Problem: Train a model to distinguish between different species of the Iris flower based on four measurements (features): sepal length, sepal width, petal length, and petal width.

Context: The Iris classification dataset is famous in the world of machine learning. Dating back to R.A. Fisher’s 1936 paper, “The Use of Multiple Measurements in Taxonomic Problems,” the Iris dataset has long been used for introductory machine learning development.

Model type: Logistic regression

What we did: We built, trained, and deployed a machine learning model using a multiclass classification algorithm. (More on how we built this demo.) We developed and deployed the model all within the SnapLogic Enterprise Integration Cloud, a cloud-based data integration platform.

In the demo below, adjust the size of the sepals and petals and then click “Predict” to test out the model built on the SnapLogic platform. The model will predict the flower species based on the inputs you select.