paste0(PATH_DATASETS, dir.name, "/", FILE_CONFIG_YAML)
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config.yaml = yaml.load_file(config.yaml.file.path)
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cat(paste("#", config.yaml$name, "\n\n"))
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cat(paste("**Local directory**:", dir.name, "\n\n"))
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cat(paste0("**Details**: [link](", config.yaml$info, ")\n\n"))
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cat(paste("**Files**:\n\n"))
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for (file.url in config.yaml$urls)
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{
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cat(paste0("* [", URLdecode(basename(file.url)), "](", file.url, ")\n"))
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}
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cat("\n")
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cat(paste0("**Cite**:\n```\n", config.yaml$cite, "\n```\n\n"))
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cat(paste("**Dataset**:\n\n"))
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preprocessed.dir = gsub("\\*", dir.name, PATH_DATASET_PREPROCESSED)
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preprocessed.file.path = paste0(preprocessed.dir, FILE_PREPROCESSED_OUTPUT)
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dataset = readRDS(preprocessed.file.path)
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cat("```\n")
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cat(str(dataset))
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cat("\n```\n\n")
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perc.classes = sort(round(100*as.numeric(
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table(dataset[, ncol(dataset)]))/nrow(dataset), 0))
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cat(paste("**Class imbalance**:",
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paste0(perc.classes[1], "% / ",
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perc.classes[2], "%\n\n")))
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cat("---\n\n")
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}
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```
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README.md
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README.md
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# uci-ml-to-r
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# UCI Machine Learning datasets for R
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Andrzej Wójtowicz
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Document generation date: 2016-04-16 00:40:29.
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# Bank Marketing
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**Local directory**: bank-marketing
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
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**Files**:
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* [bank-additional.zip](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip)
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**Cite**:
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```
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[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
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```
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**Dataset**:
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```
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'data.frame': 38227 obs. of 18 variables:
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$ age : int 56 57 37 40 56 45 59 24 25 25 ...
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$ job : Factor w/ 11 levels "admin.","blue-collar",..: 4 8 8 1 8 8 1 10 8 8 ...
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$ marital : Factor w/ 3 levels "divorced","married",..: 2 2 2 2 2 2 2 3 3 3 ...
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$ education : Ord.factor w/ 6 levels "basic.4y"<"basic.6y"<..: 1 4 4 2 4 3 5 5 4 4 ...
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$ housing : Factor w/ 2 levels "no","yes": 1 1 2 1 1 1 1 2 2 2 ...
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$ loan : Factor w/ 2 levels "no","yes": 1 1 1 1 2 1 1 1 1 1 ...
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$ contact : Factor w/ 2 levels "cellular","telephone": 2 2 2 2 2 2 2 2 2 2 ...
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$ month : Ord.factor w/ 12 levels "jan"<"feb"<"mar"<..: 5 5 5 5 5 5 5 5 5 5 ...
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$ day_of_week : Ord.factor w/ 5 levels "mon"<"tue"<"wed"<..: 1 1 1 1 1 1 1 1 1 1 ...
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$ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
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$ previous : int 0 0 0 0 0 0 0 0 0 0 ...
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$ poutcome : Factor w/ 3 levels "failure","nonexistent",..: 2 2 2 2 2 2 2 2 2 2 ...
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$ emp.var.rate : num 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
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$ cons.price.idx: num 94 94 94 94 94 ...
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$ cons.conf.idx : num -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 ...
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$ euribor3m : num 4.86 4.86 4.86 4.86 4.86 ...
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$ nr.employed : num 5191 5191 5191 5191 5191 ...
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$ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
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```
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**Class imbalance**: 11% / 89%
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---
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# Breast Cancer Wisconsin (Diagnostic)
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**Local directory**: breast-cancer-wisconsin-diagnostic
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)
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**Files**:
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* [wdbc.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data)
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* [wdbc.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names)
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**Cite**:
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```
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https://archive.ics.uci.edu/ml/citation_policy.html
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@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" }
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```
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**Dataset**:
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```
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'data.frame': 569 obs. of 31 variables:
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$ mean radius : num 18 20.6 19.7 11.4 20.3 ...
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$ mean texture : num 10.4 17.8 21.2 20.4 14.3 ...
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$ mean perimeter : num 122.8 132.9 130 77.6 135.1 ...
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$ mean area : num 1001 1326 1203 386 1297 ...
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$ mean smoothness : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
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$ mean compactness : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
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$ mean concavity : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
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$ mean concave points : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
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$ mean symmetry : num 0.242 0.181 0.207 0.26 0.181 ...
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$ mean fractal dimension : num 0.0787 0.0567 0.06 0.0974 0.0588 ...
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$ se radius : num 1.095 0.543 0.746 0.496 0.757 ...
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$ se texture : num 0.905 0.734 0.787 1.156 0.781 ...
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$ se perimeter : num 8.59 3.4 4.58 3.44 5.44 ...
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$ se area : num 153.4 74.1 94 27.2 94.4 ...
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$ se smoothness : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
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$ se compactness : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
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$ se concavity : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
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$ se concave points : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
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$ se symmetry : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
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$ se fractal dimension : num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
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$ worst radius : num 25.4 25 23.6 14.9 22.5 ...
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$ worst texture : num 17.3 23.4 25.5 26.5 16.7 ...
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$ worst perimeter : num 184.6 158.8 152.5 98.9 152.2 ...
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$ worst area : num 2019 1956 1709 568 1575 ...
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$ worst smoothness : num 0.162 0.124 0.144 0.21 0.137 ...
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$ worst compactness : num 0.666 0.187 0.424 0.866 0.205 ...
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$ worst concavity : num 0.712 0.242 0.45 0.687 0.4 ...
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$ worst concave points : num 0.265 0.186 0.243 0.258 0.163 ...
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$ worst symmetry : num 0.46 0.275 0.361 0.664 0.236 ...
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$ worst fractal dimension: num 0.1189 0.089 0.0876 0.173 0.0768 ...
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$ diagnosis : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2 ...
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```
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**Class imbalance**: 37% / 63%
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---
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# Breast Cancer Wisconsin (Original)
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**Local directory**: breast-cancer-wisconsin-original
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29)
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**Files**:
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* [breast-cancer-wisconsin.data](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data)
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* [breast-cancer-wisconsin.names](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names)
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**Cite**:
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```
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O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
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```
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**Dataset**:
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```
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'data.frame': 683 obs. of 10 variables:
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$ Clump Thickness : int 5 5 3 6 4 8 1 2 2 4 ...
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$ Uniformity of Cell Size : int 1 4 1 8 1 10 1 1 1 2 ...
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$ Uniformity of Cell Shape : int 1 4 1 8 1 10 1 2 1 1 ...
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$ Marginal Adhesion : int 1 5 1 1 3 8 1 1 1 1 ...
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$ Single Epithelial Cell Size: int 2 7 2 3 2 7 2 2 2 2 ...
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$ Bare Nuclei : int 2 3 4 6 2 3 3 2 2 2 ...
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$ Bland Chromatin : int 3 3 3 3 3 9 3 3 1 2 ...
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$ Normal Nucleoli : int 1 2 1 7 1 7 1 1 1 1 ...
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$ Mitoses : int 1 1 1 1 1 1 1 1 5 1 ...
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$ Class : Factor w/ 2 levels "2","4": 1 1 1 1 1 2 1 1 1 1 ...
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```
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**Class imbalance**: 35% / 65%
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---
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# Cardiotocography
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**Local directory**: cardiotocography
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Cardiotocography)
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**Files**:
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* [CTG.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls)
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**Cite**:
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```
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Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318
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```
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**Dataset**:
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```
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'data.frame': 2126 obs. of 30 variables:
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$ LB : int 120 132 133 134 132 134 134 122 122 122 ...
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$ AC : int 0 4 2 2 4 1 1 0 0 0 ...
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$ FM : int 0 0 0 0 0 0 0 0 0 0 ...
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$ UC : int 0 4 5 6 5 10 9 0 1 3 ...
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$ ASTV : int 73 17 16 16 16 26 29 83 84 86 ...
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$ MSTV : num 0.5 2.1 2.1 2.4 2.4 5.9 6.3 0.5 0.5 0.3 ...
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$ ALTV : int 43 0 0 0 0 0 0 6 5 6 ...
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$ MLTV : num 2.4 10.4 13.4 23 19.9 0 0 15.6 13.6 10.6 ...
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$ DL : int 0 2 2 2 0 9 6 0 0 0 ...
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$ DP : int 0 0 0 0 0 2 2 0 0 0 ...
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$ Width : int 64 130 130 117 117 150 150 68 68 68 ...
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$ Min : int 62 68 68 53 53 50 50 62 62 62 ...
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$ Max : int 126 198 198 170 170 200 200 130 130 130 ...
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$ Nmax : int 2 6 5 11 9 5 6 0 0 1 ...
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$ Nzeros : int 0 1 1 0 0 3 3 0 0 0 ...
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$ Mode : int 120 141 141 137 137 76 71 122 122 122 ...
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$ Mean : int 137 136 135 134 136 107 107 122 122 122 ...
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$ Median : int 121 140 138 137 138 107 106 123 123 123 ...
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$ Variance: int 73 12 13 13 11 170 215 3 3 1 ...
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$ Tendency: Ord.factor w/ 3 levels "-1"<"0"<"1": 3 2 2 3 3 2 2 3 3 3 ...
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$ A : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
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$ B : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
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$ C : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
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$ D : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
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$ E : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
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$ AD : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 1 1 ...
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$ DE : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
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$ LD : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 2 1 1 1 ...
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$ FS : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 2 2 2 ...
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$ NSP : Factor w/ 2 levels "1","3": 2 1 1 1 1 2 2 2 2 2 ...
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```
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**Class imbalance**: 22% / 78%
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---
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# Default of credit card clients
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**Local directory**: credit-card
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**Details**: [link](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients)
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**Files**:
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* [default of credit card clients.xls](https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls)
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**Cite**:
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```
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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.
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```
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**Dataset**:
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```
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'data.frame': 30000 obs. of 24 variables:
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$ LIMIT_BAL : int 20000 120000 90000 50000 50000 50000 500000 100000 140000 20000 ...
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$ SEX : Factor w/ 2 levels "1","2": 2 2 2 2 1 1 1 2 2 1 ...
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$ EDUCATION : Factor w/ 7 levels "0","1","2","3",..: 3 3 3 3 3 2 2 3 4 4 ...
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$ MARRIAGE : Factor w/ 4 levels "0","1","2","3": 2 3 3 2 2 3 3 3 2 3 ...
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$ AGE : int 24 26 34 37 57 37 29 23 28 35 ...
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$ PAY_0 : int 2 0 0 0 0 0 0 0 0 0 ...
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$ PAY_2 : int 2 2 0 0 0 0 0 0 0 0 ...
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$ PAY_3 : int 0 0 0 0 0 0 0 0 2 0 ...
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$ PAY_4 : int 0 0 0 0 0 0 0 0 0 0 ...
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$ PAY_5 : int 0 0 0 0 0 0 0 0 0 0 ...
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$ PAY_6 : int 0 2 0 0 0 0 0 0 0 0 ...
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$ BILL_AMT1 : int 3913 2682 29239 46990 8617 64400 367965 11876 11285 0 ...
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$ BILL_AMT2 : int 3102 1725 14027 48233 5670 57069 412023 380 14096 0 ...
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$ BILL_AMT3 : int 689 2682 13559 49291 35835 57608 445007 601 12108 0 ...
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$ BILL_AMT4 : int 0 3272 14331 28314 20940 19394 542653 221 12211 0 ...
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$ BILL_AMT5 : int 0 3455 14948 28959 19146 19619 483003 -159 11793 13007 ...
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$ BILL_AMT6 : int 0 3261 15549 29547 19131 20024 473944 567 3719 13912 ...
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$ PAY_AMT1 : int 0 0 1518 2000 2000 2500 55000 380 3329 0 ...
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$ PAY_AMT2 : int 689 1000 1500 2019 36681 1815 40000 601 0 0 ...
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||||||
|
$ PAY_AMT3 : int 0 1000 1000 1200 10000 657 38000 0 432 0 ...
|
||||||
|
$ PAY_AMT4 : int 0 1000 1000 1100 9000 1000 20239 581 1000 13007 ...
|
||||||
|
$ PAY_AMT5 : int 0 0 1000 1069 689 1000 13750 1687 1000 1122 ...
|
||||||
|
$ PAY_AMT6 : int 0 2000 5000 1000 679 800 13770 1542 1000 0 ...
|
||||||
|
$ default payment next month: Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
**Class imbalance**: 22% / 78%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
# ILPD (Indian Liver Patient Dataset)
|
||||||
|
|
||||||
|
**Local directory**: indian-liver
|
||||||
|
|
||||||
|
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset))
|
||||||
|
|
||||||
|
**Files**:
|
||||||
|
|
||||||
|
* [Indian Liver Patient Dataset (ILPD).csv](https://archive.ics.uci.edu/ml/machine-learning-databases/00225/Indian%20Liver%20Patient%20Dataset%20(ILPD).csv)
|
||||||
|
|
||||||
|
**Cite**:
|
||||||
|
```
|
||||||
|
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" }
|
||||||
|
```
|
||||||
|
|
||||||
|
**Dataset**:
|
||||||
|
|
||||||
|
```
|
||||||
|
'data.frame': 583 obs. of 11 variables:
|
||||||
|
$ 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 ...
|
||||||
|
$ TB : num 0.7 10.9 7.3 1 3.9 1.8 0.9 0.9 0.9 0.7 ...
|
||||||
|
$ DB : num 0.1 5.5 4.1 0.4 2 0.7 0.2 0.3 0.3 0.2 ...
|
||||||
|
$ Alkphos : int 187 699 490 182 195 208 154 202 202 290 ...
|
||||||
|
$ Sgpt : int 16 64 60 14 27 19 16 14 22 53 ...
|
||||||
|
$ Sgot : int 18 100 68 20 59 14 12 11 19 58 ...
|
||||||
|
$ TP : num 6.8 7.5 7 6.8 7.3 7.6 7 6.7 7.4 6.8 ...
|
||||||
|
$ ALB : num 3.3 3.2 3.3 3.4 2.4 4.4 3.5 3.6 4.1 3.4 ...
|
||||||
|
$ A/G Ratio: num 0.9 0.74 0.89 1 0.4 1.3 1 1.1 1.2 1 ...
|
||||||
|
$ Selector : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 2 1 ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
**Class imbalance**: 29% / 71%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
# MAGIC Gamma Telescope
|
||||||
|
|
||||||
|
**Local directory**: magic
|
||||||
|
|
||||||
|
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope)
|
||||||
|
|
||||||
|
**Files**:
|
||||||
|
|
||||||
|
* [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)
|
||||||
|
|
||||||
|
**Cite**:
|
||||||
|
```
|
||||||
|
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" }
|
||||||
|
```
|
||||||
|
|
||||||
|
**Dataset**:
|
||||||
|
|
||||||
|
```
|
||||||
|
'data.frame': 19020 obs. of 11 variables:
|
||||||
|
$ fLength : num 28.8 31.6 162.1 23.8 75.1 ...
|
||||||
|
$ fWidth : num 16 11.72 136.03 9.57 30.92 ...
|
||||||
|
$ fSize : num 2.64 2.52 4.06 2.34 3.16 ...
|
||||||
|
$ fConc : num 0.3918 0.5303 0.0374 0.6147 0.3168 ...
|
||||||
|
$ fConc1 : num 0.1982 0.3773 0.0187 0.3922 0.1832 ...
|
||||||
|
$ fAsym : num 27.7 26.27 116.74 27.21 -5.53 ...
|
||||||
|
$ fM3Long : num 22.01 23.82 -64.86 -6.46 28.55 ...
|
||||||
|
$ fM3Trans: num -8.2 -9.96 -45.22 -7.15 21.84 ...
|
||||||
|
$ fAlpha : num 40.09 6.36 76.96 10.45 4.65 ...
|
||||||
|
$ fDist : num 81.9 205.3 256.8 116.7 356.5 ...
|
||||||
|
$ class : Factor w/ 2 levels "g","h": 1 1 1 1 1 1 1 1 1 1 ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
**Class imbalance**: 35% / 65%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
# Seismic bumps
|
||||||
|
|
||||||
|
**Local directory**: seismic-bumps
|
||||||
|
|
||||||
|
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/seismic-bumps)
|
||||||
|
|
||||||
|
**Files**:
|
||||||
|
|
||||||
|
* [seismic-bumps.arff](https://archive.ics.uci.edu/ml/machine-learning-databases/00266/seismic-bumps.arff)
|
||||||
|
|
||||||
|
**Cite**:
|
||||||
|
```
|
||||||
|
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**:
|
||||||
|
|
||||||
|
```
|
||||||
|
'data.frame': 2584 obs. of 16 variables:
|
||||||
|
$ 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 ...
|
||||||
|
$ shift : Factor w/ 2 levels "N","W": 1 1 1 1 1 2 2 1 1 2 ...
|
||||||
|
$ genergy : int 15180 14720 8050 28820 12640 63760 207930 48990 100190 247620 ...
|
||||||
|
$ gpuls : int 48 33 30 171 57 195 614 194 303 675 ...
|
||||||
|
$ gdenergy : int -72 -70 -81 -23 -63 -73 -6 -27 54 4 ...
|
||||||
|
$ gdpuls : int -72 -79 -78 40 -52 -65 18 -3 52 25 ...
|
||||||
|
$ ghazard : Factor w/ 3 levels "a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
|
||||||
|
$ nbumps : int 0 1 0 1 0 0 2 1 0 1 ...
|
||||||
|
$ nbumps2 : int 0 0 0 0 0 0 2 0 0 1 ...
|
||||||
|
$ nbumps3 : int 0 1 0 1 0 0 0 1 0 0 ...
|
||||||
|
$ nbumps4 : int 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ nbumps5 : int 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ energy : int 0 2000 0 3000 0 0 1000 4000 0 500 ...
|
||||||
|
$ maxenergy : int 0 2000 0 3000 0 0 700 4000 0 500 ...
|
||||||
|
$ class : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
**Class imbalance**: 7% / 93%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
# Spambase
|
||||||
|
|
||||||
|
**Local directory**: spambase
|
||||||
|
|
||||||
|
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Spambase)
|
||||||
|
|
||||||
|
**Files**:
|
||||||
|
|
||||||
|
* [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.names](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.names)
|
||||||
|
|
||||||
|
**Cite**:
|
||||||
|
```
|
||||||
|
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" }
|
||||||
|
```
|
||||||
|
|
||||||
|
**Dataset**:
|
||||||
|
|
||||||
|
```
|
||||||
|
'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_address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ...
|
||||||
|
$ word_freq_all : num 0.64 0.5 0.71 0 0 0 0 0 0.46 0.77 ...
|
||||||
|
$ word_freq_3d : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_our : num 0.32 0.14 1.23 0.63 0.63 1.85 1.92 1.88 0.61 0.19 ...
|
||||||
|
$ word_freq_over : num 0 0.28 0.19 0 0 0 0 0 0 0.32 ...
|
||||||
|
$ word_freq_remove : num 0 0.21 0.19 0.31 0.31 0 0 0 0.3 0.38 ...
|
||||||
|
$ word_freq_internet : num 0 0.07 0.12 0.63 0.63 1.85 0 1.88 0 0 ...
|
||||||
|
$ word_freq_order : num 0 0 0.64 0.31 0.31 0 0 0 0.92 0.06 ...
|
||||||
|
$ word_freq_mail : num 0 0.94 0.25 0.63 0.63 0 0.64 0 0.76 0 ...
|
||||||
|
$ word_freq_receive : num 0 0.21 0.38 0.31 0.31 0 0.96 0 0.76 0 ...
|
||||||
|
$ word_freq_will : num 0.64 0.79 0.45 0.31 0.31 0 1.28 0 0.92 0.64 ...
|
||||||
|
$ word_freq_people : num 0 0.65 0.12 0.31 0.31 0 0 0 0 0.25 ...
|
||||||
|
$ word_freq_report : num 0 0.21 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_addresses : num 0 0.14 1.75 0 0 0 0 0 0 0.12 ...
|
||||||
|
$ word_freq_free : num 0.32 0.14 0.06 0.31 0.31 0 0.96 0 0 0 ...
|
||||||
|
$ word_freq_business : num 0 0.07 0.06 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_email : num 1.29 0.28 1.03 0 0 0 0.32 0 0.15 0.12 ...
|
||||||
|
$ word_freq_you : num 1.93 3.47 1.36 3.18 3.18 0 3.85 0 1.23 1.67 ...
|
||||||
|
$ word_freq_credit : num 0 0 0.32 0 0 0 0 0 3.53 0.06 ...
|
||||||
|
$ word_freq_your : num 0.96 1.59 0.51 0.31 0.31 0 0.64 0 2 0.71 ...
|
||||||
|
$ word_freq_font : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_000 : num 0 0.43 1.16 0 0 0 0 0 0 0.19 ...
|
||||||
|
$ word_freq_money : num 0 0.43 0.06 0 0 0 0 0 0.15 0 ...
|
||||||
|
$ word_freq_hp : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_hpl : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_george : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_650 : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_lab : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_labs : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_telnet : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_857 : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_data : num 0 0 0 0 0 0 0 0 0.15 0 ...
|
||||||
|
$ word_freq_415 : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_85 : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_technology : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_1999 : num 0 0.07 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_parts : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_pm : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_direct : num 0 0 0.06 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_cs : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_meeting : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_original : num 0 0 0.12 0 0 0 0 0 0.3 0 ...
|
||||||
|
$ word_freq_project : num 0 0 0 0 0 0 0 0 0 0.06 ...
|
||||||
|
$ word_freq_re : num 0 0 0.06 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_edu : num 0 0 0.06 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_table : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ word_freq_conference : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ char_freq_; : num 0 0 0.01 0 0 0 0 0 0 0.04 ...
|
||||||
|
$ char_freq_( : num 0 0.132 0.143 0.137 0.135 0.223 0.054 0.206 0.271 0.03 ...
|
||||||
|
$ char_freq_[ : num 0 0 0 0 0 0 0 0 0 0 ...
|
||||||
|
$ char_freq_! : num 0.778 0.372 0.276 0.137 0.135 0 0.164 0 0.181 0.244 ...
|
||||||
|
$ char_freq_$ : num 0 0.18 0.184 0 0 0 0.054 0 0.203 0.081 ...
|
||||||
|
$ char_freq_# : num 0 0.048 0.01 0 0 0 0 0 0.022 0 ...
|
||||||
|
$ capital_run_length_average: num 3.76 5.11 9.82 3.54 3.54 ...
|
||||||
|
$ capital_run_length_longest: int 61 101 485 40 40 15 4 11 445 43 ...
|
||||||
|
$ capital_run_length_total : int 278 1028 2259 191 191 54 112 49 1257 749 ...
|
||||||
|
$ class : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
**Class imbalance**: 39% / 61%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
# Wine Quality
|
||||||
|
|
||||||
|
**Local directory**: wine-quality
|
||||||
|
|
||||||
|
**Details**: [link](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)
|
||||||
|
|
||||||
|
**Files**:
|
||||||
|
|
||||||
|
* [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.names](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality.names)
|
||||||
|
|
||||||
|
**Cite**:
|
||||||
|
```
|
||||||
|
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**:
|
||||||
|
|
||||||
|
```
|
||||||
|
'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 ...
|
||||||
|
$ volatile acidity : num 0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...
|
||||||
|
$ citric acid : num 0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...
|
||||||
|
$ residual sugar : num 20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...
|
||||||
|
$ chlorides : num 0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...
|
||||||
|
$ free sulfur dioxide : num 45 14 30 47 47 30 30 45 14 28 ...
|
||||||
|
$ total sulfur dioxide: num 170 132 97 186 186 97 136 170 132 129 ...
|
||||||
|
$ density : num 1.001 0.994 0.995 0.996 0.996 ...
|
||||||
|
$ pH : num 3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...
|
||||||
|
$ sulphates : num 0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...
|
||||||
|
$ alcohol : num 8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...
|
||||||
|
$ color : Factor w/ 2 levels "red","white": 2 2 2 2 2 2 2 2 2 2 ...
|
||||||
|
$ quality : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
**Class imbalance**: 37% / 63%
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user