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UCI Machine Learning datasets for R
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README.md

UCI Machine Learning datasets for R

Andrzej Wójtowicz

Document generation date: 2016-04-16 13:55:00.

Table of Contents

  1. Bank Marketing
  2. Breast Cancer Wisconsin (Diagnostic)
  3. Breast Cancer Wisconsin (Original)
  4. Cardiotocography
  5. Default of credit card clients
  6. ILPD (Indian Liver Patient Dataset)
  7. MAGIC Gamma Telescope
  8. Seismic bumps
  9. Spambase
  10. Wine Quality

Bank Marketing

Local directory: bank-marketing

Details: link

Files:

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

Dataset:

'data.frame':	38227 obs. of  18 variables:
 $ 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 ...
 $ marital       : Factor w/ 3 levels "divorced","married",..: 2 2 2 2 2 2 2 3 3 3 ...
 $ education     : Ord.factor w/ 6 levels "basic.4y"<"basic.6y"<..: 1 4 4 2 4 3 5 5 4 4 ...
 $ housing       : Factor w/ 2 levels "no","yes": 1 1 2 1 1 1 1 2 2 2 ...
 $ loan          : Factor w/ 2 levels "no","yes": 1 1 1 1 2 1 1 1 1 1 ...
 $ contact       : Factor w/ 2 levels "cellular","telephone": 2 2 2 2 2 2 2 2 2 2 ...
 $ month         : Ord.factor w/ 12 levels "jan"<"feb"<"mar"<..: 5 5 5 5 5 5 5 5 5 5 ...
 $ day_of_week   : Ord.factor w/ 5 levels "mon"<"tue"<"wed"<..: 1 1 1 1 1 1 1 1 1 1 ...
 $ campaign      : int  1 1 1 1 1 1 1 1 1 1 ...
 $ previous      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ poutcome      : Factor w/ 3 levels "failure","nonexistent",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ emp.var.rate  : num  1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
 $ cons.price.idx: num  94 94 94 94 94 ...
 $ cons.conf.idx : num  -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 ...
 $ euribor3m     : num  4.86 4.86 4.86 4.86 4.86 ...
 $ nr.employed   : num  5191 5191 5191 5191 5191 ...
 $ y             : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...

Predictors:

Class Frequency
factor 6
integer 3
numeric 5
ordered factor 3

Class imbalance: 11% / 89% (4254 / 33973)

Breast Cancer Wisconsin (Diagnostic)

Local directory: breast-cancer-wisconsin-diagnostic

Details: link

Files:

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':	569 obs. of  31 variables:
 $ mean radius            : num  18 20.6 19.7 11.4 20.3 ...
 $ mean texture           : num  10.4 17.8 21.2 20.4 14.3 ...
 $ mean perimeter         : num  122.8 132.9 130 77.6 135.1 ...
 $ mean area              : num  1001 1326 1203 386 1297 ...
 $ mean smoothness        : num  0.1184 0.0847 0.1096 0.1425 0.1003 ...
 $ mean compactness       : num  0.2776 0.0786 0.1599 0.2839 0.1328 ...
 $ mean concavity         : num  0.3001 0.0869 0.1974 0.2414 0.198 ...
 $ mean concave points    : num  0.1471 0.0702 0.1279 0.1052 0.1043 ...
 $ mean symmetry          : num  0.242 0.181 0.207 0.26 0.181 ...
 $ mean fractal dimension : num  0.0787 0.0567 0.06 0.0974 0.0588 ...
 $ se radius              : num  1.095 0.543 0.746 0.496 0.757 ...
 $ se texture             : num  0.905 0.734 0.787 1.156 0.781 ...
 $ se perimeter           : num  8.59 3.4 4.58 3.44 5.44 ...
 $ se area                : num  153.4 74.1 94 27.2 94.4 ...
 $ se smoothness          : num  0.0064 0.00522 0.00615 0.00911 0.01149 ...
 $ se compactness         : num  0.049 0.0131 0.0401 0.0746 0.0246 ...
 $ se concavity           : num  0.0537 0.0186 0.0383 0.0566 0.0569 ...
 $ se concave points      : num  0.0159 0.0134 0.0206 0.0187 0.0188 ...
 $ se symmetry            : num  0.03 0.0139 0.0225 0.0596 0.0176 ...
 $ se fractal dimension   : num  0.00619 0.00353 0.00457 0.00921 0.00511 ...
 $ worst radius           : num  25.4 25 23.6 14.9 22.5 ...
 $ worst texture          : num  17.3 23.4 25.5 26.5 16.7 ...
 $ worst perimeter        : num  184.6 158.8 152.5 98.9 152.2 ...
 $ worst area             : num  2019 1956 1709 568 1575 ...
 $ worst smoothness       : num  0.162 0.124 0.144 0.21 0.137 ...
 $ worst compactness      : num  0.666 0.187 0.424 0.866 0.205 ...
 $ worst concavity        : num  0.712 0.242 0.45 0.687 0.4 ...
 $ worst concave points   : num  0.265 0.186 0.243 0.258 0.163 ...
 $ worst symmetry         : num  0.46 0.275 0.361 0.664 0.236 ...
 $ worst fractal dimension: num  0.1189 0.089 0.0876 0.173 0.0768 ...
 $ diagnosis              : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2 ...

Predictors:

Class Frequency
numeric 30

Class imbalance: 37% / 63% (212 / 357)

Breast Cancer Wisconsin (Original)

Local directory: breast-cancer-wisconsin-original

Details: link

Files:

Cite:

O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.

Dataset:

'data.frame':	683 obs. of  10 variables:
 $ 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 Shape   : int  1 4 1 8 1 10 1 2 1 1 ...
 $ Marginal Adhesion          : int  1 5 1 1 3 8 1 1 1 1 ...
 $ Single Epithelial Cell Size: int  2 7 2 3 2 7 2 2 2 2 ...
 $ Bare Nuclei                : int  2 3 4 6 2 3 3 2 2 2 ...
 $ Bland Chromatin            : int  3 3 3 3 3 9 3 3 1 2 ...
 $ Normal Nucleoli            : int  1 2 1 7 1 7 1 1 1 1 ...
 $ Mitoses                    : int  1 1 1 1 1 1 1 1 5 1 ...
 $ Class                      : Factor w/ 2 levels "2","4": 1 1 1 1 1 2 1 1 1 1 ...

Predictors:

Class Frequency
integer 9

Class imbalance: 35% / 65% (239 / 444)

Cardiotocography

Local directory: cardiotocography

Details: link

Files:

Cite:

Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318

Dataset:

'data.frame':	2126 obs. of  30 variables:
 $ LB      : int  120 132 133 134 132 134 134 122 122 122 ...
 $ AC      : int  0 4 2 2 4 1 1 0 0 0 ...
 $ FM      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ UC      : int  0 4 5 6 5 10 9 0 1 3 ...
 $ ASTV    : int  73 17 16 16 16 26 29 83 84 86 ...
 $ MSTV    : num  0.5 2.1 2.1 2.4 2.4 5.9 6.3 0.5 0.5 0.3 ...
 $ ALTV    : int  43 0 0 0 0 0 0 6 5 6 ...
 $ MLTV    : num  2.4 10.4 13.4 23 19.9 0 0 15.6 13.6 10.6 ...
 $ DL      : int  0 2 2 2 0 9 6 0 0 0 ...
 $ DP      : int  0 0 0 0 0 2 2 0 0 0 ...
 $ Width   : int  64 130 130 117 117 150 150 68 68 68 ...
 $ Min     : int  62 68 68 53 53 50 50 62 62 62 ...
 $ Max     : int  126 198 198 170 170 200 200 130 130 130 ...
 $ Nmax    : int  2 6 5 11 9 5 6 0 0 1 ...
 $ Nzeros  : int  0 1 1 0 0 3 3 0 0 0 ...
 $ Mode    : int  120 141 141 137 137 76 71 122 122 122 ...
 $ Mean    : int  137 136 135 134 136 107 107 122 122 122 ...
 $ Median  : int  121 140 138 137 138 107 106 123 123 123 ...
 $ Variance: int  73 12 13 13 11 170 215 3 3 1 ...
 $ Tendency: Ord.factor w/ 3 levels "-1"<"0"<"1": 3 2 2 3 3 2 2 3 3 3 ...
 $ A       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ B       : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
 $ C       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ D       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ E       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ AD      : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 1 1 ...
 $ DE      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ LD      : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 2 1 1 1 ...
 $ FS      : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 2 2 2 ...
 $ NSP     : Factor w/ 2 levels "1","3": 2 1 1 1 1 2 2 2 2 2 ...

Predictors:

Class Frequency
factor 9
integer 17
numeric 2
ordered factor 1

Class imbalance: 22% / 78% (471 / 1655)

Default of credit card clients

Local directory: credit-card

Details: link

Files:

Cite:

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:

'data.frame':	30000 obs. of  24 variables:
 $ 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 ...
 $ EDUCATION                 : Factor w/ 7 levels "0","1","2","3",..: 3 3 3 3 3 2 2 3 4 4 ...
 $ MARRIAGE                  : Factor w/ 4 levels "0","1","2","3": 2 3 3 2 2 3 3 3 2 3 ...
 $ AGE                       : int  24 26 34 37 57 37 29 23 28 35 ...
 $ PAY_0                     : int  2 0 0 0 0 0 0 0 0 0 ...
 $ PAY_2                     : int  2 2 0 0 0 0 0 0 0 0 ...
 $ PAY_3                     : int  0 0 0 0 0 0 0 0 2 0 ...
 $ PAY_4                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ PAY_5                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ PAY_6                     : int  0 2 0 0 0 0 0 0 0 0 ...
 $ BILL_AMT1                 : int  3913 2682 29239 46990 8617 64400 367965 11876 11285 0 ...
 $ BILL_AMT2                 : int  3102 1725 14027 48233 5670 57069 412023 380 14096 0 ...
 $ BILL_AMT3                 : int  689 2682 13559 49291 35835 57608 445007 601 12108 0 ...
 $ BILL_AMT4                 : int  0 3272 14331 28314 20940 19394 542653 221 12211 0 ...
 $ BILL_AMT5                 : int  0 3455 14948 28959 19146 19619 483003 -159 11793 13007 ...
 $ BILL_AMT6                 : int  0 3261 15549 29547 19131 20024 473944 567 3719 13912 ...
 $ PAY_AMT1                  : int  0 0 1518 2000 2000 2500 55000 380 3329 0 ...
 $ PAY_AMT2                  : int  689 1000 1500 2019 36681 1815 40000 601 0 0 ...
 $ 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 ...

Predictors:

Class Frequency
factor 3
integer 20

Class imbalance: 22% / 78% (6636 / 23364)

ILPD (Indian Liver Patient Dataset)

Local directory: indian-liver

Details: link

Files:

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 ...

Predictors:

Class Frequency
factor 1
integer 4
numeric 5

Class imbalance: 29% / 71% (167 / 416)

MAGIC Gamma Telescope

Local directory: magic

Details: link

Files:

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 ...

Predictors:

Class Frequency
numeric 10

Class imbalance: 35% / 65% (6688 / 12332)

Seismic bumps

Local directory: seismic-bumps

Details: link

Files:

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 ...

Predictors:

Class Frequency
factor 4
integer 11

Class imbalance: 7% / 93% (170 / 2414)

Spambase

Local directory: spambase

Details: link

Files:

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 ...

Predictors:

Class Frequency
integer 2
numeric 55

Class imbalance: 39% / 61% (1813 / 2788)

Wine Quality

Local directory: wine-quality

Details: link

Files:

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 ...

Predictors:

Class Frequency
factor 1
numeric 11

Class imbalance: 37% / 63% (2384 / 4113)