python航空公司客戶流失預測

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
import warnings
warnings.filterwarnings('ignore')

from scipy import stats
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE 
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, KFold
from sklearn.feature_selection import RFE 
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, VotingClassifier, ExtraTreesClassifier
import xgboost as xgb
from sklearn import metrics
import prettytable

data = pd.read_csv('E:/桌面/data/WA_Fn-UseC_-Telco-Customer-Churn.csv',engine="python")
data.head().T

data.drop("customerID", axis=1, inplace=True)

# 转换成连续型变量
data['TotalCharges'] = pd.to_numeric(data.TotalCharges, errors='coerce')
# 查看是否存在缺失值
data['TotalCharges'].isnull().sum()

# 查看缺失值分布
data.loc[data['TotalCharges'].isnull()].T

data.query("tenure == 0").shape[0]

data = data.query("tenure != 0")
# 重置索引
data = data.reset_index().drop('index',axis=1)  

# 查看各类别特征频数
for i in data.select_dtypes(include="object").columns:
    print(data[i].value_counts())
    print('-'*50)

data.Churn = data.Churn.map({'No':0,'Yes':1})

fig, ax= plt.subplots(nrows=2, ncols=3, figsize = (20,8))
for i, feature in enumerate(['tenure','MonthlyCharges','TotalCharges']):
    data.loc[data.Churn == 1, feature].hist(ax=ax[0][i], bins=30)
    data.loc[data.Churn == 0, feature].hist(ax=ax[1][i], bins=30, )
    ax[0][i].set_xlabel(feature+' Churn=0')
    ax[1][i].set_xlabel(feature+' Churn=1')

plt.title("3118")

　　

　　

 

 

data['TotalCharges_diff'] = data.tenure * data.MonthlyCharges - data.TotalCharges

def func(x):
    if x > 0:
        res = 2  # 2表示月费增加
    elif x == 0:
        res = 1  # 1表示月费持平
    else:
        res = 0  # 0表示月费减少
    return res
data['TotalCharges_diff1'] = data['TotalCharges_diff'].apply(lambda x:func(x))
data.drop('TotalCharges_diff', axis=1, inplace=True)

data['tenure'] = pd.qcut(data['tenure'], q=5, labels=['tenure_'+str(i) for i in range(1,6)])
data['MonthlyCharges'] = pd.qcut(data['MonthlyCharges'], q=5, labels=['MonthlyCharges_'+str(i) for i in range(1,6)])
data['TotalCharges'], _ = stats.boxcox(data['TotalCharges'])

X = data[data.columns.drop('Churn')]
y = data.Churn

# 生成哑变量
X = pd.get_dummies(X)

# 标准化
scaler = StandardScaler()
scale_data = scaler.fit_transform(X)
X = pd.DataFrame(scale_data, columns = X.columns)

y.value_counts()

model_smote = SMOTE(random_state=10)  # 建立SMOTE模型对象
X_smote, y_smote = model_smote.fit_resample(X, y)

y_smote.value_counts()

X_smote.shape[1]

# etc = ExtraTreesClassifier(random_state=9)  # ExtraTree，用于EFE的模型对象
# selector = RFE(etc, 30)
# selected_data = selector.fit_transform(X_smote, y_smote)  # 训练并转换数据
# X_smote = pd.DataFrame(selected_data, columns = X_smote.columns[selector.get_support()])

X_train, X_test, y_train, y_test = train_test_split(X_smote, y_smote, stratify=y_smote, random_state=11)


# 交叉验证输出f1得分
def score_cv(model, X, y):
    kfold = KFold(n_splits=5, random_state=42, shuffle=True)
    f1 = cross_val_score(model, X, y, scoring='f1', cv=kfold).mean()
    return f1


# 网格搜索
def gridsearch_cv(model, test_param, cv=5):
    gsearch = GridSearchCV(estimator=model, param_grid=test_param, scoring='f1', n_jobs=-1, cv=cv)
    gsearch.fit(X_train, y_train)
    print('Best Params: ', gsearch.best_params_)
    print('Best Score: ', gsearch.best_score_)
    return gsearch.best_params_


# 输出预测结果及混淆矩阵等相关指标
def model_pred(model):
    model.fit(X_train, y_train)
    pred = model.predict(X_test)
    print('test f1-score: ', metrics.f1_score(y_test, pred))
    print('-' * 50)
    print('classification_report \n', metrics.classification_report(y_test, pred))
    print('-' * 50)
    tn, fp, fn, tp = metrics.confusion_matrix(y_test, pred).ravel()  # 获得混淆矩阵
    confusion_matrix_table = prettytable.PrettyTable(['', 'actual-1', 'actual-0'])  # 创建表格实例
    confusion_matrix_table.add_row(['prediction-1', tp, fp])  # 增加第一行数据
    confusion_matrix_table.add_row(['prediction-0', fn, tn])  # 增加第二行数据
    print('confusion matrix \n', confusion_matrix_table)

lr = LogisticRegression(random_state=10)
lr_f1 = score_cv(lr, X_train, y_train)
lr_f1
model_pred(lr)

　　

 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
datafile='E:/桌面/air_data.csv'
resultfile='E:/桌面/data/explore.csv'
data=pd.read_csv(datafile,encoding='utf-8',engine='python')
explore=data.describe(percentiles=[],include='all').T
explore['null']=len(data)-explore['count']
explore=explore[['null','max','min']]
explore.columns=[u'空值数',u'最大值',u'最小值']
explore.to_csv(resultfile)

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('各年份会员入会人数3118')
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3118')
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('3118',fontsize=20)
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('3118',fontsize=20)
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3118')
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3118')
plt.grid(axis='y')
plt.show()
plt.close

　　

 

 

fig=plt.figure(figsize=(5,8))
plt.boxplot(sks,patch_artist=True,labels=['总飞行公里数数'],boxprops={'facecolor':'lightblue'})
plt.title('客户总飞行公里数分布箱型图3118')
plt.grid(axis='y')
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('3118',fontsize=20)
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']]
age1 = data['AGE'].fillna(0)
data_corr['AGE'] = age1.astype('int64')
data_corr['ffp_year'] = ffp_year
dt_corr = data_corr.corr(method='pearson')
print('相关性矩阵为：\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("3118")
plt.show()
plt.close

　　

 

 

airline_data = pd.read_csv('E:/桌面/air_data.csv',engine="python")
resultfile = 'E:/桌面/data/data_cleaned.csv'
airline_notnull = airline_data.loc[airline_data['SUM_YR_1'].notnull() &
                                   airline_data['SUM_YR_2'].notnull(),:]
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]
airline.to_csv(resultfile)


airline = pd.read_csv('E:/桌面/data/data_cleaned.csv',engine="python")
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
# L = L.astype('str')
airline_features = pd.concat([L,airline_selection.iloc[:,2:]], axis=1)
airline_features.columns = airline_features.columns.astype(str)
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('E:/桌面/data/airline_scale.npz',data)
print('标准化后LRFMC 5个属性为：\n',data[:5,:])

　　

 

 

from sklearn.cluster import KMeans
airline_scale = np.load('E:/桌面/data/airline_scale.npz')['arr_0']
k = 5
kmeans_model = KMeans(n_clusters=k,  random_state=123)
fit_kmeans = kmeans_model.fit(airline_scale)
kmeans_cc = kmeans_model.cluster_centers_
kmeans_labels = kmeans_model.labels_
r1 = pd.Series(kmeans_model.labels_).value_counts()
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)



#%matplotlib inline
labels = ['ZL','ZR','ZF','ZM','ZC']
# labels = labels.append('ZL')
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)
# print([angle[0]])
angle = np.concatenate((angle, [angle[0]]))
labels = np.concatenate((labels, [labels[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
print(cluster_center)
print(centers)
print(angle)

for i in range(len(kinds)):
    ax.plot(angle, centers[i], linestyle=lstype[i], linewidth=2, label=kinds[i])
# ax.plot(angle, centers, linestyle=lstype[i], linewidth=2, label=kinds[i])
ax.set_thetagrids(angle*180/np.pi,labels)
plt.title('3118',fontsize=20)
plt.legend(legen)
plt.show()
plt.close