数据挖掘实战案例—

数据挖掘实战案例——客户细分

article/2025/10/30 21:23:52

我们首先来看看原始数据
具体代码与原始数据可以在我的GitHub中git clone一下学习
代码与数据github地址
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共26663条数据
此次分析是想对根据客户的购买情况对客户进行细分
分析用户的购买消费行为

首先读取csv

import pandas as pd
#import numpy as np
trad_flow = pd.read_csv('./购买情况.csv', encoding='gbk')
trad_flow.head(10)

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RFM方法建立模型

计算M反应客户的价值信息

M=trad_flow.groupby(['cumid','type'])[['amount']].sum()
M.head()
M_trans=pd.pivot_table(M,index='cumid',columns='type',values='amount')
M_trans['Special_offer']= M_trans['Special_offer'].fillna(0)
M_trans['returned_goods']= M_trans['returned_goods'].fillna(0)
M_trans["value"]=M_trans['Normal']+M_trans['Special_offer']+M_trans['returned_goods']
M_trans.head()

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定义一个从文本转化为时间的函数

from datetime import datetime
import time
def to_time(t):out_t=time.mktime(time.strptime(t, '%d%b%y:%H:%M:%S'))   ########此处修改为时间戳方便后面qcut函数分箱return out_t
a="14JUN09:17:58:34"
print(to_time(a))
# In[8]
trad_flow["time_new"]= trad_flow.time.apply(to_time)
trad_flow.head()
# In[7]:
R=trad_flow.groupby(['cumid'])[['time_new']].max()
R.head()

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构建模型，筛选目标客户

from sklearn import preprocessing
threshold = pd.qcut(F_trans['interest'], 2, retbins=True)[1][1]
binarizer = preprocessing.Binarizer(threshold=threshold)
interest_q = pd.DataFrame(binarizer.transform(F_trans['interest'].values.reshape(-1, 1)))
interest_q.index=F_trans.index
interest_q.columns=["interest"]
# In[12]
threshold = pd.qcut(M_trans['value'], 2, retbins=True)[1][1]
binarizer = preprocessing.Binarizer(threshold=threshold)
value_q = pd.DataFrame(binarizer.transform(M_trans['value'].values.reshape(-1, 1)))
value_q.index=M_trans.index
value_q.columns=["value"]
# In[12]
threshold = pd.qcut(R["time_new"], 2, retbins=True)[1][1]
binarizer = preprocessing.Binarizer(threshold=threshold)
time_new_q = pd.DataFrame(binarizer.transform(R["time_new"].values.reshape(-1, 1)))
time_new_q.index=R.index
time_new_q.columns=["time"]
# In[12]
analysis=pd.concat([interest_q, value_q,time_new_q], axis=1)
# In[12]
#analysis['rank']=analysis.interest_q+analysis.interest_q
analysis = analysis[['interest','value','time']]
analysis.head()label = {(0,0,0):'无兴趣-低价值-沉默',(1,0,0):'有兴趣-低价值-沉默',(1,0,1):'有兴趣-低价值-活跃',(0,0,1):'无兴趣-低价值-活跃',(0,1,0):'无兴趣-高价值-沉默',(1,1,0):'有兴趣-高价值-沉默',(1,1,1):'有兴趣-高价值-活跃',(0,1,1):'无兴趣-高价值-活跃'
}
analysis['label'] = analysis[['interest','value','time']].apply(lambda x: label[(x[0],x[1],x[2])], axis = 1)
print(analysis)