What are the steps involved in building and evaluating a linear regression model in R?

The following steps are followed to build and evaluate a linear regression model in R

• The first step is to divide the data into train and test sets. This step is crucial because you have to build a model on the train and evaluate its performance on the test set. This can be done with sample.split() function from the package.
• The second step is to build the model on the train set. The function used to build the model is Im() function.
• Once you’ve built the model, you can predict the values on the data set with the help of the predict() function.
• The last step in the linear regression model is to find out the RMSE. The lower the value of RMSE, better is the prediction.