Load the data: The code loads the house price dataset from a CSV file.
Prepare the data: The code prepares the data by converting categorical features to numerical features and handling missing values.
Split the data into training and test sets: The code splits the data into training and test sets using the train_test_split() function from scikit-learn.
Create the linear regression model: The code creates a linear regression model using the LinearRegression() class from scikit-learn.
Train the model: The code trains the linear regression model on the training set.
Evaluate the model: The code evaluates the linear regression model on the test set using the mean_squared_error() function from scikit-learn.
Make predictions: The code uses the trained linear regression model to make predictions on new data.