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".

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 ---

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

Context: 🌼 The Iris flower dataset, an iconic multivariate set, was first introduced by the renowned British statistician and biologist, Ronald Fisher in 1936 📝. Commonly known as Anderson's Iris dataset, it was curated by Edgar Anderson to measure the morphologic variation of three Iris species 🌸: Iris Setosa, Iris Virginica, and Iris Versicolor.

📊 The set comprises 100 samples from each species, with four features - sepal length, sepal width, petal length, and petal width, measured in centimetres.

🔬 This dataset has since served as a standard test case for various statistical classification techniques in machine learning, including the widely used support vector machines (SVM).

So, whether you're a newbie dipping your toes into the ML pond or a seasoned data scientist testing out a new classification method, the Iris dataset is a classic starting point! 🎯🚀

Columns:

1. Sepal Length: The length of the sepal of the iris flower, is measured in centimetres.
2. Sepal Width: The width of the sepal of the iris flower, measured in centimetres.
3. Petal Length: The length of the petal of the iris flower, is measured in centimetres.
4. Petal Width: The width of the petal of the iris flower, measured in centimetres.
5. Species:The specific species of the iris flower, categorized into Sentosa, Virginica, and Versicolor.

Problem Statement:

1.🎯 Classification Challenge: Can you accurately predict the species of an Iris flower based on the four given measurements: sepal length, sepal width, petal length, and petal width?

2.💡 Feature Importance: Which feature (sepal length, sepal width, petal length, or petal width) is the most significant in distinguishing between the species of Iris flowers?

3.📈 Data Scaling: How does standardization (or normalization) of the features affect the performance of your classification models?

4.🧪 Model Experimentation: Can simpler models such as Logistic Regression perform as well as more complex models like Support Vector Machines or Neural Networks on the Iris dataset? Compare the performance of various models.

5.🤖 AutoML Challenge: Use AutoML tools (like Google's AutoML or H2O's AutoML) to build a classification model. How does its performance compare with your handcrafted models?

Kindly, upvote if you find the dataset interesting

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. {"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. "Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Fisher, R. A. 1936. "The Use of Multiple Measurements in Taxonomic Problems." Annals of Eugenics 7 (2): 179\u201388. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x."]}

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 in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. Please use this data set to clustering the iris flowers data. You can use k-means clustering algorithm.

Dataset Card for Iris Dataset

  Dataset Summary

The Iris dataset is a classic multivariate dataset introduced by Ronald Fisher in 1936. It contains 150 samples of iris flowers from three different species: Iris setosa, Iris versicolor, and Iris virginica. Each sample has four features: sepal length, sepal width, petal length, and petal width. This dataset is widely used for classification tasks, especially in machine learning tutorials and benchmarks.

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/mariaasoriaano/iris_clase.