This project explores the application of K-Means clustering, an unsupervised machine learning algorithm, on two widely-used datasets:
The Iris dataset contains information on three different species of Iris flowers (Setosa, Versicolor, Virginica) based on four features: sepal length, sepal width, petal length, and petal width. The objective is to use K-Means clustering to group the flowers into distinct clusters and compare the results with the known species labels. The project visualizes the clusters using scatter plots, and the cluster centers are plotted to show the points around which the data has been grouped.
This dataset includes information about customers' annual income and spending score in a mall. K-Means clustering is applied to segment customers into different groups based on their behavior, helping in customer segmentation and targeted marketing strategies. The clusters are visualized by plotting the spending score against the annual income, with the cluster centers marked on the plot. For both datasets, visualizations of the clusters and cluster centers are provided, illustrating how K-Means identifies groups based on feature similarity. This project can be used as a reference for understanding the basics of K-Means and how it is applied to real-world datasets