Iris dataset from open source.

This is an iris dataset commonly used in machine learning. Accessed on 10-19-2020 from the following URL: http://faculty.smu.edu/tfomby/eco5385_eco6380/data/Iris.xls

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

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.

Iris dataset

"Exploring Patterns: The Iris Dataset Analysis" https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19517213%2F157356294f32ae95956a495960b7ea39%2F1_ZK9_HrpP_lhSzTq9xVJUQw.png?generation=1711814010788837&alt=media" alt=""> The Iris dataset is a classic dataset in the field of machine learning and statistics. It was introduced 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. The dataset consists of 150 samples of iris flowers, each belonging to one of three species: Setosa, Versicolor, and Virginica.

Columns : -

• Sepal Length (cm):The length of the sepal, which is the outermost part of the flower that protects the petals.
• Sepal Width (cm): The width of the sepal, measured at its widest point.
• Petal Length (cm): The length of the petal, which is the colorful inner part of the flower.
• Petal Width (cm): The width of the petal, measured at its widest point.
• Species: The species of iris flower, which can be one of three classes: Setosa, Versicolor, or Virginica.

These columns provide measurements of various parts of the iris flower, along with the corresponding species labels, making it a versatile dataset for analysis and classification tasks.

Dataset

This dataset was created by Nasima Islam Bithi

Contents

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.

Description: - This dataset includes all 22 built-in datasets from the Seaborn library, a widely used Python data visualization tool. Seaborn's built-in datasets are essential resources for anyone interested in practicing data analysis, visualization, and machine learning. They span a wide range of topics, from classic datasets like the Iris flower classification to real-world data such as Titanic survival records and diamond characteristics.

• Included Datasets:
  • Anagrams: Analysis of word anagram patterns.
  • Anscombe: Anscombe's quartet demonstrating the importance of data visualization.
  • Attention: Data on attention span variations in different scenarios.
  • Brain Networks: Connectivity data within brain networks.
  • Car Crashes: US car crash statistics.
  • Diamonds: Data on diamond properties including price, cut, and clarity.
  • Dots: Randomly generated data for scatter plot visualization.
  • Dow Jones: Historical records of the Dow Jones Industrial Average.
  • Exercise: The relationship between exercise and health metrics.
  • Flights: Monthly passenger numbers on flights.
  • FMRI: Functional MRI data capturing brain activity.
  • Geyser: Eruption times of the Old Faithful geyser.
  • Glue: Strength of glue under different conditions.
  • Health Expenditure: Health expenditure statistics across countries.
  • Iris: Famous dataset for classifying Iris species.
  • MPG: Miles per gallon for various vehicles.
  • Penguins: Data on penguin species and their features.
  • Planets: Characteristics of discovered exoplanets.
  • Sea Ice: Measurements of sea ice extent.
  • Taxis: Taxi trips data in a city.
  • Tips: Tipping data collected from a restaurant.
  • Titanic: Survival data from the Titanic disaster.

This complete collection serves as an excellent starting point for anyone looking to improve their data science skills, offering a wide array of datasets suitable for both beginners and advanced users.

Dataset

This dataset was created by Iris Ying Li

Contents