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Keras Project - Iris Flower Identification - Introduction to Iris dataset

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.

The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), and 150 records under five attributes - sepal length, sepal width, petal length, petal width and species. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.

The iris data set is widely used as a beginner's dataset for machine learning purposes.

Objectives

Use the Iris dataset to identify the different 3 classes of flowers from the 4 given features using Deep Learning models in the Keras library.

Let us begin!


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