Annealing
Steel annealing data
Dataset Characteristics
Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Classification
Feature Type
Categorical, Integer, Real
# Instances
798
# Features
38
Dataset Information
Additional Information
Attribute Listing: 1. family: --,GB,GK,GS,TN,ZA,ZF,ZH,ZM,ZS 2. product-type: C, H, G 3. steel: -,R,A,U,K,M,S,W,V 4. carbon: continuous 5. hardness: continuous 6. temper_rolling: -,T 7. condition: -,S,A,X 8. formability: -,1,2,3,4,5 9. strength: continuous 10. non-ageing: -,N 11. surface-finish: P,M,- 12. surface-quality: -,D,E,F,G 13. enamelability: -,1,2,3,4,5 14. bc: Y,- 15. bf: Y,- 16. bt: Y,- 17. bw/me: B,M,- 18. bl: Y,- 19. m: Y,- 20. chrom: C,- 21. phos: P,- 22. cbond: Y,- 23. marvi: Y,- 24. exptl: Y,- 25. ferro: Y,- 26. corr: Y,- 27. blue/bright/varn/clean: B,R,V,C,- 28. lustre: Y,- 29. jurofm: Y,- 30. s: Y,- 31. p: Y,- 32. shape: COIL, SHEET 33. thick: continuous 34. width: continuous 35. len: continuous 36. oil: -,Y,N 37. bore: 0000,0500,0600,0760 38. packing: -,1,2,3 classes: 1,2,3,4,5,U -- The '-' values are actually 'not_applicable' values rather than 'missing_values' (and so can be treated as legal discrete values rather than as showing the absence of a discrete value).
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| famiily | Feature | Categorical | yes | ||
| product-type | Feature | Categorical | no | ||
| steel | Feature | Categorical | yes | ||
| carbon | Feature | Integer | no | ||
| hardness | Feature | Integer | no | ||
| temper-rolling | Feature | Categorical | yes | ||
| condition | Feature | Categorical | yes | ||
| formability | Feature | Integer | yes | ||
| strength | Feature | Integer | no | ||
| non-ageing | Feature | Categorical | yes |
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Additional Variable Information
Attribute Listing: 1. family: --,GB,GK,GS,TN,ZA,ZF,ZH,ZM,ZS 2. product-type: C, H, G 3. steel: -,R,A,U,K,M,S,W,V 4. carbon: continuous 5. hardness: continuous 6. temper_rolling: -,T 7. condition: -,S,A,X 8. formability: -,1,2,3,4,5 9. strength: continuous 10. non-ageing: -,N 11. surface-finish: P,M,- 12. surface-quality: -,D,E,F,G 13. enamelability: -,1,2,3,4,5 14. bc: Y,- 15. bf: Y,- 16. bt: Y,- 17. bw/me: B,M,- 18. bl: Y,- 19. m: Y,- 20. chrom: C,- 21. phos: P,- 22. cbond: Y,- 23. marvi: Y,- 24. exptl: Y,- 25. ferro: Y,- 26. corr: Y,- 27. blue/bright/varn/clean: B,R,V,C,- 28. lustre: Y,- 29. jurofm: Y,- 30. s: Y,- 31. p: Y,- 32. shape: COIL, SHEET 33. thick: continuous 34. width: continuous 35. len: continuous 36. oil: -,Y,N 37. bore: 0000,0500,0600,0760 38. packing: -,1,2,3 classes: 1,2,3,4,5,U -- The '-' values are actually 'not_applicable' values rather than 'missing_values' (and so can be treated as legal discrete values rather than as showing the absence of a discrete value).
Dataset Files
| File | Size |
|---|---|
| anneal.data | 78.5 KB |
| anneal.test | 9.8 KB |
| anneal.names | 2.7 KB |
| Index | 147 Bytes |
Papers Citing this Dataset
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By Saptarshi Sengupta, Sanchita Basak, Richard Peters. 2018
Published in Mach. Learn. Knowl. Extr. 2018, 1(1), 157-191.
By Ireneusz Czarnowski, Piotr Jędrzejowicz. 2011
Published in Applied Mathematics and Computer Science.
0 to 3 of 3
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset annealing = fetch_ucirepo(id=3) # data (as pandas dataframes) X = annealing.data.features y = annealing.data.targets # metadata print(annealing.metadata) # variable information print(annealing.variables)
Annealing [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5RW2F.
Keywords
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.