Logistic Regression in Python

 import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

# Load the data

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

# Split the data into train and test sets

X_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'], test_size=0.25)

# Create the model

model = LogisticRegression()

# Fit the model to the training data

model.fit(X_train, y_train)

# Predict the labels for the test data

y_pred = model.predict(X_test)

# Evaluate the model

accuracy = np.mean(y_pred == y_test)

print('Accuracy:', accuracy)