Python

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Generate some synthetic data to work with.
num_examples = 1000
num_features = 10
X = np.random.rand(num_examples, num_features)
y = np.random.rand(num_examples)

# Split the data into training and test sets.
train_test_split = 0.8
train_size = int(num_examples * train_test_split)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]

# Train a simple linear regression model on the training data.
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate the model on the test data and print the results.
y_pred = model.predict(X_test)
rmse = mean_squared_error(y_test, y_pred)
print(f"Root Mean Squared Error on test set: {rmse:.2f}")