我使用kmeans算法來確定數據集中的簇數。在下面的代碼中，您可以看到我有多個特征，有些是分類的，有些不是。我對它們進行編碼和縮放，然后我得到了我的最佳簇數。您可以從這里下載數據：import sklearn.metrics as smfrom sklearn.preprocessing import scalefrom sklearn.preprocessing import Normalizerfrom sklearn.preprocessing import StandardScaler, MinMaxScalerfrom sklearn.cluster import KMeans, SpectralClustering, MiniBatchKMeansfrom sklearn.compose import ColumnTransformerfrom sklearn.preprocessing import OneHotEncoderimport matplotlib.pyplot as pltimport pandas as pddf = pd.read_csv('dataset.csv')print(df.columns)features = df[['parcela', 'bruto', 'neto',? ? ? ? ? ? ? ?'osnova', 'sipovi', 'nadzemno',? ? ? ? ? ? ? ?'podzemno', 'tavanica', 'fasada']]trans = ColumnTransformer(transformers=[('onehot', OneHotEncoder(), ['tavanica', 'fasada']),? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ('StandardScaler', Normalizer(), ['parcela', 'bruto', 'neto', 'osnova', 'nadzemno', 'podzemno', 'sipovi'])],? ? ? ? ? ? ? ? ? ? ? ? ? remainder='passthrough') # Default is to drop untransformed columnsfeatures = trans.fit_transform(features)Sum_of_squared_distances = []for i in range(1,19):? ? ?kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)? ? ?kmeans.fit(features)? ? ?Sum_of_squared_distances.append(kmeans.inertia_)plt.plot(range(1,19), Sum_of_squared_distances, 'bx-')plt.xlabel('k')plt.ylabel('Sum_of_squared_distances')plt.title('Elbow Method For Optimal k')plt.show()在圖表上，肘部方法顯示我的最佳聚類數為 7。如何繪制 7 個集群？我想在圖表上看到質心，以及具有 7 種不同顏色的簇的散點圖。