1.11

简单的python代码

# 导入所需库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score

# 读取数据
data = pd.read_csv('insurance_data.csv')

# 数据预处理
# 这里假设数据已经经过清洗和处理，包括缺失值处理、特征编码等

# 划分特征和标签
X = data.drop('is_fraud', axis=1)
y = data['is_fraud']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 初始化逻辑回归模型
lr_model = LogisticRegression()

# 训练模型
lr_model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = lr_model.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)