K-MEANS CLUSTERINGimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import make_blobsfrom sklearn.cluster import KMeansX, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.6, random_state=0)kmeans = KMeans(n_clusters=4)kmeans.fit(X)labels = kmeans.labels_centers = kmeans.cluster_centers_plt.scatter(X[:, 0], X[:, 1], c=labels, cmap=’viridis’)plt.scatter(centers[:, 0], centers[:, 1], marker=’X’, color=’red’, s=200)plt.title(“K-means Clustering”)plt.xlabel(“Feature 1”)plt.ylabel(“Feature 2”)plt.show() Decision trees import warningswarnings.filterwarnings(“ignore”)# Importing the required packagesimport numpy as npimport pandas as pdfrom …
Robo 2.0 Review Read More »