from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Sample data
X = [[1], [2], [3], [4]] # Features
y = [10, 20, 30, 40] # Target

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict and evaluate
predictions = model.predict(X_test)
print(f'Hean Squared Error: {mean_squared_error(y_test, predictions)}')