The Iris Dataset consists of 150 iris samples, each having four numerical features: sepal length, sepal width, petal length, and petal width. Each sample is categorized into one of three iris species: Setosa, Versicolor, or Virginica. This dataset is widely used as a sample dataset in machine learning and statistics due to its simple and easily understandable structure.

Feature Information : - Sepal Length (cm) - Sepal Width (cm) - Petal Length (cm) - Petal Width (cm)

Target Information : - Iris Species : 1. Setosa 1. Versicolor 1. Virginica

Source : The Iris Dataset is obtained from the scikit-learn (sklearn) library under the BSD (Berkeley Software Distribution) license.

File Formats :

1. CSV (Comma-Separated Values): CSV format is the most commonly used and easily readable format. Each row represents one sample with its features separated by commas.
2. Excel (.xlsx): Excel format is suitable for further data analysis, visualization, and integration with other software.
3. JSON (JavaScript Object Notation): JSON format allows data to be stored in a more complex structure, suitable for web-based data processing or applications.
4. Parquet: Parquet format is an efficient columnar data format for large and complex data.
5. HDF5 (Hierarchical Data Format version 5): HDF5 format stores data in hierarchical groups and datasets, excellent for storing large scientific and numerical data.
6. Feather: Feather format is a lightweight binary format for storing data frames. It provides excellent performance for reading and writing data.
7. SQLite Database (.db, .sqlite): SQLite is a lightweight database format suitable for local data storage and querying. It is widely used for small to medium-scale applications.
8. Msgpack: Msgpack format is a binary serialization format that is efficient in terms of storage and speed. It is suitable for storing and transmitting data efficiently between systems.

The Iris Dataset is one of the most iconic datasets in the world of machine learning and data science. This dataset contains information about three species of iris flowers: Setosa, Versicolor, and Virginica. With features like sepal and petal length and width, the Iris dataset has been a stepping stone for many beginners in understanding the fundamental concepts of classification and data analysis. With its clarity and diversity of features, the Iris dataset is perfect for exploring various machine learning techniques and building accurate classification models. I present the Iris dataset from scikit-learn with the hope of providing an enjoyable and inspiring learning experience for the Kaggle community!

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

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

1) Data Introduction • The Iris Species Dataset is a classic multi-class classification data that collected a total of 150 samples, 50 for each of the three iris species (Setosa, Versicolor, Virginica), consisting of four numerical characteristics and species labels, including calyx length, width, petal length, and width.

2) Data Utilization (1) The Iris Species Dataset has characteristics that: • This dataset consists of a total of six columns and is labeled as one of three types, making it suitable for class division and basic statistical analysis. (2) The Iris Species Dataset can be used to: • Classification Algorithm Practice: You can easily practice various machine learning classification models such as logistic regression, SVM, and decision tree by inputting four characteristics: calyx and petal length and width. • Visualize data and analyze basic statistics: Visualize the distribution of characteristics by variety into scatterplots, boxplots, etc. to explore differences between classes and correlations between characteristics.

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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('iris', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This dataset about different flowers. It contains information about different species of flowers.

Context

Iris es un dataset propio incluido en R. En este caso se han adicionado 2 nuevas variables al archivo original (Petal.number y color). Contiene información sobre la medición de los pétalos y sépalos de 150 plantas.

Content

Sepal.Length: Longitud del sépalo (cm) Sepal.Width: Tamaño del sépalo (cm) Petal.Length: Longitud del pétalo (cm) Petal.Width: Tamaño del pétalo (cm) Species: Especie de planta Iris Petal.number: Cantidad de pétalos Color: Color de la flor

Total-Current-Assets Time Series for Iris Business Services Limited. IRIS Business Services Limited, together with its subsidiaries, provides regulatory technology solutions in India, the Middle East, the Asia Pacific, Africa, the United States, Europe, and the United Kingdom. It operates through SupTech, RegTech, TaxTech, DataTech segments. The company offers IRIS iDEAL, a regulatory reporting solution for banks, credit institutions, and investment firms; IRIS CARBON, a SaaS platform to create comprehensive financial and non-financial reports in XBRL/iXBRL formats; IRIS GST Software, a filing solution with an ERP connector, reconciliation module, vendor management, and informative MIS and reports; and IRIS IRP, a connection solution for taxpayers. It also provides IRIS iFILE, an end-to-end electronic filing platform to collect, validate, and analyze any type of data from the entities; IRIS E-Invoicing, an e-invoicing solution with integrated GST compliance and reconciliation; IRIS Zircon, that are APIs for GST, Eway bill, and e-invoicing for enterprises and partners; and IRI LMS, a DT and IDT litigation management tool to simplify GST audits and litigations for enterprises. In addition, the company offers IRIS iConnect, an XBRL analytics tool to evaluate and compare XBRL and iXBRL data using the familiar Microsoft Excel format; IRIS Credixo for banks and fintech to make credit analysis models; and IRIS Peridot, an app for GST counterparty verification. Further, it provides workflow-based e-filing software; tax technology solutions; taxonomy development and testing; and consulting and training services, as well as software licensing, subscription of software as a service, and application maintenance services. The company serves regulators, including central banks, business registries, capital market regulators, and stock exchanges, as well as corporates, banks, and mutual funds. IRIS Business Services Limited was incorporated in 2000 and is headquartered in Thane, India.

lung detection for pneumonia or normal based on kaggle dataset

Supplementary Material for ‘Pseudomonas germanica sp. nov., isolated from Iris germanica rhizomes’, as published in International Journal of Systematic and Evolutionary Microbiology

Reports “Engaged to the Daily” extracted from the OSIS database, by IRIS Each report was repositioned at the IRIS centre on which it was located. The IRIS (Iots Regrouped for Statistical Information)” is the foundation for the dissemination of infra-communal data. Initially it was supposed to have a size of about 2,000 inhabitants. The municipalities of at least 10000 inhabitants and a large proportion of the municipalities of 5000 to 10000 inhabitants are divided into IRIS. This division constitutes a partition of their territory. By extension, in order to cover the entire territory, we assimilate to an IRIS each of the municipalities not cut into IRIS. Alerts entered by users on EAQ/MDMP/MPM web or mobile applications and alerts entered by operators, which transcribe user phone calls. Alert excluded: — trade alerts which correspond to exchanges of information between trades, following reports from users — reports of families ‘GEOLOCALISATION,’_INACTIVE', ‘Tools’, ‘_TEST_DPSI’, ‘BATIMENT’, ‘ETUDE’, ‘POSTS FIXES’, ‘QUESTION’ — alerts from the sub-family ‘CONNECTED DECHETERIES’ — geolocated reports outside the metropolis — reports of deactivated families, sub-families and types — reports of type of complaint on agent behaviour

Dataset

This dataset was created by sikandar

Contents

Active sensing using light, or active photolocation, is only known from deep sea and nocturnal fish with chemiluminescent "search" lights. Bright irides in diurnal fish species have recently been proposed as a potential analogue. Here, we contribute to this discussion by testing whether iris radiance is actively modulated. The focus is on behaviourally controlled iris reflections, called "ocular sparks". The triplefin Tripterygion delaisi can alternate between red and blue ocular sparks, allowing us to test the prediction that spark frequency and hue depend on background hue and prey presence. In a first experiment, we found that blue ocular sparks were significantly more often "on" against red backgrounds, and red ocular sparks against blue backgrounds, particularly when copepods were present. A second experiment tested whether hungry fish showed more ocular sparks, which was not the case. Again, background hue resulted in differential use of ocular spark types. We conclude that iris radiance through ocular sparks in T. delaisi is not a side effect of eye movement, but adaptively modulated in response to the context under which prey are detected. We discuss the possible alternative functions of ocular sparks, including an as yet speculative role in active photolocation.

Infra-communal demography in IRIS of the communes 2014 and population 2010 according to INSEE in the Somme. 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. There are three types of Iris Habitat Iris: their population is generally between 1,800 and 5,000 inhabitants. They are homogeneous in the type of habitat and their boundaries are based on major cuts in the urban fabric (main roads, railways, rivers, etc.). Activity Iris: they have more than 1,000 employees and have at least twice as many salaried jobs as the resident population. Miscellaneous Iris: these are large specific areas that are little inhabited and have a large area (recreation parks, port areas, forests, etc.). 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.

Context

The Iris database was the first DB I used at the beginning of my career as a data science student!

Content

This DB was introduced to me by my professor John Ponciano from Samsung Ocean

The global motorized iris diaphragm market, valued at $675 million in 2025, is projected to experience steady growth, driven by increasing demand across diverse sectors. The Compound Annual Growth Rate (CAGR) of 3.5% from 2025 to 2033 indicates a consistent expansion, fueled primarily by advancements in optoelectronics and medical technology. The optoelectronics segment is a major contributor, with applications ranging from advanced imaging systems to high-precision laser equipment requiring precise light control. Medical technology applications, including ophthalmic surgery and diagnostic imaging, are also significant drivers, as motorized iris diaphragms enable greater control and precision in these procedures. The market is segmented by type, with zero-aperture and adjustable-aperture motorized iris diaphragms catering to varying application needs. While precise market share data for each type is unavailable, it's reasonable to assume that adjustable-aperture diaphragms hold a larger market share due to their greater versatility. Competitive dynamics are characterized by the presence of established players such as Standa, Newport, and Edmund Optics, alongside specialized manufacturers like Eksma Optics and SIGMA KOKI. Geographic distribution shows a relatively balanced spread across North America, Europe, and Asia Pacific, although the precise regional breakdown requires more granular data. Future growth will likely be influenced by technological advancements leading to smaller, more efficient, and cost-effective motorized iris diaphragms, further expanding their applicability in various industries. The market's growth trajectory is expected to be influenced by several factors. Technological innovations, like improved motor designs and advanced materials, are expected to enhance performance and durability, thus driving adoption. Increased demand for higher-precision optical systems in industries like semiconductor manufacturing and laser processing will also contribute to market expansion. However, potential restraints include the relatively high cost of advanced motorized iris diaphragms, which could limit adoption in certain price-sensitive applications. Furthermore, the market’s growth could be impacted by economic fluctuations and global supply chain disruptions. Despite these challenges, the long-term outlook for the motorized iris diaphragm market remains positive, with continuous technological advancements and expanding applications across diverse industries poised to drive substantial growth in the coming years.

****** Number of households by type in 2012 in the communes of Angers Loire Métropole cut into IRIS. **This dataset is a working tool of the Angevin Region Planning Agency.

Labelled fields in this dataset:

LABIRIS: scale value 1 to 4 (if=1 usable and if=4 non-usable on its own; obligation to add more than one IRIS); C12_MENPSEUL: number of households of a single person in 2012; C12_MENCOUPSENF: number of couples households without children in 2012; C12_MENFAMMONO: number of single-parent households in 2012; C12_MENCOUPAENF: number of couples households with children in 2012; C12_MENSFAM: number of other households in 2012; C12_MEN: total number of households in 2012.

Predicting Iris Species using Decision Tree.

The Iris dataset is a multivariate dataset containing measurements of 150 iris flowers. The dataset is commonly used for classification and machine learning tasks. Each data point represents an iris flower and includes the following features: sepal length, sepal width, petal length, and petal width, all measured in centimeters. The dataset contains 50 instances of each of three species of iris: Setosa, Versicolor, and Virginica. This makes it a balanced dataset for classification purposes. The dataset is often used for educational purposes and to demonstrate various machine learning algorithms, such as decision trees.

The Iris dataset, as used in this experiment, comprises 150 instances of iris flowers, each described by five attributes: sepal length, sepal width, petal length, petal width, and species. The dataset is structured as a CSV file (iris.csv) and contains no null values, ensuring data cleanliness. The dataset's shape is (150, 5), indicating 150 rows (samples) and 5 columns (features and target variable). Each instance is classified into one of three species: Setosa, Versicolor, or Virginica, with 50 instances per species, making it a balanced dataset. The features (sepal length, sepal width, petal length, and petal width) are measured in centimeters and are of the float64 data type, while the species is an object (string) type. This dataset is widely used for classification tasks, particularly with decision tree algorithms, to predict the species of iris flowers based on their sepal and petal measurements.

Aim: - The aim of this experiment is to predict the species of Iris flowers based on the measurements of their sepals and petals using the Decision Tree algorithm. By training the model with known data, the decision tree will classify the species of Iris flowers based on input features. *

**Details of Dataset ** • Size of the Dataset: 750 elements • Shape of the Dataset: (150, 5) (rows, columns)
• Total memory usage of the Dataset: 14732 bytes

**Decision Tree Classifier: ** A decision tree is a flowchart-like structure where: • Each internal node represents a "test" on an attribute (e.g., petal length or sepal width). • Each branch represents the outcome of that test (e.g., whether the attribute is greater than a threshold). • Each leaf node represents a class label (species in our case). The algorithm works by recursively splitting the dataset into subsets based on the value of features. It continues splitting until a stopping criterion is met (e.g., maximum depth of the tree or no further improvement can be made). For classification tasks like Iris species prediction, the Decision Tree uses metrics such as Gini Impurity or Information Gain (Entropy) to decide the best split at each node.

**Steps in the process: ** 1. Load the dataset and understand its structure. 2. Pre process the data by segregating features and target variables. 3. Split the data into training and testing sets. 4. Train the Decision Tree classifier. 5. Tune the parameters and evaluate the performance using metrics like accuracy and confusion matrix. 6. Visualize the decision tree.

This map, built from INSEE, IGN data, the National Address Base, supervised by the Interministerial Directorate of Digital (DINUM), and departmental data, allows to visualize the streets and numbers of the Le Havre Seine Métropole intercommunality as well as different administrative and social perimeters in the Seine-Maritime Department in 2022. There are three types of administrative perimeter: * IRIS (Grouped Islands for Statistical Information). This division, set up by INSEE, is the basic building block for the dissemination of infra-municipal data. An IRIS represents between 1,800 and 5,000 inhabitants or more than 1,000 employees or a specific sparsely populated right-of-way (port, nature park, etc.), * the municipalities, * the cantons, which are the constituencies serving as the framework for the election of departmental councillors, as well as a sectorisation corresponding to a scale of implementation of the Department's social policies: * the sectors of the Medical and Social Centres (CMS), departmental structures that provide medical follow-up for babies and young children but also constitute a local entry point for access to rights (professional integration, support for the elderly, etc.). Metadata Link to metadata Additional resources * INSEE website: The website of the National Institute of Statistics and Economic Studies provides detailed definitions of the different French administrative perimeters and also allows you to download many data at these scales. * Geoservices website: Many data from the National Institute of Geographical and Forestry Information are freely downloadable, in particular in shape format, on this site published by IGN. * Website of the Seine-Maritime Department: and The website of the Department of Seine-Maritime provides more information on the role of CMS and UTAS and the services they may offer.

This interactive map, built from INSEE, IGN and departmental data, allows you to visualize different administrative and social perimeters in the Seine-Maritime Department in 2022. there are four types of administrative perimeter: * IRIS (Grouped Islands for Statistical Information). This division, set up by INSEE, is the basic building block for the dissemination of infra-municipal data. An IRIS represents between 1,800 and 5,000 inhabitants or more than 1,000 employees or a specific sparsely populated right-of-way (port, nature park, etc.), * the municipalities, * inter-municipalities (Public Establishments of Inter-municipal Cooperation or EPCI), which are groupings of municipalities around common projects: Communities of Municipalities, Agglomeration Communities, Metropolises, etc., * the cantons, which are the constituencies serving as the framework for the election of departmental councillors, as well as three sectorisations corresponding to the scales of implementation of the Department’s social policies: * the sectors of the Medical and Social Centres (CMS), departmental structures that provide medical follow-up for babies and young children but also constitute a local entry point for access to rights (professional integration, support for the elderly, etc.) * groupings of CMS, which are more technical perimeters used by the Department for the implementation of its social policies, * and the Territorial Units of Social Action (UTAS), the Department is divided into 5 UTAS, which are both places of reception, information, guidance and accompaniment of the public as well as perimeters of reflection with sociological and territorial specificities. The user can, by clicking on the name of a municipality and then, by selecting the choice(s) that automatically appear in the corresponding drop-down menus, view to which EPCI, Canton, CMS, grouping of CMS and UTAS this territory belongs. Research is also possible from the other levels: EPCI, CMS, etc. Finally, a right click on the map will indicate in a pop-up window the names of the different perimeters to which the clicked place belongs (press the arrows at the top right of the window to scroll through them). For more readability, you can also disable the selection of one or more categories by clicking on the ‘reset’ button (at the bottom of the category) to leave visible only those that interest you. Metadata Link to metadata Additional resources * INSEE website: The website of the National Institute of Statistics and Economic Studies provides detailed definitions of the different French administrative perimeters and also allows you to download many data at these scales. * Geoservices website: Many data from the National Institute of Geographical and Forestry Information are freely downloadable, in particular in shape format, on this site published by IGN. * Website of the Seine-Maritime Department: and The website of the Department of Seine-Maritime provides more information on the role of CMS and UTAS and the services they may offer.