银行客户认购产品预测

# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原
# View dataset directory. 
# This directory will be recovered automatically after resetting environment. 
!ls /home/aistudio/data
!pip install --upgrade pip
!pip install shap
!pip install numba --user --ignore-installed llvmlite
!pip install numba
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data178027
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前言

所给数据质量比较好，没有缺失值和重复值，但正负样本不均衡，模型使用了xgboost,lightgbm,catboost三个模型训练，结果lightgbm>xgboost>catboost。lightgbm加交叉验证可以达到0.97左右，xgboost在0.965左右，catboost在0.96左右。

一.项目背景

用户购买预测是数字化营销领域中的重要应用场景。在数字营销成为新常态下，数字营销向何处去本质上是由主力消费人群及其消费方式与偏好等决定的，因此消费品企业有必要重新认识后疫情时代下主流消费人群的行为特征及其变化。通过这个项目，鼓励学习者利用营销活动信息，为企业提供销售策略，也为消费者提供更适合的商品推荐。

本次项目以银行具体产品认购预测为背景，想让你来预测下客户是否会购买银行的产品。在和客户沟通的过程中，记录了和客户联系的次数，上一次联系的时长，上一次联系的时间间隔，同时在银行系统中保存了客户的基本信息，包括：年龄、职业、婚姻、之前是否有违约、是否有房贷等信息，此外所给数据集还统计了当前市场的情况：就业、消费信息、银行同业拆解率等。

• age : 年龄
• job : admin ,unknown，unemployed，management
• marital:婚姻:married,divorced,single
• default:信用卡是否违约:yes or no
• housing:是否有房贷:yes or no
• contact:联系方式:unkown,telephone,cellular
• month:上次联系的月份:jan,feb,mar,…
• day_of_week:上一次联系的星期几:mon,tue,wed,thu,fri,…
• duration:上一次联系的时长(秒)
• campaign:活动期间联系客户的次数
• pdays:上一次与客户联系后的间隔天数
• previous:在本次营销活动前，与客户联系的次数
• poutcome:之前营销活动的结果，unknown,other,failure,success
• emp_var_rate:就业变动率(季度指标)
• cons_price_index:消费者价格指数(月度指标)
• cons_conf_index:消费者信心指数（月度指标）
• lending_rate3m:银行同业拆解率3个月利率(每日指标)
• nr_employed:雇员人数(季度指标)
• subscribe:客户是否进行购买:yes or no
  评价标准：Accuracy(所有分类准确的百分比)

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二、数据探索

1.读取数据

import  pandas as pd
import  numpy  as np
df=pd.read_csv("train.csv")
test=pd.read_csv("test.csv")
df['subscribe'].value_counts()
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no     19548
yes     2952
Name: subscribe, dtype: int64
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目标变量比例失衡

查看数据统计量

df.describe().T
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# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢.
# View personal work directory. 
# All changes under this directory will be kept even after reset. 
# Please clean unnecessary files in time to speed up environment loading. 
!ls /home/aistudio/work
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duration分箱演示

import matplotlib.pyplot as  plt
import seaborn as sns
bins=[0,143,353,1873,4198]
df1=df[df['subscribe']=='yes']
binning=pd.cut(df1['duration'],bins,right=False)
time=pd.value_counts(binning)
#可视化
time=time.sort_index()
fig=plt.figure(figsize=(6,2),dpi=120)
sns.barplot(time.index,time,color='royalblue')
x=np.arange(len(time))
y=time.values
for x_loc,jobs in zip(x,y):
   plt.text(x_loc,jobs+2,'{:.1f}%'.format(jobs/sum(time)*100),ha='center',va='bottom',fontsize=8)
plt.xticks(fontsize=8)
plt.yticks([])
plt.ylabel('')
plt.title('duration_yes',size=8)
sns.despine(left=True)
plt.show()
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<Figure size 720x240 with 1 Axes>
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# 如果需要进行持久化安装, 需要使用持久化路径, 如下方代码示例:
# If a persistence installation is required, 
# you need to use the persistence path as the following: 
!mkdir /home/aistudio/external-libraries
!pip install beautifulsoup4 -t /home/aistudio/external-libraries
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可以看出时长对目标变量有一定的区分

3.查看数据分布

#分离数值变量与分类变量
Nu_feature=list(df.select_dtypes(exclude=['object']).columns)
Ca_feature=list(df.select_dtypes(include=['object']).columns)
#查看训练集和测试集数值变量分布
import matplotlib.pyplot as  plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
plt.figure(figsize=(20,15))
i=1
for col in Nu_feature:
     ax=plt.subplot(4,4,i)
     ax=sns.kdeplot(df[col],color='red')
     ax=sns.kdeplot(test[col],color='cyan')
     ax.set_xlabel(col)
     ax.set_ylabel('Frenquency')
     ax=ax.legend(['train','test'])
     i=i+1
plt.show()
#查看分类变量分布
Ca_feature.remove('subscribe')
col1=Ca_feature
plt.figure(figsize=(20,10))
j=1
for col in col1:
     ax=plt.subplot(4,5,j)
     ax=plt.scatter(x=range(len(df)),y=df[col],color='red') 
     plt.title(col)
     j=j+1
k=11
for  col in col1:
      ax=plt.subplot(4,5,k)
      ax=plt.scatter(x=range(len(test)),y=test[col],color='cyan')   
      plt.title(col)
      k=k+1
plt.subplots_adjust(wspace=0.4,hspace=0.3) 
plt.show()    
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4.数据相关图

from sklearn.preprocessing import LabelEncoder
lb=LabelEncoder()
cols=Ca_feature
for m in cols:
    df[m]=lb.fit_transform(df[m])
    test[m]=lb.fit_transform(test[m])
df['subscribe']=df['subscribe'].replace(['no','yes'],[0,1])
correlation_matrix=df.corr()   
plt.figure(figsize=(12,10))
sns.heatmap(correlation_matrix,vmax=0.9,linewidths=0.05,cmap='RdGy')
#相关性比较高的特征在模型特征输出部分也占据比较重要的
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<matplotlib.axes._subplots.AxesSubplot at 0x7fdcecdb9c50>
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三、数据建模

本次数据没有做任何特征工程，所以直接使用原始数据

from lightgbm.sklearn import LGBMClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection  import KFold
from sklearn.metrics import accuracy_score,auc,roc_auc_score
X=df.drop(columns=['RecordID','subscribe'])
Y=df['subscribe']
test=test.drop(columns='RecordID')
#划分训练及测试集
x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=1)
#建立模型
gbm=LGBMClassifier(n_estimators=600,learning_rate=0.01,boosting_type='gbdt',objective='binary',max_depth=-1,random_state=2022,metric='auc')
#交叉验证
result1=[]
mean_score1=0
n_fold=5
kf=KFold(n_splits=n_fold,shuffle=True,random_state=2022)
for train_index,test_index in kf.split(X):
    x_train=X.iloc[train_index]
    y_train=Y.iloc[train_index]
    x_test=X.iloc[test_index]
    y_test=Y.iloc[test_index]
    gbm.fit(x_train,y_train)
    y_pred1=gbm.predict_proba((x_test),num_iteration=gbm.best_iteration_)[:,1]
    print('验证集AUC:{}'.format(roc_auc_score(y_test,y_pred1)))
    mean_score1+=roc_auc_score(y_test,y_pred1)/n_fold
    y_pred_final1=gbm.predict_proba((test),num_iteration=gbm.best_iteration_)[:,1]
    y_pred_test=y_pred_final1
    result1.append(y_pred_final1)
#模型评估 
print('mean验证集auc:{}'.format(mean_score1))   
cat_pre1=sum(result1)/n_fold
ret1=pd.DataFrame(cat_pre1,columns=['subscribe'])
ret1['subscribe']=np.where(ret1['subscribe']>0.5,'yes','no').astype('str')
ret1.to_csv('测试.csv',index=False)



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验证集AUC:0.8926203815766213
验证集AUC:0.8896462215236163
验证集AUC:0.8795877825112728
验证集AUC:0.9017302955665025
验证集AUC:0.9012188887720168
mean验证集auc:0.892960713990006
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特征输出

import shap
explainer=shap.TreeExplainer(gbm)
shap_values=explainer.shap_values(X)
shap.summary_plot(shap_values,X,plot_type='bar',max_display=20)

(gbm)
shap_values=explainer.shap_values(X)
shap.summary_plot(shap_values,X,plot_type='bar',max_display=20)


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此文章为搬运
原项目链接