Python通过神经网络训练模型

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 加载数据
data = pd.read_csv('data.csv')

# 分离特征和标签
X = data.drop('label', axis=1)
y = data['label']

# 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)

# 训练模型
clf = LogisticRegression()
clf.fit(X_train, y_train)

# 在测试集上评估模型
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)