ML之RF：基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例
2024-04-17 01:48:22
ML之RF：基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例
目录
基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例
数据说明
查看数据分布
# 1、定义数据集
# 1.1、提取有意义的字段(比如去掉ID)
# 1.2、特征类型划分
# 2、数据预处理/特征工程
# 2.1、分析目标变量
# 2.1.1、目标变量占比
# 2.1.2、计算训练集特征与lable之间的相关系数
# 2.2、分析类别型字段：依次查看训练集、测试集中，类别型字段的细分类
# 3、模型训练与评估
# 3.1、构建Pipeline流水线
# 3.2、模型训练与评估：采用RFC算法
# 3.4、模型推理：基于模型最佳参数
相关文章
ML之RF：基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例
ML之RF：基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例
基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例
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。
数据集地址：和鲸社区 - Heywhale.com
| 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
查看数据分布
# 1、定义数据集
| ID | age | job | marital | education | default | balance | housing | loan | contact | day | month | duration | campaign | pdays | previous | poutcome | y |
| 1 | 43 | management | married | tertiary | no | 291 | yes | no | unknown | 9 | may | 150 | 2 | -1 | 0 | unknown | 0 |
| 2 | 42 | technician | divorced | primary | no | 5076 | yes | no | cellular | 7 | apr | 99 | 1 | 251 | 2 | other | 0 |
| 3 | 47 | admin. | married | secondary | no | 104 | yes | yes | cellular | 14 | jul | 77 | 2 | -1 | 0 | unknown | 0 |
| 4 | 28 | management | single | secondary | no | -994 | yes | yes | cellular | 18 | jul | 174 | 2 | -1 | 0 | unknown | 0 |
| 5 | 42 | technician | divorced | secondary | no | 2974 | yes | no | unknown | 21 | may | 187 | 5 | -1 | 0 | unknown | 0 |
| 6 | 56 | services | divorced | secondary | no | 450 | no | no | unknown | 9 | jun | 47 | 1 | -1 | 0 | unknown | 0 |
| 7 | 32 | management | single | tertiary | no | 1716 | no | no | cellular | 21 | nov | 157 | 4 | -1 | 0 | unknown | 0 |
| 8 | 33 | management | single | tertiary | no | -359 | no | no | telephone | 29 | aug | 59 | 2 | -1 | 0 | unknown | 0 |
| 9 | 32 | technician | single | secondary | no | 309 | yes | yes | cellular | 25 | aug | 99 | 3 | -1 | 0 | unknown | 0 |
| 10 | 55 | management | married | tertiary | no | 4777 | no | yes | unknown | 20 | jun | 12 | 3 | -1 | 0 | unknown | 0 |
# 1.1、提取有意义的字段(比如去掉ID)
RangeIndex: 25317 entries, 0 to 25316
Data columns (total 18 columns):# Column Non-Null Count Dtype
--- ------ -------------- ----- 0 ID 25317 non-null int64 1 age 25317 non-null int64 2 job 25317 non-null object3 marital 25317 non-null object4 education 25317 non-null object5 default 25317 non-null object6 balance 25317 non-null int64 7 housing 25317 non-null object8 loan 25317 non-null object9 contact 25317 non-null object10 day 25317 non-null int64 11 month 25317 non-null object12 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 object17 y 25317 non-null int64
dtypes: int64(9), object(9)
memory usage: 3.5+ MB
None# 1.2、特征类型划分
返回数值型字段 7 ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']
返回类别型字段 9 ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome']
返回非数值型和类别型字段 0 []# 2、数据预处理/特征工程
# 2.1、分析目标变量
# 2.1.1、目标变量占比
训练集 y标签的比例： 0.11695698542481336# 2.1.2、计算训练集特征与lable之间的相关系数
训练集计算相关系数： y 1.000000
duration 0.394746
pdays 0.107565
previous 0.088337
campaign 0.075173
balance 0.057564
day 0.031886
age 0.029916
Name: y, dtype: float64# 2.2、分析类别型字段：依次查看训练集、测试集中，类别型字段的细分类
job ['management' 'technician' 'admin.' 'services' 'retired' 'student''blue-collar' 'unknown' 'entrepreneur' 'housemaid' 'self-employed''unemployed']
marital ['married' 'divorced' 'single']
education ['tertiary' 'primary' 'secondary' 'unknown']
default ['no' 'yes']
housing ['yes' 'no']
loan ['no' 'yes']
contact ['unknown' 'cellular' 'telephone']
month ['may' 'apr' 'jul' 'jun' 'nov' 'aug' 'jan' 'feb' 'dec' 'oct' 'sep' 'mar']
poutcome ['unknown' 'other' 'failure' 'success']
job ['housemaid' 'management' 'retired' 'student' 'technician' 'services''admin.' 'blue-collar' 'unemployed' 'self-employed' 'entrepreneur''unknown']
marital ['married' 'single' 'divorced']
education ['unknown' 'tertiary' 'primary' 'secondary']
default ['no' 'yes']
housing ['no' 'yes']
loan ['no' 'yes']
contact ['telephone' 'cellular' 'unknown']
month ['jul' 'nov' 'jun' 'may' 'aug' 'apr' 'feb' 'mar' 'oct' 'jan' 'dec' 'sep']
poutcome ['unknown' 'failure' 'other' 'success']# 3、模型训练与评估
# 3.1、构建Pipeline流水线
df_train_X_after_pipeline 0 1 2 3 ... 47 48 49 50
0 0.194151 -0.355546 -0.821617 -0.419241 ... 0.0 0.0 0.0 1.0
1 0.100114 1.239579 -1.062021 -0.617708 ... 0.0 1.0 0.0 0.0
2 0.570301 -0.417885 -0.220606 -0.703321 ... 0.0 0.0 0.0 1.0
3 -1.216408 -0.783913 0.260203 -0.325845 ... 0.0 0.0 0.0 1.0
4 0.100114 0.538857 0.620810 -0.275255 ... 0.0 0.0 0.0 1.0
... ... ... ... ... ... ... ... ... ...
25312 1.322599 2.274328 -0.220606 2.320378 ... 1.0 0.0 0.0 0.0
25313 1.040487 -0.132195 0.260203 -0.139053 ... 1.0 0.0 0.0 0.0
25314 -0.558147 -0.352546 -0.340808 2.674504 ... 0.0 0.0 0.0 1.0
25315 -0.370072 -0.430552 0.260203 3.526744 ... 0.0 0.0 0.0 1.0
25316 1.040487 -0.439220 0.380405 3.919786 ... 0.0 0.0 0.0 1.0[25317 rows x 51 columns]# 3.2、模型训练与评估：采用RFC算法
Fitting 3 folds for each of 35 candidates, totalling 105 fits
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=50; total time= 1.0s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=50; total time= 1.0s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=50; total time= 1.0s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=100; total time= 1.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=100; total time= 1.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=100; total time= 1.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=150; total time= 2.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=150; total time= 2.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=150; total time= 2.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=200; total time= 3.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=200; total time= 3.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=200; total time= 4.0s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=250; total time= 4.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=250; total time= 4.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=250; total time= 5.0s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=300; total time= 5.9s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=50; total time= 1.8s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=300; total time= 5.8s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=50; total time= 1.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=300; total time= 5.8s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=50; total time= 1.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=350; total time= 7.4s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=350; total time= 7.5s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=100; total time= 4.5s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=100; total time= 4.9s
[CV] END forst_reg__max_features=5, forst_reg__n_estimators=350; total time= 7.8s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=100; total time= 5.0s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=150; total time= 7.6s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=150; total time= 7.8s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=150; total time= 7.9s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=200; total time= 9.9s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=200; total time= 10.0s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=200; total time= 10.0s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=250; total time= 12.2s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=250; total time= 12.1s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=50; total time= 3.9s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=250; total time= 12.6s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=300; total time= 15.1s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=300; total time= 14.8s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=50; total time= 3.9s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=50; total time= 4.0s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=300; total time= 14.8s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=350; total time= 17.6s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=100; total time= 7.6s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=350; total time= 17.0s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=100; total time= 7.6s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=100; total time= 7.9s
[CV] END forst_reg__max_features=15, forst_reg__n_estimators=350; total time= 17.4s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=150; total time= 11.9s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=150; total time= 12.3s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=150; total time= 12.0s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=200; total time= 16.1s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=200; total time= 15.9s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=200; total time= 17.0s
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[CV] END forst_reg__max_features=25, forst_reg__n_estimators=250; total time= 20.4s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=250; total time= 20.3s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=50; total time= 5.2s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=50; total time= 5.5s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=300; total time= 24.1s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=50; total time= 5.5s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=300; total time= 23.9s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=300; total time= 23.8s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=100; total time= 11.2s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=100; total time= 10.4s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=350; total time= 27.3s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=100; total time= 10.8s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=350; total time= 27.0s
[CV] END forst_reg__max_features=25, forst_reg__n_estimators=350; total time= 27.4s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=150; total time= 16.3s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=150; total time= 16.3s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=150; total time= 16.9s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=200; total time= 21.9s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=200; total time= 22.4s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=200; total time= 22.7s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=250; total time= 28.6s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=250; total time= 27.6s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=50; total time= 0.0s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=50; total time= 0.0s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=50; total time= 0.0s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=100; total time= 0.0s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=100; total time= 0.0s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=100; total time= 0.0s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=150; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=150; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=150; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=200; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=200; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=200; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=250; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=250; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=250; total time= 0.1s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=250; total time= 28.3s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=300; total time= 0.2s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=300; total time= 0.1s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=300; total time= 0.2s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=350; total time= 0.2s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=350; total time= 0.2s
[CV] END forst_reg__max_features=55, forst_reg__n_estimators=350; total time= 0.2s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=300; total time= 34.0s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=300; total time= 34.0s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=300; total time= 31.9s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=350; total time= 34.6s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=350; total time= 33.6s
[CV] END forst_reg__max_features=35, forst_reg__n_estimators=350; total time= 30.1s# 3.4、模型推理：基于模型最佳参数
{'forst_reg__max_features': 15, 'forst_reg__n_estimators': 350}age job marital education ... pdays previous poutcome pred
0 51 housemaid married unknown ... -1 0 unknown 0
1 32 management married tertiary ... -1 0 unknown 0
2 60 retired married primary ... -1 0 unknown 0
3 32 student single tertiary ... 105 5 failure 1
4 41 housemaid married secondary ... -1 0 unknown 0
... ... ... ... ... ... ... ... ... ...
10847 29 management single tertiary ... -1 0 unknown 0
10848 35 management married secondary ... -1 0 unknown 0
10849 38 services married secondary ... -1 0 unknown 0
10850 41 admin. divorced secondary ... 363 2 failure 0
10851 34 blue-collar married primary ... -1 0 unknown 0[10852 rows x 17 columns]Process finished with exit code 0
ML之RF：基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+RF模型预测(调参+交叉验证评估+模型推理)客户是否购买该银行的产品二分类案例相关推荐
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