Supervised Machine Learning w/ Iris Flowers Classification
The Iris Flowers dataset is seen as the “Hello World” of ML as it’s the classic example of classification. This dataset offers a great introduction as it requires you to learn how to explore data and how to load it. The benefit of this dataset is that is small to load into your memory (150 rows) and it has only four properties: Petal length, Petal width, Sepal length, and Sepal width.
The project involves the identification of four different species of Iris flowers using the four known properties. The dataset allows you to use a supervised learning algorithm as the data is labeled whereas unsupervised means that we are looking for hidden structures in the data as the data is unlabeled.
Classification Type? We are using Multiclass Classification here. This means that we should be able to predict accurately to which class a data point belongs.
Goal: Classify flowers among three species based on the properties of the flower: dimensions of petals and sepals.
Download: Iris Flowers Dataset Full guide: To solving the problem can be found here.