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本文针对某国外匿名化处理后的信用卡真实数据集，通过建模判断该用户是否已经流失，包括特征处理与分类模型建模评估。
目录
问题描述
依据某国外匿名化处理后的真实数据集，通过建模，判断该用户是否已经流失。
1. 读取数据并分离特征与标签
import pandas as pd
import numpy as np
# 读取数据
train_data = pd.read_csv('./Churn-Modelling.csv')
test_data = pd.read_csv('./Churn-Modelling-Test-Data.csv')
x_train = train_data.iloc[:,:-1]
y_train = train_data.iloc[:,-1].astype(int)
x_test = test_data.iloc[:,:-1]
y_test = test_data.iloc[:,-1].astype(int)
x_train.head()
| RowNumber | CustomerId | Surname | CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 15634602 | Hargrave | 619 | France | Female | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 |
| 1 | 2 | 15647311 | Hill | 608 | Spain | Female | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 |
| 2 | 3 | 15619304 | Onio | 502 | France | Female | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 |
| 3 | 4 | 15701354 | Boni | 699 | France | Female | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 |
| 4 | 5 | 15737888 | Mitchell | 850 | Spain | Female | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 |
数据说明:
RowNumber：行号
CustomerID：用户编号
Surname：用户姓名
CreditScore：信用分数
Geography：用户所在国家/地区
Gender：用户性别
Age：年龄
Tenure：当了本银行多少年用户
Balance：存贷款情况
NumOfProducts：使用产品数量
HasCrCard：是否有本行信用卡
IsActiveMember：是否活跃用户
EstimatedSalary：估计收入
Exited：是否已流失，这将作为我们的标签数据
2.特征工程
2.1 删除无用特征
# 删除前三列没用的数据
x_train = x_train.drop(labels=x_train.columns[[0,1,2]], axis=1)
x_test = x_test.drop(labels=x_test.columns[[0,1,2]], axis=1)
x_train.head()
| CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 619 | France | Female | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 |
| 1 | 608 | Spain | Female | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 |
| 2 | 502 | France | Female | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 |
| 3 | 699 | France | Female | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 |
| 4 | 850 | Spain | Female | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 |
y_train[:5]
0 1
1 0
2 1
3 0
4 0
Name: Exited, dtype: int32
2.2 将字符串特征进行编码
# 国家与性别两列为非数值型数据，使用LabelEncoder进行编码，将其转换为数值数据
from sklearn.preprocessing import LabelEncoder
Lb1 = LabelEncoder()
x_train.iloc[:,1] = Lb1.fit_transform(x_train.iloc[:,1])
x_test.iloc[:,1] = Lb1.transform(x_test.iloc[:,1])
Lb2 = LabelEncoder()
x_train.iloc[:,2] = Lb2.fit_transform(x_train.iloc[:,2])
x_test.iloc[:,2] = Lb2.transform(x_test.iloc[:,2])
x_train[:5]
| CreditScore | Geography | Gender | Age | Tenure | Balance | NumOfProducts | HasCrCard | IsActiveMember | EstimatedSalary | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 619 | 0 | 0 | 42 | 2 | 0.00 | 1 | 1 | 1 | 101348.88 |
| 1 | 608 | 2 | 0 | 41 | 1 | 83807.86 | 1 | 0 | 1 | 112542.58 |
| 2 | 502 | 0 | 0 | 42 | 8 | 159660.80 | 3 | 1 | 0 | 113931.57 |
| 3 | 699 | 0 | 0 | 39 | 1 | 0.00 | 2 | 0 | 0 | 93826.63 |
| 4 | 850 | 2 | 0 | 43 | 2 | 125510.82 | 1 | 1 | 1 | 79084.10 |
x_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 10 columns):
CreditScore 10000 non-null int64
Geography 10000 non-null int64
Gender 10000 non-null int64
Age 10000 non-null int64
Tenure 10000 non-null int64
Balance 10000 non-null float64
NumOfProducts 10000 non-null int64
HasCrCard 10000 non-null int64
IsActiveMember 10000 non-null int64
EstimatedSalary 10000 non-null float64
dtypes: float64(2), int64(8)
memory usage: 781.3 KB
2.3 对特征数据进行归一化
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
x_train[:5]
array([[-0.32622142, -0.90188624, -1.09598752, 0.29351742, -1.04175968,
-1.22584767, -0.91158349, 0.64609167, 0.97024255, 0.02188649],
[-0.44003595, 1.51506738, -1.09598752, 0.19816383, -1.38753759,
0.11735002, -0.91158349, -1.54776799, 0.97024255, 0.21653375],
[-1.53679418, -0.90188624, -1.09598752, 0.29351742, 1.03290776,
1.33305335, 2.52705662, 0.64609167, -1.03067011, 0.2406869 ],
[ 0.50152063, -0.90188624, -1.09598752, 0.00745665, -1.38753759,
-1.22584767, 0.80773656, -1.54776799, -1.03067011, -0.10891792],
[ 2.06388377, 1.51506738, -1.09598752, 0.38887101, -1.04175968,
0.7857279 , -0.91158349, 0.64609167, 0.97024255, -0.36527578]])
3. 建模预测与评估
# 使用逻辑回归进行建模
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()
sgd=SGDClassifier()
lr.fit(x_train,y_train)
lr_y_predict=lr.predict(x_test)
#使用逻辑斯蒂回归墨香自带的评分函数score获得模型在测试集上的准确性结果
print('LogisticRegression测试集准确度:',lr.score(x_test,y_test))
print('LogisticRegression训练集准确度:',lr.score(x_train,y_train))
LogisticRegression测试集准确度: 0.761
LogisticRegression训练集准确度: 0.809
from sklearn.metrics import classification_report
#使用classificaion_report模块获得LogisticRegression其他三个指标的结果
print(classification_report(y_test,lr_y_predict,target_names=['Exited','UnExited']))
precision recall f1-score support
Exited 0.77 0.97 0.86 740
UnExited 0.68 0.15 0.25 260
avg / total 0.74 0.76 0.70 1000
结果表明该模型准确率只有76%，还有一定的优化空间。
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