Training your first model in Python
In the previous recipe, we generated a scatter plot diagram to explore the relationship between the two variables in the dataset. In this recipe, we will use the SageMaker Linear Learner built-in algorithm to build a linear regression model that predicts a professional's salary using the number of months of relevant managerial experience. This recipe aims to demonstrate how a SageMaker built-in algorithm is used in a ML experiment that involves the train-test split and running the training job:
Figure 1.37 – Performing the train-test split and then running the training jobs to generate a model
Figure 1.37 shows us what we will do in this recipe. Using the DataFrame loaded from the Visualizing and understanding your data in Python recipe, we will perform the train-test split and use the training dataset to train and build the model.
Getting ready
This recipe continues on from Visualizing and understanding...
