Datasets:
Id
int64 1
150
| SepalLengthCm
float64 4.3
7.9
| SepalWidthCm
float64 2
4.4
| PetalLengthCm
float64 1
6.9
| PetalWidthCm
float64 0.1
2.5
| Species
stringclasses 3
values |
|---|---|---|---|---|---|
1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
2 | 4.9 | 3 | 1.4 | 0.2 | Iris-setosa |
3 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |
4 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa |
5 | 5 | 3.6 | 1.4 | 0.2 | Iris-setosa |
6 | 5.4 | 3.9 | 1.7 | 0.4 | Iris-setosa |
7 | 4.6 | 3.4 | 1.4 | 0.3 | Iris-setosa |
8 | 5 | 3.4 | 1.5 | 0.2 | Iris-setosa |
9 | 4.4 | 2.9 | 1.4 | 0.2 | Iris-setosa |
10 | 4.9 | 3.1 | 1.5 | 0.1 | Iris-setosa |
11 | 5.4 | 3.7 | 1.5 | 0.2 | Iris-setosa |
12 | 4.8 | 3.4 | 1.6 | 0.2 | Iris-setosa |
13 | 4.8 | 3 | 1.4 | 0.1 | Iris-setosa |
14 | 4.3 | 3 | 1.1 | 0.1 | Iris-setosa |
15 | 5.8 | 4 | 1.2 | 0.2 | Iris-setosa |
16 | 5.7 | 4.4 | 1.5 | 0.4 | Iris-setosa |
17 | 5.4 | 3.9 | 1.3 | 0.4 | Iris-setosa |
18 | 5.1 | 3.5 | 1.4 | 0.3 | Iris-setosa |
19 | 5.7 | 3.8 | 1.7 | 0.3 | Iris-setosa |
20 | 5.1 | 3.8 | 1.5 | 0.3 | Iris-setosa |
21 | 5.4 | 3.4 | 1.7 | 0.2 | Iris-setosa |
22 | 5.1 | 3.7 | 1.5 | 0.4 | Iris-setosa |
23 | 4.6 | 3.6 | 1 | 0.2 | Iris-setosa |
24 | 5.1 | 3.3 | 1.7 | 0.5 | Iris-setosa |
25 | 4.8 | 3.4 | 1.9 | 0.2 | Iris-setosa |
26 | 5 | 3 | 1.6 | 0.2 | Iris-setosa |
27 | 5 | 3.4 | 1.6 | 0.4 | Iris-setosa |
28 | 5.2 | 3.5 | 1.5 | 0.2 | Iris-setosa |
29 | 5.2 | 3.4 | 1.4 | 0.2 | Iris-setosa |
30 | 4.7 | 3.2 | 1.6 | 0.2 | Iris-setosa |
31 | 4.8 | 3.1 | 1.6 | 0.2 | Iris-setosa |
32 | 5.4 | 3.4 | 1.5 | 0.4 | Iris-setosa |
33 | 5.2 | 4.1 | 1.5 | 0.1 | Iris-setosa |
34 | 5.5 | 4.2 | 1.4 | 0.2 | Iris-setosa |
35 | 4.9 | 3.1 | 1.5 | 0.1 | Iris-setosa |
36 | 5 | 3.2 | 1.2 | 0.2 | Iris-setosa |
37 | 5.5 | 3.5 | 1.3 | 0.2 | Iris-setosa |
38 | 4.9 | 3.1 | 1.5 | 0.1 | Iris-setosa |
39 | 4.4 | 3 | 1.3 | 0.2 | Iris-setosa |
40 | 5.1 | 3.4 | 1.5 | 0.2 | Iris-setosa |
41 | 5 | 3.5 | 1.3 | 0.3 | Iris-setosa |
42 | 4.5 | 2.3 | 1.3 | 0.3 | Iris-setosa |
43 | 4.4 | 3.2 | 1.3 | 0.2 | Iris-setosa |
44 | 5 | 3.5 | 1.6 | 0.6 | Iris-setosa |
45 | 5.1 | 3.8 | 1.9 | 0.4 | Iris-setosa |
46 | 4.8 | 3 | 1.4 | 0.3 | Iris-setosa |
47 | 5.1 | 3.8 | 1.6 | 0.2 | Iris-setosa |
48 | 4.6 | 3.2 | 1.4 | 0.2 | Iris-setosa |
49 | 5.3 | 3.7 | 1.5 | 0.2 | Iris-setosa |
50 | 5 | 3.3 | 1.4 | 0.2 | Iris-setosa |
51 | 7 | 3.2 | 4.7 | 1.4 | Iris-versicolor |
52 | 6.4 | 3.2 | 4.5 | 1.5 | Iris-versicolor |
53 | 6.9 | 3.1 | 4.9 | 1.5 | Iris-versicolor |
54 | 5.5 | 2.3 | 4 | 1.3 | Iris-versicolor |
55 | 6.5 | 2.8 | 4.6 | 1.5 | Iris-versicolor |
56 | 5.7 | 2.8 | 4.5 | 1.3 | Iris-versicolor |
57 | 6.3 | 3.3 | 4.7 | 1.6 | Iris-versicolor |
58 | 4.9 | 2.4 | 3.3 | 1 | Iris-versicolor |
59 | 6.6 | 2.9 | 4.6 | 1.3 | Iris-versicolor |
60 | 5.2 | 2.7 | 3.9 | 1.4 | Iris-versicolor |
61 | 5 | 2 | 3.5 | 1 | Iris-versicolor |
62 | 5.9 | 3 | 4.2 | 1.5 | Iris-versicolor |
63 | 6 | 2.2 | 4 | 1 | Iris-versicolor |
64 | 6.1 | 2.9 | 4.7 | 1.4 | Iris-versicolor |
65 | 5.6 | 2.9 | 3.6 | 1.3 | Iris-versicolor |
66 | 6.7 | 3.1 | 4.4 | 1.4 | Iris-versicolor |
67 | 5.6 | 3 | 4.5 | 1.5 | Iris-versicolor |
68 | 5.8 | 2.7 | 4.1 | 1 | Iris-versicolor |
69 | 6.2 | 2.2 | 4.5 | 1.5 | Iris-versicolor |
70 | 5.6 | 2.5 | 3.9 | 1.1 | Iris-versicolor |
71 | 5.9 | 3.2 | 4.8 | 1.8 | Iris-versicolor |
72 | 6.1 | 2.8 | 4 | 1.3 | Iris-versicolor |
73 | 6.3 | 2.5 | 4.9 | 1.5 | Iris-versicolor |
74 | 6.1 | 2.8 | 4.7 | 1.2 | Iris-versicolor |
75 | 6.4 | 2.9 | 4.3 | 1.3 | Iris-versicolor |
76 | 6.6 | 3 | 4.4 | 1.4 | Iris-versicolor |
77 | 6.8 | 2.8 | 4.8 | 1.4 | Iris-versicolor |
78 | 6.7 | 3 | 5 | 1.7 | Iris-versicolor |
79 | 6 | 2.9 | 4.5 | 1.5 | Iris-versicolor |
80 | 5.7 | 2.6 | 3.5 | 1 | Iris-versicolor |
81 | 5.5 | 2.4 | 3.8 | 1.1 | Iris-versicolor |
82 | 5.5 | 2.4 | 3.7 | 1 | Iris-versicolor |
83 | 5.8 | 2.7 | 3.9 | 1.2 | Iris-versicolor |
84 | 6 | 2.7 | 5.1 | 1.6 | Iris-versicolor |
85 | 5.4 | 3 | 4.5 | 1.5 | Iris-versicolor |
86 | 6 | 3.4 | 4.5 | 1.6 | Iris-versicolor |
87 | 6.7 | 3.1 | 4.7 | 1.5 | Iris-versicolor |
88 | 6.3 | 2.3 | 4.4 | 1.3 | Iris-versicolor |
89 | 5.6 | 3 | 4.1 | 1.3 | Iris-versicolor |
90 | 5.5 | 2.5 | 4 | 1.3 | Iris-versicolor |
91 | 5.5 | 2.6 | 4.4 | 1.2 | Iris-versicolor |
92 | 6.1 | 3 | 4.6 | 1.4 | Iris-versicolor |
93 | 5.8 | 2.6 | 4 | 1.2 | Iris-versicolor |
94 | 5 | 2.3 | 3.3 | 1 | Iris-versicolor |
95 | 5.6 | 2.7 | 4.2 | 1.3 | Iris-versicolor |
96 | 5.7 | 3 | 4.2 | 1.2 | Iris-versicolor |
97 | 5.7 | 2.9 | 4.2 | 1.3 | Iris-versicolor |
98 | 6.2 | 2.9 | 4.3 | 1.3 | Iris-versicolor |
99 | 5.1 | 2.5 | 3 | 1.1 | Iris-versicolor |
100 | 5.7 | 2.8 | 4.1 | 1.3 | Iris-versicolor |
Iris Dataset Card
Dataset Overview
The Iris dataset is a classic dataset widely used in the fields of machine learning and statistics. Originally introduced by Ronald A. Fisher in 1936, it serves as a benchmark for classification algorithms. The dataset contains measurements of iris flowers from three different species: Iris setosa, Iris versicolor, and Iris virginica.
Key Characteristics
- Number of Instances: 150
- Number of Features: 4
- Classes: 3 (50 instances each)
- Feature Types: Continuous
- Missing Values: No
Features
| Feature Name | Type | Description | Units |
|---|---|---|---|
| Sepal Length | Continuous | Length of the sepal | cm |
| Sepal Width | Continuous | Width of the sepal | cm |
| Petal Length | Continuous | Length of the petal | cm |
| Petal Width | Continuous | Width of the petal | cm |
Target Variable
The target variable is categorical, representing the species of the iris flower:
- 0: Iris setosa
- 1: Iris versicolor
- 2: Iris virginica
Data Format
The dataset is structured in a tabular format where each row corresponds to an individual flower, and each column represents a specific attribute. The species classification is typically included as an additional column.
Usage
The Iris dataset is ideal for:
- Classification tasks: It allows practitioners to apply various classification algorithms, such as Support Vector Machines (SVM), Random Forest, and Gradient Boosting.
- Data visualization: The simplicity of the dataset enables effective visualizations, such as scatter plots and pair plots, to understand feature distributions and relationships.
- Educational purposes: It serves as an excellent introductory dataset for those new to data analysis and machine learning.
Loading the Dataset
The dataset can be easily loaded using libraries like scikit-learn in Python:
from sklearn import datasets
iris = datasets.load_iris()
This command retrieves the dataset as a structured object containing features, target values, and metadata.
Historical Context
The dataset was collected by Edgar Anderson and was used by Fisher to demonstrate linear discriminant analysis. It has since become one of the most referenced datasets in machine learning literature due to its simplicity and effectiveness in illustrating fundamental concepts in classification.
Conclusion
The Iris dataset remains a foundational resource for both beginners and experienced practitioners in data science and machine learning, offering a straightforward yet rich context for exploring classification techniques and model evaluation.
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