Data analysis - user clustering analysis

Do clustering analysis of characteristics of the user groups, but also somewhat similar to RFM models, but only an optional indicator selection RFM more than three indicators, where data is used airline users' data, including data index

On the following codes:

Import PANDAS PD AS 
Data = pd.read_csv ( ' air_data.csv ' )
 # some basic data 
data.describe ()

# Data null value, the null properties will be found that some are more 
data.isnull (). Sum (). Sort_values (ascending = False) .head (10)

Up to a few null are as follows:

#查找每列数据的空值数量，最大值，最小值情况
max_data = data.max()
min_data = data.min()
null_data = data.isnull().sum()
data_count = pd.DataFrame({'max_data':max_data,'min_data':min_data,'null_data':null_data})

#做数据清洗
#丢弃票价为空值的的数据
data = data[data['SUM_YR_1'].notnull()*data['SUM_YR_2'].notnull()]
#data.dropna(subset=['SUM_YR_2','SUM_YR_1'])

#只选择票价不为0,或者折扣
index1 = data['SUM_YR_1'] != 0 
index2 = data['SUM_YR_2'] != 0
index3 = (data['SEG_KM_SUM'] == 0) & (data['avg_discount'] == 0) 
data = data[index1|index2|index3]

#计算LRFMC五个指标
data['FFP_DATE'] = pd.to_datetime(data['FFP_DATE'])
data['LOAD_TIME'] = pd.to_datetime(data['LOAD_TIME'])
data['L'] = data['LOAD_TIME'] - data['FFP_DATE']  
data['R'] = data['LAST_TO_END']
data['F'] = data['FLIGHT_COUNT']
data['M'] = data['SEG_KM_SUM']
data['C'] = data['avg_discount']

finall_data = data.loc[:,['L','R','F','M','C']]
finall_data['L'] = finall_data['L'].dt.days   #转换成天
#标准化
finall_data = (finall_data - finall_data.mean(axis=0))/finall_data.std()

#聚类分析
from sklearn.cluster import KMeans
model = KMeans(n_clusters=5)
model.fit(finall_data)
model.cluster_centers_
model.labels_
finall_data['label'] = model.labels_  
center = pd.DataFrame(center,columns=finall_data.columns[:-1])

最后几类用户几个指标的分布如下，可以有针对性的做营销

针对用户群体的特征做分群分析，也有点类似RFM模型一样，不过可选的指标比只选择RFM三个指标更多，这里用的数据是航空公司用户的数据，数据指标包括

下面上代码：

import pandas as pd
data = pd.read_csv('air_data.csv')
#数据的一些基本情况
data.describe()

#数据空值情况，会发现一些属性的空值比较多
data.isnull().sum().sort_values(ascending = False).head(10)

空值最多的几个列如下：

#查找每列数据的空值数量，最大值，最小值情况
max_data = data.max()
min_data = data.min()
null_data = data.isnull().sum()
data_count = pd.DataFrame({'max_data':max_data,'min_data':min_data,'null_data':null_data})

#做数据清洗
#丢弃票价为空值的的数据
data = data[data['SUM_YR_1'].notnull()*data['SUM_YR_2'].notnull()]
#data.dropna(subset=['SUM_YR_2','SUM_YR_1'])

#只选择票价不为0,或者折扣
index1 = data['SUM_YR_1'] != 0 
index2 = data['SUM_YR_2'] != 0
index3 = (data['SEG_KM_SUM'] == 0) & (data['avg_discount'] == 0) 
data = data[index1|index2|index3]

#计算LRFMC五个指标
data['FFP_DATE'] = pd.to_datetime(data['FFP_DATE'])
data['LOAD_TIME'] = pd.to_datetime(data['LOAD_TIME'])
data['L'] = data['LOAD_TIME'] - data['FFP_DATE']  
data['R'] = data['LAST_TO_END']
data['F'] = data['FLIGHT_COUNT']
data['M'] = data['SEG_KM_SUM']
data['C'] = data['avg_discount']

finall_data = data.loc[:,['L','R','F','M','C']]
finall_data['L'] = finall_data['L'].dt.days   #转换成天
#标准化
finall_data = (finall_data - finall_data.mean(axis=0))/finall_data.std()

#聚类分析
from sklearn.cluster import KMeans
model = KMeans(n_clusters=5)
model.fit(finall_data)
model.cluster_centers_
model.labels_
finall_data['label'] = model.labels_  
center = pd.DataFrame(center,columns=finall_data.columns[:-1])

最后几类用户几个指标的分布如下，可以有针对性的做营销

Data analysis - user clustering analysis

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