数据挖掘3

1~5、

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

import pandas as pd
datafile = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/air_data.csv'
resultfile = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/explore.csv'

data = pd.read_csv(datafile,encoding = 'utf-8')

explore = data.describe(percentiles=[],include = 'all').T
explore['null'] = len(data)-explore['count']

explore = explore[['null','max','min']]
explore.colums = [u'空数值',u'最大值',u'最小值']

data.describe

explore.to_csv(resultfile)

import matplotlib.pyplot as plt
from datetime import datetime
ffp = data['FFP_DATE'].apply(lambda x:datetime.strptime(x,'%Y/%m/%d'))
ffp_year = ffp.map(lambda x : x.year)

fig = plt.figure(figsize = (8,5))
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False
plt.hist(ffp_year,bins='auto',color='#0504aa')
plt.xlabel('年份')
plt.ylabel('入会人数')
plt.title('各年份会员入户人数3023')
plt.show()
plt.close

male = pd.value_counts(data['GENDER'])['男']
female = pd.value_counts(data['GENDER'])['女']

fig = plt.figure(figsize=(7,4))
plt.pie([male,female],labels=['男','女'],colors=['lightskyblue','lightcoral'],autopct='%1.1f%%')
plt.title('会员性别比例3023')
plt.show()
plt.close

lv_four = pd.value_counts(data['FFP_TIER'])[4]
lv_five = pd.value_counts(data['FFP_TIER'])[5]
lv_six = pd.value_counts(data['FFP_TIER'])[6]

fig = plt.figure(figsize=(8,5))
plt.bar(x=range(3),height=[lv_four,lv_five,lv_six],width=0.4,alpha=0.8,color='skyblue')
plt.xticks([index for index in range(3)],['4','5','6'])
plt.xlabel('会员等级')
plt.ylabel('会员人数')
plt.title('会员各级别人数3023')
plt.show()
plt.close

age = data['AGE'].dropna()
age = age.astype('int64')

fig = plt.figure(figsize=(5,10))
plt.boxplot(age,
patch_artist=True,
labels = ['会员年龄'],
boxprops = {'facecolor':'lightblue'})
plt.title('会员年龄分布箱型图3023')

plt.grid(axis='y')
plt.show()
plt.close

lte = data['LAST_TO_END']
fc = data['FLIGHT_COUNT']
sks = data['SEG_KM_SUM']

fig = plt.figure(figsize=(5,8))

plt.boxplot(lte,
patch_artist=True,
labels = ['时长'],
boxprops = {'facecolor':'lightblue'})
plt.title('会员最后乘机至结束时长分布箱型图3023')

plt.grid(axis='y')
plt.show()
plt.close

fig = plt.figure(figsize=(5,8))
plt.boxplot(fc,
patch_artist=True,
labels = ['飞行次数'],
boxprops = {'facecolor':'lightblue'})
plt.title('会员飞行次数分布箱型图3023')

plt.grid(axis='y')
plt.show()
plt.close

fig = plt.figure(figsize=(5,10))
plt.boxplot(sks,
patch_artist=True,
labels = ['总飞行公里数'],
boxprops = {'facecolor':'lightblue'})
plt.title('客户总飞行公里数箱型图3023')

plt.grid(axis='y')
plt.show()
plt.close

ec = data['EXCHANGE_COUNT']

fig = plt.figure(figsize=(8,5))
plt.hist(ec, bins=5,color='#0504aa')
plt.xlabel('兑换次数')
plt.ylabel('会员人数')
plt.title('会员兑换积分次数分布直方图3023')
plt.show()
plt.close()

ps = data['Points_Sum']

fig = plt.figure(figsize=(5,8))
plt.boxplot(ps,
patch_artist=True,
labels = ['总累计积分'],
boxprops = {'facecolor':'lightblue'})
plt.title('客户总累计积分箱型图3023')

plt.grid(axis='y')
plt.show()
plt.close

data_corr = data[['FFP_TIER','FLIGHT_COUNT','LAST_TO_END','SEG_KM_SUM','EXCHANGE_COUNT','Points_Sum']]
agel = data['AGE'].fillna(0)
data_corr['AGE'] = agel.astype('int64')
data_corr['ffp_year'] = ffp_year

dt_corr = data_corr.corr(method='pearson')
print('相关性助阵为：\n',dt_corr)

import seaborn as sns
plt.subplots(figsize=(10,10))
sns.heatmap(dt_corr,annot=True,vmax=1,square=True,cmap='Blues')
plt.title('热力图3023')
plt.show()
plt.close

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 6~8、

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 8 11:32:28 2023

@author: admin
"""

import numpy as np
import pandas as pd

datafile = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/air_data.csv'
cleanedfile = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/data_cleaned.csv'

airline_data = pd.read_csv(datafile,encoding = 'utf-8')
print('原始数据的形状为：',airline_data.shape)

airline_notnull = airline_data.loc[airline_data['SUM_YR_1'].notnull() &
airline_data['SUM_YR_2'].notnull(),:]
print('删除缺失记录后数据的形状为：',airline_notnull.shape)

index1 = airline_notnull['SUM_YR_1'] != 0
index2 = airline_notnull['SUM_YR_2'] != 0
index3 = (airline_notnull['SEG_KM_SUM']>0) & (airline_notnull['avg_discount'] != 0)
index4 = airline_notnull['AGE'] > 100
airline = airline_notnull[(index1 | index2) & index3 & ~index4]
print('数据清洗后数据的形状为：',airline.shape)

airline.to_csv(cleanedfile)

cleanedfile = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/data_cleaned.csv'
airline = pd.read_csv(cleanedfile,encoding='utf-8')

airline_selection = airline[['FFP_DATE','LOAD_TIME','LAST_TO_END',
'FLIGHT_COUNT','SEG_KM_SUM','avg_discount']]
print('筛选的属性前5行为：\n',airline_selection.head())

L = pd.to_datetime(airline_selection['LOAD_TIME']) - \
pd.to_datetime(airline_selection['FFP_DATE'])
L = L.astype('str').str.split().str[0]
L = L.astype('int')/30

airline_features = pd.concat([L,airline_selection.iloc[:,2:]],axis=1)
print('构建的LRFMC属性前5行为：\n',airline_features.head())

from sklearn.preprocessing import StandardScaler
data = StandardScaler().fit_transform(airline_features)
np.savez('D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/airline_scale.npz',data)
print('标准化后LRFMC5个属性为：\n',data[:5,:])

 

 

 9~10、

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 8 12:59:31 2023

@author: admin
"""

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans

airline_scale = np.load('D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/airline_scale.npz')['arr_0']
k = 5

kmeans_model = KMeans(n_clusters=k,n_init=4,random_state=123)
fit_kmeans = kmeans_model.fit(airline_scale)

kmeans_cc = kmeans_model.cluster_centers_
print('各类聚类中心为：\n',kmeans_cc)
kmeans_labels = kmeans_model.labels_
print('各样本的类别标签为：\n',kmeans_labels)
r1 = pd.Series(kmeans_model.labels_).value_counts()
print('最终每个类别的数目为：\n',r1)

cluster_center = pd.DataFrame(kmeans_model.cluster_centers_,\
columns = ['ZL','ZR','ZF','ZM','ZC'])
cluster_center.index = pd.DataFrame(kmeans_model.labels_).\
drop_duplicates().iloc[:,0]
print(cluster_center)

import matplotlib.pyplot as plt

labels = ['ZL','ZR','ZF','ZM','ZC']
legen = ['客户群' + str(i+1) for i in cluster_center.index]
lstype = ['-','--',(0,(3,5,1,5,1,5)),':','-.']
kinds = list(cluster_center.iloc[:,0])

cluster_center = pd.concat([cluster_center,cluster_center[['ZL']]],axis=1)
centers = np.array(cluster_center.iloc[:,0:])

n = len(labels)
angle = np.linspace(0,2*np.pi,n,endpoint=False)
angle = np.concatenate((angle,[angle[0]]))

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111,polar=True)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

for i in range(len(kinds)):
ax.plot(angle,centers[i],linestyle=lstype[i],linewidth=2,label=kinds[i])
ang=angle*180/np.pi
ax.set_thetagrids(ang[:-1],labels)
plt.title('客户特征分析雷达图3023')
plt.legend(legen)
plt.show()
plt.close

 客户分析、

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 8 13:22:42 2023

@author: admin
"""

import numpy as np
import pandas as pd
datafile='D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/air_data.csv'
cleanedfile='D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/data_cleaned.csv'
airline_data=pd.read_csv(datafile,encoding='utf-8')
print('原始数据的形状为：',airline_data.shape)
airline_notnull=airline_data.loc[airline_data['SUM_YR_1'].notnull()&airline_data['SUM_YR_2'].notnull(),:]
print('删除缺失记录后数据的形状为：\n',airline_notnull.shape)

airline_data['单位里程票价'] = (airline_data['SUM_YR_1'] + airline_data['SUM_YR_2'])/airline_data['SEG_KM_SUM']
airline_data['单位里程积分'] = (airline_data['P1Y_BP_SUM'] + airline_data['L1Y_BP_SUM'])/airline_data['SEG_KM_SUM']
airline_data['飞行次数比例'] = airline_data['L1Y_Flight_Count'] / airline_data['P1Y_Flight_Count'] 
airline_data = airline_data[airline_data['FLIGHT_COUNT'] > 6]
airline_data = airline_data[['FFP_TIER','飞行次数比例','AVG_INTERVAL',
'avg_discount','EXCHANGE_COUNT','Eli_Add_Point_Sum','单位里程票价','单位里程积分']]
airline_data.to_csv(cleanedfile,index=None)

input_file = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/data_cleaned.csv'
output_file = 'D:/WeixinWenjian/WeChat Files/wxid_5onnacvxxvpj22/FileStorage/File/2023-03/data_cleaned3.csv'
data = pd.read_csv(input_file,encoding='utf-8')
data['客户类型'] = None
for i in range(len(data)):
if data['飞行次数比例'][i] < 0.5:
data['客户类型'][i] = '已流失' 
if (data['飞行次数比例'][i] >= 0.5) & (data['飞行次数比例'][i] < 0.9) :
data['客户类型'][i] = '准流失' 
if data['飞行次数比例'][i] >= 0.9:
data['客户类型'][i] = '未流失' 
data.to_csv(output_file,index=None)
print('筛选的属性前5行为：\n',data.head())