model based learning the model based learning methods are more traditional ml approach toward generalizing based on training data typically an iterative process takes place where the input data is used to extract features and models are built based on various model parameters (known as hyperparametersthese hyperparameters are optimized based on various model validation techniques to select the model that generalizes best on the training data and some amount of validation and test data (split from the initial datasetfinallythe best model is used to make predictions or decisions as and when needed the crisp-dm process model the crisp-dm model stands for cross industry standard process for data mining more popularly known by the acronym itselfcrisp-dm is triedtestedand robust industry standard process model followed for data mining and analytics projects crisp-dm clearly depicts necessary stepsprocessesand workflows for executing any project right from formalizing business requirements to testing and deploying solution to transform data into insights data sciencedata miningand machine learning are all about trying to run multiple iterative processes to extract insights and information from data hence we can say that analyzing data is truly both an art as well as sciencebecause it is not always about running algorithms without reasona lot of the major effort involves in understanding the businessthe actual value of the efforts being investedand proper methods to articulate end results and insights the crisp-dm model tells us that for building an end-to-end solution for any analytics project or systemthere are total of six major steps or phasessome of them being iterative just like we have software development lifecycle with several major phases or steps for software development projectwe have data mining or analysis lifecycle in this scenario figure - depicts the data mining lifecycle with the crisp-dm model