The KMeans model is a machine learning algorithm used for clustering data into groups based on their similarities. In this particular implementation, the model is trained on the Iris dataset, which consists of measurements of three different species of Iris flowers (setosa, versicolor, and virginica). The KMeans model is used to group the Iris flowers into three clusters based on their sepal length, sepal width, petal length, and petal width. Once the model is trained, it can be used to predict the cluster assignments for new data points. In this implementation, the model is saved to disk using pickle, and can be loaded back into memory for later use. The code also includes a Flask web application that allows users to input values for the four Iris flower measurements, and displays the predicted cluster assignment as well as the corresponding Iris type.
