the data science process step build the models with clean data in place and good understanding of the contentyou're ready to build models with the goal of making better predictionsclassifying objectsor gaining an understanding of the system that you're modeling this phase is much more focused than the exploratory analysis stepbecause you know what you're looking for and what you want the outcome to be figure shows the components of model building data science process setting the research goal retrieving data data preparation data exploration model and variable selection data modeling model execution model diagnostic and model comparison presentation and automation figure step data modeling the techniques you'll use now are borrowed from the field of machine learningdata miningand/or statistics in this we only explore the tip of the iceberg of existing techniqueswhile introduces them properly it' beyond the scope of this book to give you more than conceptual introductionbut it' enough to get you started of the techniques will help you in of the cases because techniques overlap in what they try to accomplish they often achieve their goals in similar but slightly different ways building model is an iterative process the way you build your model depends on whether you go with classic statistics or the somewhat more recent machine learning schooland the type of technique you want to use either waymost models consist of the following main steps selection of modeling technique and variables to enter in the model execution of the model diagnosis and model comparison model and variable selection you'll need to select the variables you want to include in your model and modeling technique your findings from the exploratory analysis should already give fair idea