identifying profitable customers (regression and classificationproactively identifying car parts that are likely to fail (regressionidentifying tumors and diseases (classificationpredicting the amount of money person will spend on product (regressionpredicting the number of eruptions of volcano in period (regressionpredicting your company' yearly revenue (regressionpredicting which team will win the champions league in soccer (classificationoccasionally data scientists build model (an abstraction of realitythat provides insight to the underlying processes of phenomenon when the goal of model isn' prediction but interpretationit' called root cause analysis here are few examplesunderstanding and optimizing business processsuch as determining which products add value to product line discovering what causes diabetes determining the causes of traffic jams this list of machine learning applications can only be seen as an appetizer because it' ubiquitous within data science regression and classification are two important techniquesbut the repertoire and the applications don' endwith clustering as one other example of valuable technique machine learning techniques can be used throughout the data science processas we'll discuss in the next section where machine learning is used in the data science process although machine learning is mainly linked to the data-modeling step of the data science processit can be used at almost every step to refresh your memory from previous the data science process is shown in figure 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 the data science process