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

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/scikit-learn/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".

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!

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.

Partitioned IRIS Datasets

This repository contains a script (dataset.py) to download the Iris dataset and split it into multiple partitions. Each partition is further divided into a public "mock" dataset and a "private" dataset.

  IRIS Dataset Overview

The Iris dataset is a classic dataset in machine learning, consisting of 150 samples of iris flowers. Each sample has four features (sepal length, sepal width, petal length, and petal width) and belongs to one of three… See the full description on the dataset page: https://huggingface.co/datasets/khoaguin/iris-partitions.

Iris

## Overview

Iris is a dataset for semantic segmentation tasks - it contains Iris annotations for 201 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.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Generalisation of the limits provided by INSEE for the return of statistical data on an infra-communal scale, the Francisian extraction of the IRIS perimeters (from the product Contours...Iris® distributed by the IGN) covers all the municipalities of Ile-de-France.

Infra-communal cutting into IRIS of Loir-et-Cher. Municipalities with at least 10 000 inhabitants and most municipalities with 5,000 to 10 000 inhabitants are divided into IRIS. By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS.

IRIS

## Overview

IRIS is a dataset for object detection tasks - it contains Eye Iris Position annotations for 586 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.

Discover the CASIA-Iris-Thousand dataset with 20,000 iris images from 1,000 subjects, captured using the advanced IKEMB-100 camera.

Detect Iris

## Overview

Detect Iris is a dataset for computer vision tasks - it contains Iris annotations for 508 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.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Municipalities with at least 10 000 inhabitants and most municipalities with 5,000 to 10 000 inhabitants are divided into IRIS. This division, which is the basis for the dissemination of sub-communal statistics, constitutes a partition of the territory of these communes into “neighbourhoods” with a population of about 2,000 inhabitants. By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS.

This division was drawn up in partnership with local partners, in particular the municipalities, in accordance with precise rules defined in consultation with the Commission Nationale Informatique et Libertés (CNIL). It is constructed on the basis of geographical and statistical criteria and, as far as possible, each IRIS must be homogeneous in terms of habitat. The IRIS offer the most developed tool to date to describe the internal structure of nearly 1,900 municipalities with at least 5,000 inhabitants.

Detections De L'iris

## Overview

Detections De L'iris is a dataset for object detection tasks - it contains Iris annotations for 1,151 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.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Eye And Iris

## Overview

Eye And Iris is a dataset for object detection tasks - it contains Eyes annotations for 439 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.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Sonification for Exploratory Data Analysis #### Chapter 8: Sonification Models In Chapter 8 of the thesis, 6 sonification models are presented to give some examples for the framework of Model-Based Sonification, developed in Chapter 7. Sonification models determine the rendering of the sonification and possible interactions. The "model in mind" helps the user to interprete the sound with respect to the data. ##### 8.1 Data Sonograms Data Sonograms use spherical expanding shock waves to excite linear oscillators which are represented by point masses in model space. * Table 8.2, page 87: Sound examples for Data Sonograms File: Iris dataset: started in plot (a) at S0 (b) at S1 (c) at S2
10d noisy circle dataset: started in plot (c) at S0 (mean) (d) at S1 (edge)
10d Gaussian: plot (d) started at S0
3 clusters: Example 1
3 clusters: invisible columns used as output variables: Example 2 Description: Data Sonogram Sound examples for synthetic datasets and the Iris dataset Duration: about 5 s ##### 8.2 Particle Trajectory Sonification Model This sonification model explores features of a data distribution by computing the trajectories of test particles which are injected into model space and move according to Newton's laws of motion in a potential given by the dataset. * Sound example: page 93, PTSM-Ex-1 Audification of 1 particle in the potential of phi(x). * Sound example: page 93, PTSM-Ex-2 Audification of a sequence of 15 particles in the potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-3 Audification of 25 particles simultaneous in a potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-4 Audification of 25 particles simultaneous in a potential of a dataset with 1 cluster. * Sound example: page 95, PTSM-Ex-5 sigma-step sequence for a mixture of three Gaussian clusters * Sound example: page 95, PTSM-Ex-6 sigma-step sequence for a Gaussian cluster * Sound example: page 96, PTSM-Iris-1 Sonification for the Iris Dataset with 20 particles per step. * Sound example: page 96, PTSM-Iris-2 Sonification for the Iris Dataset with 3 particles per step. * Sound example: page 96, PTSM-Tetra-1 Sonification for a 4d tetrahedron clusters dataset. ##### 8.3 Markov chain Monte Carlo Sonification The McMC Sonification Model defines a exploratory process in the domain of a given density p such that the acoustic representation summarizes features of p, particularly concerning the modes of p by sound. * Sound Example: page 105, MCMC-Ex-1 McMC Sonification, stabilization of amplitudes. * Sound Example: page 106, MCMC-Ex-2 Trajectory Audification for 100 McMC steps in 3 cluster dataset * McMC Sonification for Cluster Analysis, dataset with three clusters, page 107 * Stream 1 MCMC-Ex-3.1 * Stream 2 MCMC-Ex-3.2 * Stream 3 MCMC-Ex-3.3 * Mix MCMC-Ex-3.4 * McMC Sonification for Cluster Analysis, dataset with three clusters, T =0.002s, page 107 * Stream 1 MCMC-Ex-4.1 (stream 1) * Stream 2 MCMC-Ex-4.2 (stream 2) * Stream 3 MCMC-Ex-4.3 (stream 3) * Mix MCMC-Ex-4.4 * McMC Sonification for Cluster Analysis, density with 6 modes, T=0.008s, page 107 * Stream 1 MCMC-Ex-5.1 (stream 1) * Stream 2 MCMC-Ex-5.2 (stream 2) * Stream 3 MCMC-Ex-5.3 (stream 3) * Mix MCMC-Ex-5.4 * McMC Sonification for the Iris dataset, page 108 * MCMC-Ex-6.1 * MCMC-Ex-6.2 * MCMC-Ex-6.3 * MCMC-Ex-6.4 * MCMC-Ex-6.5 * MCMC-Ex-6.6 * MCMC-Ex-6.7 * MCMC-Ex-6.8 ##### 8.4 Principal Curve Sonification Principal Curve Sonification represents data by synthesizing the soundscape while a virtual listener moves along the principal curve of the dataset through the model space. * Noisy Spiral dataset, PCS-Ex-1.1 , page 113 * Noisy Spiral dataset with variance modulation PCS-Ex-1.2 , page 114 * 9d tetrahedron cluster dataset (10 clusters) PCS-Ex-2 , page 114 * Iris dataset, class label used as pitch of auditory grains PCS-Ex-3 , page 114 ##### 8.5 Data Crystallization Sonification Model * Table 8.6, page 122: Sound examples for Crystallization Sonification for 5d Gaussian distribution File: DCS started at center, in tail, from far outside Description: DCS for dataset sampled from N{0, I_5} excited at different locations Duration: 1.4 s * Mixture of 2 Gaussians, page 122 * DCS started at point A DCS-Ex1A * DCS started at point B DCS-Ex1B * Table 8.7, page 124: Sound examples for DCS on variation of the harmonics factor File: h_omega = 1, 2, 3, 4, 5, 6 Description: DCS for a mixture of two Gaussians with varying harmonics factor Duration: 1.4 s * Table 8.8, page 124: Sound examples for DCS on variation of the energy decay time File: tau_(1/2) = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2 Description: DCS for a mixture of two Gaussians varying the energy decay time tau_(1/2) Duration: 1.4 s * Table 8.9, page 125: Sound examples for DCS on variation of the sonification time File: T = 0.2, 0.5, 1, 2, 4, 8 Description: DCS for a mixture of two Gaussians on varying the duration T Duration: 0.2s -- 8s * Table 8.10, page 125: Sound examples for DCS on variation of model space dimension File: selected columns of the dataset: (x0) (x0,x1) (x0,...,x2) (x0,...,x3) (x0,...,x4) (x0,...,x5) Description: DCS for a mixture of two Gaussians varying the dimension Duration: 1.4 s * Table 8.11, page 126: Sound examples for DCS for different excitation locations File: starting point: C0, C1, C2 Description: DCS for a mixture of three Gaussians in 10d space with different rank(S) = {2,4,8} Duration: 1.9 s * Table 8.12, page 126: Sound examples for DCS for the mixture of a 2d distribution and a 5d cluster File: condensation nucleus in (x0,x1)-plane at: (-6,0)=C1, (-3,0)=C2, ( 0,0)=C0 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s * Table 8.13, page 127: Sound examples for DCS for the cancer dataset File: condensation nucleus in (x0,x1)-plane at: benign 1, benign 2
malignant 1, malignant 2 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s ##### 8.6 Growing Neural Gas Sonification * Table 8.14, page 133: Sound examples for GNGS Probing File: Cluster C0 (2d): a, b, c
Cluster C1 (4d): a, b, c
Cluster C2 (8d): a, b, c Description: GNGS for a mixture of 3 Gaussians in 10d space Duration: 1 s * Table 8.15, page 134: Sound examples for GNGS for the noisy spiral dataset File: (a) GNG with 3 neurons 1, 2
(b) GNG with 20 neurons end, middle, inner end
(c) GNG with 45 neurons outer end, middle, close to inner end, at inner end
(d) GNG with 150 neurons outer end, in the middle, inner end
(e) GNG with 20 neurons outer end, in the middle, inner end
(f) GNG with 45 neurons outer end, in the middle, inner end Description: GNG probing sonification for 2d noisy spiral dataset Duration: 1 s * Table 8.16, page 136: Sound examples for GNG Process Monitoring Sonification for different data distributions File: Noisy spiral with 1 rotation: sound
Noisy spiral with 2 rotations: sound
Gaussian in 5d: sound
Mixture of 5d and 2d distributions: sound Description: GNG process sonification examples Duration: 5 s #### Chapter 9: Extensions #### In this chapter, two extensions for Parameter Mapping

Circles proportional to the 2011 population located in the centre of the IRIS of Ile-de-France and associating variables from the 2011 population census. Confined to the limits of their original IRISs, these abstract cartographic objects visually reflect information more rooted in the reality of their demography and can be used as a medium for thematic analysis of other information derived from the data awarded to this population and expressed in rates.

Discover iris synechia significant genes, associations, procedures and more. Data for phenotype iris synechia is all freely available for download.

Discover abnormal iris morphology significant genes, associations, procedures and more. Data for phenotype abnormal iris morphology is all freely available for download.

Il controllo della cronologia dei prezzi di IRIS Chain consente agli investitori di criptovalute di monitorare facilmente la performance dei loro investimenti. Puoi monitorare comodamente il valore di apertura, di chiusura e massimo di IRIS Chain nel tempo, nonché il volume di trading. Inoltre, puoi visualizzare istantaneamente la variazione giornaliera sotto forma di percentuale, per identificare facilmente i giorni con oscillazioni significative. Secondo i dati del nostro storico dei prezzi di IRIS Chain, il suo valore è salito vertiginosamente a un livello senza precedenti in 2025-06-27, superando il valore di -- USD. D'altra parte, il punto più basso nella traiettoria del prezzo di IRIS Chain, comunemente chiamato "minimo storico di IRIS Chain", si è verificato il 2025-06-27. Se qualcuno avesse acquistato IRIS Chain in quel periodo, attualmente godrebbe di un notevole profitto pari a 0%. Saranno creati 2B IRIS Chain per design. Al momento, l’offerta circolante di IRIS Chain è di circa 0. Tutti i prezzi elencati in questa pagina sono stati ottenuti da Bitget, una fonte affidabile. È fondamentale affidarsi a un'unica fonte per verificare i propri investimenti, poiché i valori possono variare tra i diversi venditori. Il nostro insieme di dati dei prezzi storici di IRIS Chain include dati a intervalli di 1 minuto, 1 giorno, 1 settimana e 1 mese (apertura/alto/basso/chiusura/volume). Questi insiemi di dati sono stati sottoposti a test rigorosi per garantire coerenza, completezza e accuratezza. Sono progettati specificamente per simulare il trading e il backtesting, immediatamente disponibili per il download gratuito e aggiornati in tempo reale.