写一份利用支持向量机进行贷款违约检测的代码
时间: 2024-01-17 14:27:12 浏览: 25
好的,下面是一个简单的基于支持向量机(SVM)的贷款违约检测代码,使用的语言是Python:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# 读入数据
data = pd.read_csv('loan_data.csv')
# 数据预处理,删除不需要的列
data.drop(['id', 'issue_d', 'loan_status', 'purpose'], axis=1, inplace=True)
data.dropna(inplace=True)
# 将分类变量转换为数值变量
data['term'] = data['term'].apply(lambda x: int(x[:3]))
data['int_rate'] = data['int_rate'].apply(lambda x: float(x[:-1]))
data['emp_length'] = data['emp_length'].apply(lambda x: int(x[:1]) if x[0].isdigit() else 0)
data = pd.get_dummies(data, columns=['grade', 'sub_grade', 'home_ownership', 'verification_status',
'addr_state', 'initial_list_status', 'application_type'])
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop('loan_repaid', axis=1),
data['loan_repaid'], test_size=0.2, random_state=42)
# SVM模型训练
svm = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)
svm.fit(X_train, y_train)
# 模型评估
y_pred = svm.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.4f}')
print(f'Precision: {precision:.4f}')
print(f'Recall: {recall:.4f}')
print(f'F1 Score: {f1:.4f}')
```
需要注意的是,这里的数据集(`loan_data.csv`)需要自己准备,其中包含贷款信息以及是否违约的标签。同时,这只是一个简单的示例代码,实际应用中还需要对特征进行进一步的筛选、优化模型参数等操作。
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