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

Iris Species Dataset

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 dataset is taken from UCI Machine Learning Repository's… See the full description on the dataset page: https://huggingface.co/datasets/beierr1/my_iris.

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".

The Iris dataset is a classic dataset in the field of machine learning, containing measurements of various features of iris flowers, such as sepal length, sepal width, petal length, and petal width, along with their corresponding species. Through analysis, we aim to explore the characteristics of different iris species, identify patterns in their measurements, and potentially build predictive models to classify iris species based on their features. This dataset serves as an excellent resource for understanding and practicing classification techniques in data science.

Iris Dataset

The classic Iris dataset in .parquet format. Useful for ML demos, classification tasks, and model testing.

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 ---

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.

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.

Iris Petal and Sepal Dataset Description The Iris dataset is one of the most famous datasets in the field of machine learning and statistical classification. It was first introduced by British biologist and statistician Ronald Fisher in 1936 as an example of linear discriminant analysis. The dataset is widely used for educational purposes and model building in machine learning due to its simplicity and versatility.

Dataset Overview The dataset contains 150 observations of Iris flowers from three species:

Iris Setosa Iris Versicolor Iris Virginica Each observation includes four numerical features:

Sepal Length (cm) Sepal Width (cm) Petal Length (cm) Petal Width (cm) Additionally, the dataset provides a class label for the species of the Iris flower.

Feature Descriptions: Sepal Length: The length of the flower’s sepal in centimeters. Sepal Width: The width of the flower’s sepal in centimeters. Petal Length: The length of the flower’s petal in centimeters. Petal Width: The width of the flower’s petal in centimeters. Species: The class label that classifies the flower into one of three species (Setosa, Versicolor, Virginica). Data Summary: 150 instances (50 samples per species) 4 features (numeric data) 1 target variable (categorical – species of the flower) Applications: The dataset is often used for:

Classification tasks: Building models to classify the species of Iris flowers. Exploratory data analysis (EDA): Exploring relationships between features. Data visualization: Plotting petal and sepal dimensions to understand patterns. Predictive modeling: Training and testing machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), and decision trees. Why This Dataset? The Iris dataset is ideal for beginners and experts alike, as it provides an easy introduction to supervised learning. It is perfect for understanding basic classification algorithms and exploring key concepts such as:

Multiclass classification Feature correlation Data visualization techniques This description is tailored for the Kaggle community and provides a clear overview of the dataset’s content and potential use cases. You can customize it further if needed!

it can be used in applications such as access control systems

The Land Resource Information Systems (LRIS) Portal is a repository of authoritative New Zealand science datasets and information.

It allows you to download environment data held specifically by Landcare Research for use in GIS and other applications that can handle geospatial data for mapping, querying and spatial analyses. There is strong focus on metadata and supporting documentation. The portal supports downloading data in many geospatial data formats.

LRIS Terms of Use

in centimeters. Based on the combination of these four features

IRIS tracks the status of safety deficiencies identified during Occupational Safety and Health Administration (OSHA) and Safety & Environmental Management (SEM) survey inspections.

IRIS

## Overview

IRIS is a dataset for object detection tasks - it contains Flip annotations for 347 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

The table Iris is part of the dataset Iris species, available at https://redivis.com/datasets/9qtx-4e1cf0fjf. It contains 150 rows across 6 variables.

Fisher's Iris dataset is a multivariate dataset introduced by Sir Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems". It contains 150 samples from three species of iris flowers (Iris setosa, Iris virginica, and Iris versicolor). Each sample is described by 4 features: the length and width of the sepal and petal.

Iris dataset in json format

BIT/iris-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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