A machine learning professional is designing a linear regression model using Spark ML to predict car prices. They have a Spark DataFrame (train_df) for training the model, which includes the schema: car_id STRING, price DOUBLE, stars DOUBLE, year_updated DOUBLE, seats DOUBLE. The professional uses the following code block:

lr = LinearRegression(
  featuresCol = [“stars”, “year_updated”, “seats”],
  labelCol = “price”
)
lr_model = lr.fit(train_df)

lr = LinearRegression(
  featuresCol = [“stars”, “year_updated”, “seats”],
  labelCol = “price”
)
lr_model = lr.fit(train_df)

What changes are necessary for the professional to successfully implement their linear regression model?