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

# Sample input features (house size and number of rooms)
X = np.array([[1000, 2], [1500, 3], [1200, 2], [1700, 4], [2000, 3]])

# Corresponding target values (house prices)
y = np.array([300000, 450000, 350000, 500000, 550000])

# Split the 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)

# Create a linear regression model
model = LinearRegression()

# Train the model
model.fit(X_train, y_train)

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

# Calculate the root mean squared error (RMSE)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print("Root Mean Squared Error:", rmse)

# Predict house prices for new data
new_data = np.array([[1300, 2], [1800, 3]])
new_predictions = model.predict(new_data)
print("Predictions for new data:"), new_predictions