What are the steps for estimating a polynomial regression model?

1. Select the appropriate polynomial degree for the model, based on the complexity of the data. 2. Collect the data required for the model, including the predictor and response variables. 3. Transform the data if necessary, such as by normalizing or standardizing the variables. 4. Split the data into a training and testing set. 5. Fit a linear model to the training data and evaluate the model’s performance. 6. Add polynomial terms to the model and fit the model to the data again. 7. Evaluate the performance of the updated model using the testing data. 8. Optimize the parameters of the model using ridge or lasso regularization. 9. Evaluate the performance of the optimized model and assess any changes in performance or accuracy. 10. Make predictions using the final model.