機器學習—聚類5-1（K-Means演算法+瑞士捲）

使用K-Means對超市客戶分組

主要步驟流程：

• 1. 匯入包
• 2. 匯入資料集
• 3. 使用肘部法則選擇最優的K值
• 4. 使用K=5做聚類
• 5. 視覺化聚類效果
• 6. 採取措施
• 7. 瑞士捲生產及其聚類

 

 資料集連結：https://www.heywhale.com/mw/dataset/6230697d5f17950018ee88b5/file

 

1. 匯入包

In [1]:

# 匯入包
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

 

2. 匯入資料集

In [2]:

# 匯入資料集
dataset = pd.read_csv('Mall_Customers.csv')
dataset

Out[2]:

	CustomerID	Genre	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40
...	...	...	...	...	...
195	196	Female	35	120	79
196	197	Female	45	126	28
197	198	Male	32	126	74
198	199	Male	32	137	18
199	200	Male	30	137	83

200 rows × 5 columns

為了視覺化聚類效果，僅選取Annual Income (k$)和Spending Score (1-100)這2個欄位

In [3]:

X = dataset.iloc[:, [3, 4]].values
X[:3, :]

Out[3]:

array([[15, 39],
       [15, 81],
       [16,  6]], dtype=int64)

 

3. 使用肘部法則選擇最優的K值

In [4]:

# 使用肘部法則選擇最優的K值
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', n_init=10, max_iter=300, random_state = 0)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)

In [5]:

# 畫出 聚類個數 vs WCSS 圖
plt.figure()
plt.plot(range(1, 11), wcss, 'ro-')
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

從K=5開始，WCSS下降的不再明顯，說明K=5是最優選擇

 

4. 使用K=5做聚類

In [6]:

# 使用選擇出的K，使用K-Means做聚類
kmeans = KMeans(n_clusters = 5, init = 'k-means++', n_init=10, max_iter=300, random_state = 0)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)

In [7]:

y_kmeans

Out[7]:

array([3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,
       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 0,
       3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 2, 0, 2, 4, 2, 4, 2,
       0, 2, 4, 2, 4, 2, 4, 2, 4, 2, 0, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,
       4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,
       4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,
       4, 2])

5. 視覺化聚類效果

In [8]:

# 視覺化聚類效果
plt.figure()
plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')
plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()

 

6. 採取措施

1. Cluster 1 工資收入中等，消費中等；
2. Cluster 2 工資收入低，消費高，檢視這個分組主要購買哪些商品；
3. Cluster 3 工資收入高，消費高；
4. Cluster 4 工資收入低，消費低；
5. Cluster 5 工資收入高，消費低，給這個分組的客戶辦理優惠券或打折購物卡，吸引他們消費；

 

7. 瑞士捲生產及其聚類

In [10]:

from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
from sklearn import manifold, datasets
import matplotlib.pyplot as plt
​

#生成帶噪聲的瑞士捲資料集
X,color = datasets.make_swiss_roll(n_samples=3000)
​
#使用100個K-means簇對資料進行近似
clusters_swiss_roll = KMeans(n_clusters=3,random_state=1).fit_predict(X)
​
fig2 = plt.figure(figsize=(10,10))

ax = fig2.add_subplot(111,projection='3d')
ax.scatter(X[:,0],X[:,1],X[:,2],c = clusters_swiss_roll,cmap = 'Spectral')

plt.show()

如上圖，根據距離將其聚成了3類，