Flags
Donated on 5/14/1990
From Collins Gem Guide to Flags, 1986
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Categorical, Integer
# Instances
194
# Features
30
Dataset Information
Additional Information
This data file contains details of various nations and their flags. In this file the fields are separated by spaces (not commas). With this data you can try things like predicting the religion of a country from its size and the colours in its flag. 10 attributes are numeric-valued. The remainder are either Boolean- or nominal-valued.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| name | Feature | Categorical | Other | no | ||
| landmass | Feature | Integer | no | |||
| zone | Feature | Integer | no | |||
| area | Feature | Integer | no | |||
| population | Feature | Integer | no | |||
| language | Feature | Integer | Other | no | ||
| religion | Feature | Integer | Other | no | ||
| bars | Feature | Integer | no | |||
| stripes | Feature | Integer | no | |||
| colours | Feature | Integer | no |
0 to 10 of 30
Additional Variable Information
1. name: Name of the country concerned 2. landmass: 1=N.America, 2=S.America, 3=Europe, 4=Africa, 4=Asia, 6=Oceania 3. zone: Geographic quadrant, based on Greenwich and the Equator; 1=NE, 2=SE, 3=SW, 4=NW 4. area: in thousands of square km 5. population: in round millions 6. language: 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others 7. religion: 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others 8. bars: Number of vertical bars in the flag 9. stripes: Number of horizontal stripes in the flag 10. colours: Number of different colours in the flag 11. red: 0 if red absent, 1 if red present in the flag 12. green: same for green 13. blue: same for blue 14. gold: same for gold (also yellow) 15. white: same for white 16. black: same for black 17. orange: same for orange (also brown) 18. mainhue: predominant colour in the flag (tie-breaks decided by taking the topmost hue, if that fails then the most central hue, and if that fails the leftmost hue) 19. circles: Number of circles in the flag 20. crosses: Number of (upright) crosses 21. saltires: Number of diagonal crosses 22. quarters: Number of quartered sections 23. sunstars: Number of sun or star symbols 24. crescent: 1 if a crescent moon symbol present, else 0 25. triangle: 1 if any triangles present, 0 otherwise 26. icon: 1 if an inanimate image present (e.g., a boat), otherwise 0 27. animate: 1 if an animate image (e.g., an eagle, a tree, a human hand) present, 0 otherwise 28. text: 1 if any letters or writing on the flag (e.g., a motto or slogan), 0 otherwise 29. topleft: colour in the top-left corner (moving right to decide tie-breaks) 30. botright: Colour in the bottom-left corner (moving left to decide tie-breaks)
Dataset Files
| File | Size |
|---|---|
| flag.data | 14.9 KB |
| flag.names | 2.9 KB |
| Index | 106 Bytes |
Papers Citing this Dataset
Sort by Year, desc
By Kang Deng, Osmar Zaïane. 2010
Published in DaWak.
By Kang Deng, Osmar Zaïane. 2009
Published in In Discovery Science.
By Djamel Zighed, Gilbert Ritschard, Walid Erray, Vasile-Marian Scuturici. 2005
Published in Int. J. Intell. Syst..
By Djamel Zighed, Gilbert Ritschard, Walid Erray, Vasile-Marian Scuturici. 2003
Published in PKDD.
0 to 4 of 4
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset flags = fetch_ucirepo(id=40) # data (as pandas dataframes) X = flags.data.features y = flags.data.targets # metadata print(flags.metadata) # variable information print(flags.variables)
Flags [Dataset]. (1990). UCI Machine Learning Repository. https://doi.org/10.24432/C52C7Z.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.