<prompt> Kenya tsebetsong algorithm ea ho kopanya ea K ho lintlha tsena tse fanoeng tsa data.3,7,2,2,2,5,1,3,8,6  <response>: Instruction in English: Implement a K-means clustering algorithm on these given data points. . Response in English: Sure. Here is an implementation in Python that uses the k-means clustering algorithm on the given data points:from sklearn.cluster import KMeansimport numpy as npdata np.array3,7,2,2,2,5,1,3,8,6kmeans KMeansn_clusters2.fitdata Display the cluster centersprintkmeans.cluster_centers_ Display the cluster assignments of each data pointprintkmeans.labels_The k-means algorithm is a type of unsupervised learning that aims to partition n data points into k clusters, where each data point belongs to the cluster with the nearest mean cluster center. In this code, the data points are first represented as a NumPy array. Then, the KMeans class from the scikit-learn library is used to fit the data points into 2 clusters. The cluster centers are displayed using the cluster_centers attribute, and the cluster assignments of each data point are displayed using the labels attribute.In this case, the output would be:7. 6. 2. 3.333333331 1 1 0The first array represents the cluster centers and the second array represents the cluster assignments of each data point. Data points 3,7,2,2,2,5,1,3 are assigned to Cluster 1 while the data point 8,6 is assigned to Cluster 0. . Response in Sesotho: Ehlile. Ena ke tsebetsong ho Python e sebelisang k-means clustering algorithm ho lintlha tse fanoeng tsa data: ho tsoa ho sklearn.cluster import KMeans import numpy as np data np.array3,7,2,2 ,2,5,1,3,8,6 kmeans KMeansn_clusters2.fitdata Bontša khatiso ea litsi tsa lihlophakmeans.cluster_centers_ Bontša mesebetsi ea lihlopha ea mongolo o mong le o mong oa lintlha tsa datakmeans.labels_ K-means algorithm ke mofuta oa thuto e sa laoleheng e ikemiselitseng ho arola lintlha tsa data ka lihlopha tsa k, moo ntlha ka ngoe ea data e leng ea sehlopha se nang le moelelo o haufi cluster center. . Khoutung ena, lintlha tsa data li qala ho emeloa e le sehlopha sa NumPy. Ebe, sehlopha sa KMeans se tsoang laeboraring ea scikit-learn se sebelisoa ho kopanya lintlha tsa data ka lihlopha tse 2. Litsi tsa cluster li bontsoa ho sebelisoa tšobotsi ea cluster_centers, me likabelo tsa sehlopha sa ntlha e ngoe le e ngoe ea data li hlahisoa ho sebelisoa sebopeho sa li-label. Tabeng ena, tlhahiso e tla ba: 7. 6. 2. 3.33333333 1 1 1 0 Sehlopha sa pele se emela litsi tsa lihlopha me sehlopha sa bobeli se emela likabelo tsa sehlopha sa ntlha ka ngoe ea data. Lintlha tsa lintlha 3,7,2,2,2,5,1,3 li abetsoe Cluster 1 ha ntlha ea data 8,6 e abetsoe Cluster 0. 