#-*-coding:utf-8-*-
import  numpy  as np
import  matplotlib.pyplot as plt
import pandas as pd
import matplotlib; matplotlib.use('TkAgg')
dataset=pd.read_csv(r'Customer_Info.csv')
print(dataset)
X=dataset.iloc[:,[4,3]].values
from sklearn.cluster import  KMeans
sumDs=[]
for i in range(1,11):
    kmeans=KMeans(n_clusters=i)
    kmeans.fit(X)
    sumDs.append(kmeans.inertia_)
    print(kmeans.inertia_)
plt.plot(range(1,11),sumDs)
plt.title('the Elbow method')
plt.xlabel('number of cluster k')
plt.ylabel('SSE')
plt.show()
kmenas1=KMeans(n_clusters=3,init='k-means++',max_iter=300,n_init=10,random_state=0)
y_kmeans=kmenas1.fit_predict(X)
plt.scatter(X[y_kmeans==0,0],X[y_kmeans==0,1],s=100,marker='^',c='red',label='poor')
plt.scatter(X[y_kmeans==2,0],X[y_kmeans==2,1],s=100,marker='o',c='green',label='middle')
plt.scatter(X[y_kmeans==1,0],X[y_kmeans==1,1],s=100,marker='*',c='blue',label='rich')
plt.scatter(kmenas1.cluster_centers_[:,0],kmenas1.cluster_centers_[:,1],s=250,c='yellow',label='Centroids')
plt.title('clusters of customer info')
plt.xlabel('deposit')
plt.ylabel('age')
plt.legend()
plt.show()