pandas实现logistic回归

使用pandas实现logistic回归的步骤如下：

1.导入所需的库和数据集：

import pandas as pd
from sklearn.linear_model import LogisticRegression

df = pd.read_csv('data.csv')

2.将数据集分为训练集和测试集：

train_data = df.iloc[:80, :]
test_data = df.iloc[80:, :]

3.将训练数据集的特征和目标变量分离：

X_train = train_data.iloc[:, :-1]
y_train = train_data.iloc[:, -1]

4.训练模型：

model = LogisticRegression()
model.fit(X_train, y_train)

5.使用测试集评估模型：

X_test = test_data.iloc[:, :-1]
y_test = test_data.iloc[:, -1]

y_pred = model.predict(X_test)

accuracy = (y_pred == y_test).sum() / len(y_test)
print('Accuracy:', accuracy)

完整代码如下：

import pandas as pd
from sklearn.linear_model import LogisticRegression

df = pd.read_csv('data.csv')

train_data = df.iloc[:80, :]
test_data = df.iloc[80:, :]

X_train = train_data.iloc[:, :-1]
y_train = train_data.iloc[:, -1]

model = LogisticRegression()
model.fit(X_train, y_train)

X_test = test_data.iloc[:, :-1]
y_test = test_data.iloc[:, -1]

y_pred = model.predict(X_test)

accuracy = (y_pred == y_test).sum() / len(y_test)
print('Accuracy:', accuracy)