Pipeline linear regression round involves gathering and preprocessing data, selecting and training a linear regression model, tuning and evaluating its hyperparameters, finalizing evaluation and deploying the model. This process combines data science, machine learning, supervised learning, and artificial intelligence concepts. Python (scikit-learn) and R (caret) libraries facilitate implementation. Spark MLlib enables distributed machine learning applications.
