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CulmenLength
float64
33.5
55.9
CulmenDepth
float64
13.1
21.5
FlipperLength
float64
172
230
BodyMass
float64
2.85k
6k
Species
int64
0
2
40.9
16.8
191
3,700
0
42.2
18.5
180
3,550
0
45.8
18.9
197
4,150
0
40.2
20.1
200
3,975
0
35.9
16.6
190
3,050
0
42.8
18.5
195
4,250
0
41.3
21.1
195
4,400
0
38.1
18.6
190
3,700
0
41.1
18.6
189
3,325
0
44.1
19.7
196
4,400
0
40.2
17
176
3,450
0
41.5
18.5
201
4,000
0
42.1
19.1
195
4,000
0
42.5
20.7
197
4,500
0
40.6
19
199
4,000
0
40.9
18.9
184
3,900
0
37
16.5
185
3,400
0
40.6
18.6
183
3,550
0
41.3
20.3
194
3,550
0
42
19.5
200
4,050
0
39
17.5
186
3,550
0
37
16.9
185
3,000
0
37.2
19.4
184
3,900
0
36.4
17.1
184
2,850
0
35.5
16.2
195
3,350
0
35.9
19.2
189
3,800
0
44.1
18
210
4,000
0
37.7
16
183
3,075
0
36.8
18.5
193
3,500
0
37.5
18.9
179
2,975
0
43.1
19.2
197
3,500
0
40.3
18
195
3,250
0
38.2
20
190
3,900
0
39.5
17.4
186
3,800
0
36.9
18.6
189
3,500
0
35
17.9
190
3,450
0
37.9
18.6
172
3,150
0
38.6
17.2
199
3,750
0
36
18.5
186
3,100
0
37.7
18.7
180
3,600
0
41
20
203
4,725
0
36.7
19.3
193
3,450
0
34.1
18.1
193
3,475
0
36
17.1
187
3,700
0
39.5
17.8
188
3,300
0
36
17.8
195
3,450
0
38.1
17
181
3,175
0
38.8
17.6
191
3,275
0
35.7
16.9
185
3,150
0
41.4
18.5
202
3,875
0
37.8
17.1
186
3,300
0
40.6
17.2
187
3,475
0
36
17.9
190
3,450
0
37.8
17.3
180
3,700
0
36.6
17.8
185
3,700
0
39.3
20.6
190
3,650
0
35.2
15.9
186
3,050
0
41.1
18.2
192
4,050
0
37.3
16.8
192
3,000
0
42.7
18.3
196
4,075
0
39.6
17.7
186
3,500
0
37.8
18.1
193
3,750
0
34
17.1
185
3,400
0
36.4
17
195
3,325
0
38.6
21.2
191
3,800
0
37.5
18.5
199
4,475
0
38.1
17.6
187
3,425
0
43.2
18.5
192
4,100
0
37.9
18.6
193
2,925
0
36.2
17.2
187
3,150
0
37.6
17
185
3,600
0
36.3
19.5
190
3,800
0
35.1
19.4
193
4,200
0
34.6
21.1
198
4,400
0
40.8
18.4
195
3,900
0
37.8
18.3
174
3,400
0
41.1
18.1
205
4,300
0
40.6
18.8
193
3,800
0
39
17.1
191
3,050
0
38.3
19.2
189
3,950
0
33.5
19
190
3,600
0
37.8
20
190
4,250
0
36.7
18.8
187
3,800
0
41.1
19.1
188
4,100
0
39.6
17.2
196
3,550
0
41.1
17.6
182
3,200
0
38.1
16.5
198
3,825
0
39.2
19.6
195
4,675
0
39.7
18.4
190
3,900
0
41.4
18.6
191
3,700
0
37.2
18.1
178
3,900
0
38.7
19
195
3,450
0
null
null
null
null
0
35.7
18
202
3,550
0
37.6
19.3
181
3,300
0
35.3
18.9
187
3,800
0
39
18.7
185
3,650
0
38.6
17
188
2,900
0
46
21.5
194
4,200
0
39.2
21.1
196
4,150
0
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Penguin Species Classification

Dataset Summary

Penguin Species Classification is a small tabular dataset for predicting which of three penguin species an observation belongs to based on physical measurements. The target label is encoded as Species, where 0 = Adelie, 1 = Gentoo, and 2 = Chinstrap.

This uploaded version preserves the original rows, including missing feature values, so users can decide how they want to clean and prepare the data for their own modeling workflows.

Dataset Structure

  • train: 274 rows
  • test: 70 rows
  • 2 rows contain missing measurement values and were intentionally retained

Label Mapping

Label Species
0 Adelie
1 Gentoo
2 Chinstrap

Features

Column Type Description
CulmenLength float Culmen length measurement.
CulmenDepth float Culmen depth measurement.
FlipperLength integer Flipper length measurement.
BodyMass integer Body mass measurement.
Species integer label Target class for penguin species.

Dataset Dictionary

Field Role Notes
CulmenLength feature Numeric morphological feature used for classification.
CulmenDepth feature Numeric morphological feature used for classification.
FlipperLength feature Numeric morphological feature used for classification.
BodyMass feature Numeric morphological feature used for classification.
Species target Encoded multiclass target with three species labels.

Split Details

The train/test split was created using a reproducible stratified random split with a fixed seed so class balance is preserved across both splits.

Split Label 0 Label 1 Label 2 Total
train 120 98 54 274
test 31 25 14 70

First 5 Rows

CulmenLength CulmenDepth FlipperLength BodyMass Species
39.1 18.7 181 3750 0
39.5 17.4 186 3800 0
40.3 18.0 195 3250 0
0
36.7 19.3 193 3450 0

Intended Use

This dataset is suitable for:

  • multiclass classification practice
  • introductory tabular machine learning workflows
  • feature scaling and model comparison exercises
  • teaching supervised learning concepts

Limitations

  • This is a small dataset intended for experimentation and learning.
  • The source file provided did not include provenance or license metadata, so those should be added if you want a more complete Hub listing.
  • Users need to handle missing values themselves before training models that require complete inputs.
  • Performance estimates can vary because the classes are not perfectly balanced.
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