KMeans聚类-credit_card

Data columns (total 18 columns):
CUST_ID                             8950 non-null object
BALANCE                             8950 non-null float64
BALANCE_FREQUENCY                   8950 non-null float64
PURCHASES                           8950 non-null float64
ONEOFF_PURCHASES                    8950 non-null float64
INSTALLMENTS_PURCHASES              8950 non-null float64
CASH_ADVANCE                        8950 non-null float64
PURCHASES_FREQUENCY                 8950 non-null float64
ONEOFF_PURCHASES_FREQUENCY          8950 non-null float64
PURCHASES_INSTALLMENTS_FREQUENCY    8950 non-null float64
CASH_ADVANCE_FREQUENCY              8950 non-null float64
CASH_ADVANCE_TRX                    8950 non-null int64
PURCHASES_TRX                       8950 non-null int64
CREDIT_LIMIT                        8949 non-null float64
PAYMENTS                            8950 non-null float64
MINIMUM_PAYMENTS                    8637 non-null float64
PRC_FULL_PAYMENT                    8950 non-null float64
TENURE                              8950 non-null int64

一、加载库

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.metrics import calinski_harabaz_score
import matplotlib as mpl
import matplotlib.pyplot as plt
# 设置字体为黑体，以支持中文显示。
mpl.rcParams["font.family"] = "SimHei"
# 设置在中文字体时，能够正常的显示负号（-）。
mpl.rcParams["axes.unicode_minus"] = False

二、数据预处理

# 加载数据集
data = pd.read_csv(r"credit_card.csv",header=0)
#data.info()
# 查看是否含有异常值
#data.describe()
# 检查是否包含重复值
#ata.duplicated().any()
# 如果有重复值，可以这样去除重复值
# data.drop_duplicates(inplace=True)
data.drop(["CUST_ID","MINIMUM_PAYMENTS","CREDIT_LIMIT"],axis=1,inplace = True)
#data.head()
#data.info()

X = data
plt.scatter(X.values[:, 2], X.values[:, 5], marker='o')
plt.show()

三、调用KMeans方法

for k in range(2,8):
    #实例化线性回归模型
    km=KMeans(n_clusters=k,random_state=0)
    #训练模型
    result = km.fit_predict(X)
    
    plt.figure(figsize=(12, 8))
    plt.subplots(1, 1)
    plt.scatter(X.values[:, 2], X.values[:, 5], c=result)
    plt.scatter(km.cluster_centers_[:, 2], km.cluster_centers_[:, 5], marker="+", s=100)
    score = calinski_harabaz_score(X, result) 
    print("For n_clusters =", k,
    "The calinski_harabaz_score is :", score)

四、结果可视化

km=KMeans(n_clusters=5,random_state=0)
result = km.fit_predict(X)

plt.figure(figsize=(12, 8))
plt.scatter(X.values[:, 2], X.values[:, 5], c=result)
plt.scatter(km.cluster_centers_[:, 2], km.cluster_centers_[:, 5], marker="+", s=100)

plt.show()

KMeans聚类-credit_card

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