The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.This dataset became a typical test case for many statistical classification techniques in machine learning such as support vector machines

Analysis of ‘Iris Flower Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arshid/iris-flower-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.

This dataset became a typical test case for many statistical classification techniques in machine learning such as support vector machines

Content

The dataset contains a set of 150 records under 5 attributes - Petal Length, Petal Width, Sepal Length, Sepal width and Class(Species).

Acknowledgements

This dataset is free and is publicly available at the UCI Machine Learning Repository

--- Original source retains full ownership of the source dataset ---

It includes three iris species with 50 samples each as well as some properties of each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

FIle name: iris.csv

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus".

Iris Species Dataset

The Iris dataset is a classic dataset in machine learning, originally published by Ronald Fisher. It contains 150 instances of iris flowers, each described by four features (sepal length, sepal width, petal length, and petal width), along with the corresponding species label (setosa, versicolor, or virginica). It is commonly used as an introductory dataset for classification tasks and for demonstrating basic data exploration and model training workflows.… See the full description on the dataset page: https://huggingface.co/datasets/brjapon/iris.

Iris Flower Dataset

This is a classic and very widely used dataset in machine learning and statistics, often serving as a first dataset for classification problems. Introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems," it is a foundational resource for learning classification algorithms.

Overview:

The dataset contains measurements for 150 samples of iris flowers. Each sample belongs to one of three species of iris:

• Iris setosa
• Iris versicolor
• Iris virginica

For each flower, four features were measured:

• Sepal length (in cm)
• Sepal width (in cm)
• Petal length (in cm)
• Petal width (in cm)

The goal is typically to build a model that can classify iris flowers into their correct species based on these four features.

File Structure:

The dataset is usually provided as a single CSV (Comma Separated Values) file, often named iris.csv or similar. This file typically contains the following columns:

1. sepal_length (cm): Numerical. The length of the sepal of the iris flower.
2. sepal_width (cm): Numerical. The width of the sepal of the iris flower.
3. petal_length (cm): Numerical. The length of the petal of the iris flower.
4. petal_width (cm): Numerical. The width of the petal of the iris flower.
5. species: Categorical. The species of the iris flower (either 'setosa', 'versicolor', or 'virginica'). This is the target variable for classification.

Content of the Data:

The dataset contains an equal number of samples (50) for each of the three iris species. The measurements of the sepal and petal dimensions vary between the species, allowing for their differentiation using machine learning models.

How to Use This Dataset:

1. Download the iris.csv file.
2. Load the data using libraries like Pandas in Python.
3. Explore the data through visualization and statistical analysis to understand the relationships between the features and the different species.
4. Build classification models (e.g., Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors) using the sepal and petal measurements as features and the 'species' column as the target variable.
5. Evaluate the performance of your model using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
6. The dataset is small and well-behaved, making it excellent for learning and experimenting with various classification techniques.

Citation:

When using the Iris dataset, it is common to cite Ronald Fisher's original work:

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188.

Data Contribution:

Thank you for providing this classic and fundamental dataset to the Kaggle community. The Iris dataset remains an invaluable resource for both beginners learning the basics of classification and experienced practitioners testing new algorithms. Its simplicity and clear class separation make it an ideal starting point for many data science projects.

If you find this dataset description helpful and the dataset itself useful for your learning or projects, please consider giving it an upvote after downloading. Your appreciation is valuable!

1. Title: Iris Plants Database

2. Sources: (a) Creator: R.A. Fisher (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) (c) Date: July, 1988

3. Past Usage:

   • Publications: too many to mention!!! Here are a few.
   • Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950).
   • Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
   • Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. -- Results: -- very low misclassification rates (0% for the setosa class)
   • Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. -- Results: -- very low misclassification rates again
   • See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II conceptual clustering system finds 3 classes in the data.

4. Relevant Information: --- This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. --- Predicted attribute: class of iris plant. --- This is an exceedingly simple domain. --- This data differs from the data presented in Fishers article

5. Number of Instances: 150 (50 in each of three classes)

6. Number of Attributes: 4 numeric, predictive attributes and the class

7. Attribute Information:

   1. sepal length in cm
   2. sepal width in cm
   3. petal length in cm
   4. petal width in cm
   5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

8. Missing Attribute Values: None

Summary Statistics:

sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)

1. Class Distribution: 33.3% for each of 3 classes.

The Iris flower data set or Fisher’s Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. The dataset was introduced in his 1936 paper "The Use of Multiple Measurements in Taxonomic Problems" (Fisher 1936) as an example of linear discriminant analysis.

This dataset has the following Features:

Petal.Length: Length of the petal

Petal.Width: Width of the petal

Sepal.Length: Length of the sepal

Sepal.Width: Width of the sepal

It has a total of 3 Groups: setosa, versicolor, and virginica.

The Iris Dataset. ¶. This data sets consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray. The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width.

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

The columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

The columns in this dataset are:

Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species

New : Extra Data Generated With CTGAN

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from H. J. Andrews Experimental Forest (AND) contains plant cover of Iris measurements in percent units and were aggregated to a yearly timescale.

Additional file 3.

Iris Ceramica is planning to develop a porcelain ceramic production plant with production capacity of 3 million m2 per annum of ceramics at Stupino Industrial District in Stupino, Moscow Oblast, Russia.The project involves the construction of a manufacturing unit, a processing unit, a quality check unit, warehouses, storage facilities, packaging units, parking facilities, the installation of machinery, safety and security systems.Stakeholder Information:Planning Authority: The Government of Moscow RegionAssociated Developer: Stupino Industrial District Read More

Iris calcicola (Iridaceae), a new species from limestone areas of northern Guangxi, China based on morphological and molecular evidence Iris calcicola Z.C.Lu, Z.P.Huang & Yan Liu, a new species of Iris sect. Lophiris was found from limestone areas of Guangxi, China. Iris calcicola is similar to Iris japonica Thunb., but differs by its inflorescence simple; flowering stems ascendent, with 2–5 branches; spathes 2, narrowly lanceolate, 2–3.8 cm long, 1–2 (3)-flowered, apex acuminate; flower segments obliquely ascending, not spreading when blooming; pedicel enveloped by spathes or subequal to spathes; outer segments elliptic, with prominent, irregular, yellow crest.

Here's a brief version of what you'll find in the data description file.

Source: Creator: R.A. Fisher Donor: Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov)

Data Set Information:

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. Predicted attribute: class of iris plant. This is an exceedingly simple domain. This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.

Attribute Information:

1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

Consistency of variables for the dataset Iris Plant.

\[\color{#ff35fe}{\mathbb{History}}\]

I made the database from the fragments of my own photos of flowers. The images are selected to reflect the flowering features of these plant species.

\[\color{#ff35fe}{\mathbb{Content}}\]

The first variant

The content is very simple:
- 210 images (128x128x3) with 10 species of flowering plants; - the file with labels flower-labels.csv. Photo files are in the .png format and the labels are integers.

\(\color{#ff35fe}{\mathbb{Labels \implies Names}}\) 0 => phlox; 1 => rose; 2 => calendula; 3 => iris; 4 => leucanthemum maximum (Shasta daisy); 5 => campanula (bellflower); 6 => viola; 7 => rudbeckia laciniata (Goldquelle); 8 => peony; 9 => aquilegia.

Additional files

flowers.zip is an extended version. Now, this dataset is in progress. \(\color{#ff35fe}{\mathbb{Labels \implies Names}}\) 0 => phlox; 1 => rose; 2 => calendula; 3 => iris; 4 => leucanthemum maximum (Shasta daisy); 5 => campanula (bellflower); 6 => viola; 7 => rudbeckia laciniata (Goldquelle); 8 => peony; 9 => aquilegia; 10=> rhododendron ; 11 => passiflora; 12 => tulip; 13 => water lily; 14 => lilium; 15 => veronica chamaedrys; 16 => cosmos; 17 => aster annual; 18 => aster perennial; 19 => snowdrop.

\[\color{#ff35fe}{\mathbb{Acknowledgments}}\]

As an owner of this database, I have published it for absolutely free usage by any site visitor.

\[\color{#ff35fe}{\mathbb{Usage}}\]

Accurate classification of plant species with a small number of images isn't a trivial task. I hope this set can be interesting for training skills in this field. A wide spectrum of algorithms can be used for classification.