ANNs using Pytorch – Iris Flower Dataset

Data:

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

Data reference:

https://archive.ics.uci.edu/ml/datasets/Iris/

Citation :

UCI Machine Learning Repository [https://archive.ics.uci.edu/ml/datasets/Iris/]. Irvine, CA: University of California, School of Information and Computer Science.

Attributes:

• sepal length in cm
• sepal width in cm
• petal length in cm
• petal width in cm
• Target class:
 o Iris Setosa
 o Iris Versicolour
 o Iris Virginica

Key asks:

• Build a classification model based on the features provided for the Iris dataset
• For any new set of features, determine what the class will be