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from sklearn.preprocessing import LabelEncoder label_enc= LabelEncoder() for col in data.columns: data[col]=label_enc.fit_transform(data[col]) from sklearn.preprocessing import StandardScaler sc= StandardScaler() scaled_data=sc.fit_transform(data.iloc[:,:]) scaled_data from sklearn.cluster import KMeans wcss=[] for i in range(1,4): kmeans=KMeans(n_clusters=i,init='k-means++',random_state=42) kmeans.fit(scaled_data) wcss.append(kmeans.inertia_) ## Checking where elbow id breaking clusters_new=KMeans(3,random_state=42) clusters_new.fit(scaled_data) clusters_new.labels_ data['Clusters']=clusters_new.labels_