rlogh/cheese-texture-autogluon-classifier
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name
string | fat
float64 | texture
string | origin
string | holed
int64 | price
float64 | protein
float64 |
|---|---|---|---|---|---|---|
feta
| 21.3 |
soft
|
Greece
| 0 | 3.75 | 14.2 |
cheddar
| 33.1 |
hard
|
USA
| 0 | 1.33 | 24.9 |
mozzarella
| 22.4 |
semi-soft
|
Italy
| 0 | 1.98 | 22.2 |
swiss
| 27.8 |
hard
|
Switzerland
| 1 | 2.2 | 26.9 |
cottage
| 4.3 |
soft
|
USA
| 0 | 1.31 | 11.1 |
brie
| 27.7 |
soft
|
France
| 0 | 4.63 | 20.8 |
gouda
| 27.4 |
semi-hard
|
Netherlands
| 0 | 2.86 | 25 |
parmesan
| 29 |
hard
|
Italy
| 0 | 5.73 | 28.4 |
blue cheese
| 28.7 |
semi-soft
|
France
| 0 | 3.53 | 21.4 |
camembert
| 24.3 |
soft
|
France
| 0 | 4.63 | 19.8 |
chevre
| 21 |
soft
|
France
| 0 | 4.63 | 18 |
provolone
| 27 |
semi-hard
|
Italy
| 0 | 1.98 | 25 |
monterey jack
| 27.8 |
semi-hard
|
USA
| 0 | 2.53 | 24 |
colby
| 32 |
semi-hard
|
USA
| 0 | 2.2 | 23.8 |
havarti
| 28 |
semi-soft
|
Denmark
| 0 | 2.2 | 25 |
gruyere
| 32 |
hard
|
Switzerland
| 0 | 6.61 | 29 |
ricotta (whole milk)
| 13 |
soft
|
Italy
| 0 | 1.21 | 11 |
paneer
| 25 |
soft
|
India
| 0 | 1.76 | 18 |
halloumi
| 26 |
semi-hard
|
Cyprus
| 0 | 3.3 | 22 |
manchego
| 30 |
hard
|
Spain
| 0 | 5.84 | 25 |
queso fresco
| 21 |
soft
|
Mexico
| 0 | 3.3 | 18 |
edam
| 25 |
semi-hard
|
Netherlands
| 0 | 2.86 | 25 |
asiago
| 27 |
hard
|
Italy
| 0 | 2.75 | 27 |
romano
| 27.9 |
hard
|
Italy
| 0 | 3.53 | 28 |
jarlsberg
| 27 |
semi-hard
|
Norway
| 1 | 2.31 | 27 |
taleggio
| 27 |
semi-soft
|
Italy
| 0 | 4.63 | 21 |
pecorino romano
| 29 |
hard
|
Italy
| 0 | 5.07 | 28 |
mascarpone
| 42 |
soft
|
Italy
| 0 | 1.76 | 4 |
american processed
| 26 |
semi-soft
|
USA
| 0 | 1.38 | 16 |
cream cheese
| 34 |
soft
|
USA
| 0 | 0.78 | 6 |
Dataset Card for Dataset Name
It is a tabular dataset describing cheese and their properties.
Dataset Details
There are two splits. The split named "original" are real, and the split named "augmented" are generated using the real to expand the dataset.
Dataset Description
- Curated by: Aslan Noorghasemi
- Language(s) (NLP): English
- License: MIT
Uses
Best for AI/ML practicing.
Out-of-Scope Use
It can be used to predict cheese properties.
Dataset Structure
It has two splits and each split has cheese name and its properties. The numerical values are for 100 grams of cheese.
Dataset Creation
Curation Rationale
To practice model training.
Source Data
It has been sourced from open-source resources on the internet.
Data Collection and Processing
All numerical values are for 100 grams of cheese.
Personal and Sensitive Information
It doesn't include any personal and sensetive information.
Bias, Risks, and Limitations
The information may not be updated.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Dataset Card Contact
Contact me: [email protected]
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
14.3 kB
Number of rows:
330
