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

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 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 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.

Dataset

This dataset was created by Grady Lynn

Contents

Analysis of ‘Iris.csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/saurabh00007/iriscsv on 28 January 2022.

--- No further description of dataset provided by original source ---

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

Dataset

This dataset was created by huiyun zheng

Contents

Dataset

This dataset was created by Ibrahim Serouis 99

Contents

Dataset

This dataset was created by MITHRA SARAVANAN

Contents

Floral color plays a key role as visual signaling and is therefore of great importance in shaping plant-pollinator interactions. Iris (Iridaceae), a genus comprising over 300 species and named after the Greek goddess of the colorful rainbow, is famous for its dazzling palette of flower colors and patterns, which vary considerably both within and among species. Despite the large variation of flower color in Iris, little is known about the phylogenetic and ecological contexts of floral color. Here, we seek to resolve the evolution of flower color in the genus Iris in a macroevolutionary framework. We used a phylogenetic analysis to reconstruct the ancestral state of flower color and other pollination-related traits (e.g., the presence of nectar and mating system), and also tracked the evolution of color variation. We further explored weather floral trait transitions are better explained by environmental or pollinator-mediated selection. Our study revealed that the most recent common ancestor likely had monomorphic, purple flowers, with a crest and a spot on the fall. The flowers were likely insect-pollinated, nectar-rewarding, and self-compatible. The diversity of floral traits we see in modern irises, likely represents a trade-off between conflicting selection pressures. Whether shifts in these flower traits result from abiotic or biotic selective agents or are maintained by neutral processes without any selection remains an open question. Our analysis serves as a starting point for future work exploring the genetic and physiological mechanisms controlling flower coloration in the most color-diverse genus Iris.

Browse Iris Energy Limited (IREN) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Browse Iris Energy Limited (IREN) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Files to run the small dataset experiments used in the preprint "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. This .csv files enables to generate balanced small dataset from the PASTIS dataset. These files are required to run the experiment with a small training data-set, from the open source code ssl_ubarn. In the .csv file name selected_patches_fold_{FOLD}_nb_{NSITS}_seed_{SEED}.csv :

• FOLD: id which corresponds to one of the 5 experiments run due to PASTIS K-fold.
• NSITS: Number of SITS selected to construct this training data-set
• SEED: the randomness used to create this small dataset

Comparative studies have shown that the eye morphology of primates has been shaped by a variety of selection pressures (e.g. communication, environmental factors). To comprehensively elucidate the complex links between ocular morphology and its evolutionary drive, attention should be paid to other phylogenetic groups. Here, we address a new question regarding the evolution of eye colour patterns in the oldest domesticated animal, namely, the domestic dog (Canis familiaris). In this study, we conducted an image analysis of dogs and their closest relatives, grey wolves (Canis lupus), to compare the colours of their irises, with the aim of assessing whether eye colours of dogs affect how humans perceived dogs. We found that the irises of dogs were significantly darker than those of wolves. We also found that facial images of dark-eyed dogs were perceived as more friendly and immature, potentially eliciting caregiving responses from humans. Our findings are consistent with our expectation that humans favour dark-eyed dogs over light-eyed ones and provide an updated hypothesis that dogs with dark eyes may have evolved by acquiring a facial trait that sends a non-threatening gaze signal to humans.

Summary tabular data relating to Natura 2000 SAC sites in Ireland, providing Natura 2000 site-related details, including lists of the habitats and species listed in Annex I and Annex II of the Habitats Directive for which each Natura 2000 site is selected. Data is accurate up to March 2023. Please check the Iris Oifigiúil, Irish, Irish Statute Book for more recently published Statutory Instrument (S.I.) regulations. Data is provided in a single zip file containing sub folders holding MS Excel, CSV and JSON formats, each accompanied by a ‘readme’ file. This data should be read in conjunction with the spatial (GIS) boundaries for sites, site documents and related publications (see further https://www.npws.ie/maps-and-data/designated-site-data/ )

Sa tacar sonraí seo tá liosta de shraith na nIrisí Oifigiúla (IO) L agus C a foilsíodh in 2020 i bhformáid csv. Tá gach Iris Oifigiúil ar fáil i bhformáid XML Formex trí nasc.

Is í an Iris Oifigiúil coimre oifigiúil reachtaíocht an Aontais Eorpaigh (sraith L) agus doiciméid oifigiúla eile de chuid institiúidí, chomhlachtaí agus ghníomhaireachtaí an Aontais (sraith C agus na forlíonta a ghabhann léi). Foilsítear é gach lá ó Mháirt go Satharn i dteangacha oifigiúla an Aontais Eorpaigh.

Tá sé ar fáil freisin (i bhformáidí HTML agus PDF) ar suíomh gréasáin EUR-Lex

Sa tacar sonraí seo tá liosta de shraith L agus C na nIrisí Oifigiúla (IO) a foilsíodh in 2005 i bhformáid csv. Tá gach Iris Oifigiúil ar fáil i bhformáid XML Formex trí nasc.

Is í an Iris Oifigiúil coimre oifigiúil reachtaíocht an Aontais Eorpaigh (sraith L) agus doiciméid oifigiúla eile de chuid institiúidí, chomhlachtaí agus ghníomhaireachtaí an Aontais (sraith C agus na forlíonta a ghabhann léi). Foilsítear é gach lá ó Mháirt go Satharn i dteangacha oifigiúla an Aontais Eorpaigh.

Tá sé ar fáil freisin (i bhformáidí HTML agus PDF) ar suíomh gréasáin EUR-Lex