#Table of Contents
Source: http://lib.stat.cmu.edu/S/Harrell/data/descriptions/titanic.html
- sample size: 1313
- features: 10
| Name | Levels | Storage | NAs |
|---|---|---|---|
| pclass | 3 | integer | 0 |
| survived | double | 0 | |
| name | character | 0 | |
| age | double | 680 | |
| embarked | 3 | integer | 492 |
| home.dest | 371 | integer | 559 |
| room | character | 0 | |
| ticket | character | 0 | |
| boat | 99 | integer | 966 |
| sex | 2 | integer | 0 |
These data were obtained from Robert Dawson, Saint Mary's University, E-mail. The variables are pclass, age, sex, survived. These data frames are useful for demonstrating many of the functions in Hmisc as well as demonstrating binary logistic regression analysis using the Design library. For more details and references see Simonoff, Jeffrey S (1997): The "unusual episode" and a second statistics course. J Statistics Education, Vol. 5 No. 1.
Source: https://archive.ics.uci.edu/ml/datasets/Wine
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
| wine.csv.csv | |
|---|---|
| Samples | 178 |
| Features | 13 |
| Classes | 3 |
| Data Set Characteristics: | Multivariate |
| Attribute Characteristics: | Integer, Real |
| Associated Tasks: | Classification |
| Missing Values | None |
| column | attribute |
|---|---|
| 1) | Class Label |
| 2) | Alcohol |
| 3) | Malic acid |
| 4) | Ash |
| 5) | Alcalinity of ash |
| 6) | Magnesium |
| 7) | Total phenols |
| 8) | Flavanoids |
| 9) | Nonflavanoid phenols |
| 10) | Proanthocyanins |
| 11) | intensity |
| 12) | Hue |
| 13) | OD280/OD315 of diluted wines |
| 14) | Proline |
| class | samples |
|---|---|
| 1 | 59 |
| 2 | 71 |
| 3 | 48 |
Original Owners:
Forina, M. et al, PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.
Source:https://archive.ics.uci.edu/ml/datasets/Iris
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
| iris.csv | |
|---|---|
| Samples | 150 |
| Features | 4 |
| Classes | 3 |
| Data Set Characteristics: | Multivariate |
| Attribute Characteristics: | Real |
| Associated Tasks: | Classification |
| Missing Values | None |
| column | attribute |
|---|---|
| 1) | sepal length in cm |
| 2) | sepal width in cm |
| 3) | petal length in cm |
| 4) | petal width in cm |
| 5) | class label |
| class | samples |
|---|---|
| Iris-setosa | 50 |
| Iris-versicolor | 50 |
| Iris-virginica | 50 |
Creator: R.A. Fisher (1936)
The datasets iris_test40.csv and iris_train60.csv are randomly sampled from the original iris.csv dataset where iris_test40.csv contains 40% of the original samples and the remaining 60% are contained in iris_train60.csv. The class labels in the 5th column where replaced by integers 1-4, where
- 1 = Iris-setosa
- 2 = Iris-versicolor
- 3 = Iris-virginica
| class | # of samples in test set | # of samples in training set | total |-------|------|----------| | Iris-setosa | 21 | 29 | 50 | | Iris-versicolor | 22 | 28 | 50 | | Iris-virginica | 17 | 33 | 50 | | total | 60 | 90 | 150|
A public dataset of 5572 SMS messages that are labeled as either "spam" or "ham" (not spam).
| 0 | 1 | |
|---|---|---|
| ... | ... | ... |
| 5567 | spam | This is the 2nd time we have tried 2 contact u... |
| 5568 | ham | Will ü b going to esplanade fr home? |
| 5569 | ham | Pity, * was in mood for that. So...any other s... |
| 5570 | ham | The guy did some bitching but I acted like i'd... |
| 5571 | ham | Rofl. Its true to its name |
Source: http://www.dt.fee.unicamp.br/%7Etiago/smsspamcollection/
Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. 2011. “Contributions to the Study of SMS Spam Filtering: New Collection and Results.” In Proceedings of the 11th ACM Symposium on Document Engineering, 259–62. DocEng ’11. New York, NY, USA: ACM. doi:10.1145/2034691.2034742.