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flowers

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#
int64
1
30
flower_diameter_cm
float64
3.4
9.9
petal_length_cm
float64
1
4.6
petal_width_cm
float64
0.5
1.8
petal_count
int64
5
30
stem_height_cm
float64
12.2
78.3
color
stringclasses
6 values
1
3.4
1.3
1
7
68.7
Red
2
5.7
3.2
0.9
5
21.2
Orange
3
6.9
3.8
0.6
17
64.2
Pink
4
5.6
1
1.5
7
73.4
Red
5
7.7
1.5
1.8
13
73.8
Pink
6
3.8
2.6
0.5
27
42.6
Red
7
5.6
3
0.6
13
78.3
White
8
9.7
4.6
0.6
21
72.6
Pink
9
4.6
1.9
0.7
8
40.5
White
10
4.4
3.4
0.9
6
40.6
Orange
11
7.8
2.8
0.7
30
25.5
Pink
12
9.1
1.5
1.2
11
42.2
Pink
13
7.7
2.4
0.8
20
53.1
Pink
14
6.1
4.4
1.4
21
27.4
Yellow
15
4.4
3
1.7
12
24.8
Orange
16
6.8
4.4
1.1
24
67
White
17
9
4.5
1.8
10
13.1
Yellow
18
5.5
2
1.1
26
43.7
Red
19
3.5
4.6
1.6
17
13.4
Purple
20
6.8
1.5
1.7
13
42.3
Pink
21
6.5
3.8
1.2
19
12.2
White
22
8
2.9
1.6
29
16.8
Purple
23
3.4
1.3
1
14
34
Orange
24
5.9
3
0.8
21
21.9
Yellow
25
4.6
1.8
1
18
59.7
Red
26
9.9
3.3
0.6
10
20.3
Pink
27
8.3
2.8
1.3
15
60.8
Purple
28
7.7
1.1
0.7
16
63.4
Pink
29
6.9
2.3
1.5
20
35.1
White
30
5.8
4.1
1.3
11
16.6
Orange

Dataset Card for Tabular Flower Data

Dataset Details and Description

This dataset consists of 30 individual flowers. For each flower, five physical features were recorded: flower_diameter_cm (the width of the bloom at its widest point), petal_length_cm (average length of a petal), petal_width_cm (average width of a petal), petal_count (number of petals on the bloom), and stem_height_cm (approximate stem height from the soil line). The target variable is the color of the pedals, assigned categorically as Red, Yellow, Orange, Purple, Pink, or White. This small, structured dataset mimics the famous Iris dataset but with local flowers.

To increase the size of the dataset and provide variety for modeling, the original 30 rows were expanded to 300 synthetic samples using a stratified bootstrap with controlled noise. Specifically, rows were sampled with replacement within each color class to maintain the original class distribution. For each numeric feature, small random perturbations (Gaussian noise scaled to 10 % of the feature’s standard deviation) were added to simulate natural measurement variability. Values were then clamped to plausible ranges derived from the original data’s 1st–99th percentiles to avoid impossible numbers (like negative lengths), and integer-like features such as petal_count were rounded back to whole numbers. Categorical features were left unchanged so that each synthetic row retained a realistic label. This procedure produced a larger, more diverse dataset that still reflects realistic physical characteristics of the observed flowers.

Dataset Intended Use

To practice AI ML

Dataset Out of Scope

To predict flower properties.

Personal and Sensitive Information

This dataset does not contain personal or sensitive information.

  • Curated by: Scotty McGee
  • Language: English
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