modeling the fourth phase in the crisp-dm process is the core phase in the process where most of the analysis takes place with regard to using cleanformatted data and its attributes to build models to solve business problems this is an iterative processas depicted in figure - earlieralong with model evaluation and all the preceding steps leading up to modeling the basic idea is to build multiple models iteratively trying to get to the best model that satisfies our success criteriadata mining objectivesand business objectives we briefly talk about some of the major stages relevant to modeling in this section selecting modeling techniques in this stagewe pick up list of relevant machine learning and data mining toolsframeworkstechniquesand algorithms listed in the "business understandingphase techniques that are proven to be robust and useful in solving the problem are usually selected based on inputs and insights from data analysts and data scientists these are mainly decided by the current data availablebusiness goalsdata mining goalsalgorithm requirementsand constraints model building the process of model building is also known as training the model using data and features from our dataset combination of data (featuresand machine learning algorithms together give us model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions generally various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria key things to keep track here are the models createdmodel parameters being usedand their results model evaluation and tuning in this stagewe evaluate each model based on several metrics like model accuracyprecisionrecallf scoreand so on we also tune the model parameters based on techniques like grid search and cross validation to get to the model that gives us the best results tuned models are also matched with the data mining goals to see if we are able to get the desired results as well as performance model tuning is also termed as hyperparameter optimization in the machine learning world model assessment once we have models that are providing desirable and relevant resultsa detailed assessment of the model is performed based on the following parameters model performance is in line with defined success criteria reproducible and consistent results from models scalabilityrobustnessand ease of deployment future extensibility of the model model evaluation gives satisfactory results