利用Pipeline(客户年龄/职业/婚姻/教育/违约/余额/住房等)预测客户是否购买该银行的产品二分类(预测、推理)
数据说明
该数据集是葡萄牙银行机构进行营销活动所得。这些营销活动一般以电话为基础，银行的客服人员至少联系客户一次，以确认客户是否有意愿购买该银行的产品（定期存款）。目标是预测客户是否购买该银行的产品。
NO 字段名称 数据类型 字段描述
1 ID Int 客户唯一标识
2 age Int 客户年龄
3 job String 客户的职业
4 marital String 婚姻状况
5 education String 受教育水平
6 default String 是否有违约记录
7 balance Int 每年账户的平均余额
8 housing String 是否有住房贷款
9 loan String 是否有个人贷款
10 contact String 与客户联系的沟通方式
11 day Int 最后一次联系的时间（几号）
12 month String 最后一次联系的时间（月份）
13 duration Int 最后一次联系的交流时长
14 campaign Int 在本次活动中，与该客户交流过的次数
15 pdays Int 距离上次活动最后一次联系该客户，过去了多久（999表示没有联系过）
16 previous Int 在本次活动之前，与该客户交流过的次数
17 poutcome String 上一次活动的结果
18 y Int 预测客户是否会订购定期存款业务
数据参考：Citation: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
输出结果
查看数据分布
分析数据
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ID 25317 non-null int64
1 age 25317 non-null int64
2 job 25317 non-null object
3 marital 25317 non-null object
4 education 25317 non-null object
5 default 25317 non-null object
6 balance 25317 non-null int64
7 housing 25317 non-null object
8 loan 25317 non-null object
9 contact 25317 non-null object
10 day 25317 non-null int64
11 month 25317 non-null object
12 duration 25317 non-null int64
13 campaign 25317 non-null int64
14 pdays 25317 non-null int64
15 previous 25317 non-null int64
16 poutcome 25317 non-null object
17 y 25317 non-null int64
dtypes: int64(9), object(9)
memory usage: 3.5+ MB
训练集计算相关系数：
y 1.000000
ID 0.556627
duration 0.394746
pdays 0.107565
previous 0.088337
campaign 0.075173
balance 0.057564
day 0.031886
age 0.029916
训练集 y标签的比例： 0.11695698542481336
依次查看训练集、测试集中，类别型字段的细分类
job ['admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown']
job ['admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown']
marital ['divorced', 'married', 'single']
marital ['divorced', 'married', 'single']
education ['primary', 'secondary', 'tertiary', 'unknown']
education ['primary', 'secondary', 'tertiary', 'unknown']
default ['no', 'yes']
default ['no', 'yes']
housing ['no', 'yes']
housing ['no', 'yes']
loan ['no', 'yes']
loan ['no', 'yes']
contact ['cellular', 'telephone', 'unknown']
contact ['cellular', 'telephone', 'unknown']
month ['apr', 'aug', 'dec', 'feb', 'jan', 'jul', 'jun', 'mar', 'may', 'nov', 'oct', 'sep']
month ['apr', 'aug', 'dec', 'feb', 'jan', 'jul', 'jun', 'mar', 'may', 'nov', 'oct', 'sep']
poutcome ['failure', 'other', 'success', 'unknown']
poutcome ['failure', 'other', 'success', 'unknown']
输出训练过程
Fitting 7 folds for each of 32 candidates, totalling 224 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 31.1s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 31.0s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 31.7s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50 ..........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 32.2s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=100 .........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 27.1s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=100 .........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 27.1s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=50, total= 26.6s
[CV] forst_reg__max_features=45, forst_reg__n_estimators=100 .........
[CV] forst_reg__max_features=45, forst_reg__n_estimators=100 .........
导出推理结果
