【机器学习小案例篇】关于RFM模型的小案例

import pandas as pd

1. 导入数据

In [75]:

trad_flow = pd.read_csv(D:pythonScriptRFM_TRAD_FLOW.csv,encoding=gbk)  #编码格式需要进行修改
trad_flow.head() #默认前五行

Out[75]:

transID cumid time amount type_label type 0 9407 10001 14JUN09:17:58:34 199.0 正常 Normal 1 9625 10001 16JUN09:15:09:13 369.0 正常 Normal 2 11837 10001 01JUL09:14:50:36 369.0 正常 Normal 3 26629 10001 14DEC09:18:05:32 359.0 正常 Normal 4 30850 10001 12APR10:13:02:20 399.0 正常 Normal

2.计算 RFM

In [21]:

M = trad_flow.groupby([cumid,type])[[amount]].sum()

In [48]:

trains_M = pd.pivot_table(M,index=cumid,columns=type,values=amount)
trains_M.head()

Out[48]:

type Normal Presented Special_offer returned_goods cumid 10001 3608.0 0.0 420.0 -694.0 10002 1894.0 0.0 NaN -242.0 10003 3503.0 0.0 156.0 -224.0 10004 2979.0 0.0 373.0 -40.0 10005 2368.0 0.0 NaN -249.0

In [47]:

F = trad_flow.groupby([cumid,type])[[transID]].count()
F.head()

Out[47]:

transID cumid type 10001 Normal 15 Presented 8 Special_offer 2 returned_goods 2 10002 Normal 12

In [46]:

R = trad_flow.groupby([cumid,type])[[time]].max()
R.head()

Out[46]:

time cumid type 10001 Normal 21JUL09:09:31:26 Presented 31MAR10:20:29:48 Special_offer 12OCT09:10:59:13 returned_goods 10JUL10:20:41:54 10002 Normal 29JUL09:19:21:41

3.衡量客户对打折商品的偏好

In [53]:

trains_M[Special_offer] = trains_M[Special_offer].fillna(0)
trains_M[Special_offer].head()

Out[53]:

cumid
10001    420.0
10002      0.0
10003    156.0
10004    373.0
10005      0.0
Name: Special_offer, dtype: float64

In [67]:

trains_M[spe_ratio] = trains_M[Special_offer]/(trains_M[Special_offer]+trains_M[Normal])
trains_M[spe_ratio].head()

Out[67]:

cumid
10001    0.104270
10002    0.000000
10003    0.042635
10004    0.111277
10005    0.000000
Name: spe_ratio, dtype: float64

In [68]:

M_rank = trains_M.sort_values(spe_ratio,ascending=False).head()
M_rank.head()

Out[68]:

type Normal Presented Special_offer returned_goods spe_ratio cumid 10151 765.0 0.0 870.0 NaN 0.532110 40033 1206.0 0.0 761.0 -848.0 0.386884 40236 1155.0 0.0 691.0 -793.0 0.374323 30225 1475.0 0.0 738.0 -301.0 0.333484 20068 1631.0 0.0 731.0 -239.0 0.309483

In [74]:

pd.qcut(M_rank[spe_ratio],4)

Out[74]:

cumid
10151    (0.387, 0.532]
40033    (0.374, 0.387]
40236    (0.333, 0.374]
30225    (0.308, 0.333]
20068    (0.308, 0.333]
Name: spe_ratio, dtype: category
Categories (4, interval[float64]): [(0.308, 0.333] < (0.333, 0.374] < (0.374, 0.387] < (0.387, 0.532]]

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数据链接：