52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# 資料預處理
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dataset = pd.read_csv("Mall_Customers.csv")
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X = dataset.iloc[:, 3:5].values # 無監督學習，無需應變數
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# 獲取 K，得到結果 K = 5
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from sklearn.cluster import KMeans
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# wcss = []
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#
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# for i in range(1, 11):
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# kmeans = KMeans(n_clusters=i, max_iter=300, n_init=10, init='k-means++', random_state=0)
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# kmeans.fit(X)
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# wcss.append(kmeans.inertia_)
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#
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# plt.plot(range(1, 11), wcss)
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# plt.title('The Elbow Method')
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# plt.xlabel('Number of clusters')
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# plt.ylabel('WCSS')
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# plt.show()
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# 開始分析
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kmeans = KMeans(n_clusters=5, max_iter=300, n_init=10, init='k-means++', random_state=0)
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Y_kmeans = kmeans.fit_predict(X)
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print(Y_kmeans)
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# 視覺化
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plt.scatter(X[Y_kmeans == 0, 0], X[Y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
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plt.scatter(X[Y_kmeans == 1, 0], X[Y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
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plt.scatter(X[Y_kmeans == 2, 0], X[Y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
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plt.scatter(X[Y_kmeans == 3, 0], X[Y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
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plt.scatter(X[Y_kmeans == 4, 0], X[Y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
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plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')
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plt.title('Clusters of customers')
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plt.xlabel('Annual Income (k$)')
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plt.ylabel('Spending Score (1-100)')
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plt.legend()
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plt.show()
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