# Import the necessary libraries
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

# Load the data
data = pd.read_csv("data.csv")

# Split the data into features (X) and target (y)
X = data[["feature1", "feature2", ...]]
y = data["target"]

# Create the model
model = LinearRegression()

# Fit the model to the training data
model.fit(X, y)

# Make predictions on the test data
predictions = model.predict(X_test)

# Calculate the error
error = mean_squared_error(y_test, predictions)
