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o develop a prediction model, data scientists employ a training set  historical data in  hich the desired outcome is
        already kno n). As businesses receive intermediate insights, the modelling process is often very iterative, leading to
        refinements in data preparation and model formulation. Data scientists may attempt numerous algorithms  ith their
        respective parameters for a specific technique to get the best model for the available variables.

        S tag e 8 : Evaluation
         he data scientist

        •   revie s the model during development and before deployment to determine its quality and ensure that it correctly
            and completely ans ers the business problem.

        •       interprets the model's quality and efficacy in solving the problem by producing numerous diagnostic metrics and
            other outputs such as tables and graphs.

        •    utilises a testing set for predictive models  that is separate from the training set but follo s the same probability
            distribution and has a kno n outcome.)  he testing set is used to assess the model and ad ust it as necessary.
         or a final assessment, the final model is sometimes applied to a validation set as  ell.
        In addition, data scientists can use statistical significance tests to verify the model's accuracy.  his additional evidence
        could help  ustify model deployment or take action  hen the stakes are high, such as  ith an e pensive additional
        medical procedure or a key aviation  ight system.

        S tag e 9 : Deployment
         he model is deployed into the production environment or an equivalent test environment once it has been built
        and authorised by the business sponsors. It is usually used in a restricted capacity until its effectiveness has been
        thoroughly  assessed.   he  model  might  be  embedded  in  a  complicated   ork o   and  scoring  process  run  by  a
        customised application, or it could be as simple as providing a report  ith recommendations. Deploying a model into
        a live business process frequently necessitates the involvement of additional internal teams, skills, and technology.

        S tag e 1 0 : F eedb ack
         he organisation receives feedback on the model's effectiveness and impact on the environment in  hich it  as deployed
        by collecting findings from the implemented model.  or instance, feedback could come in the form of response rates
        to a promotional campaign. Data scientists can utilise this feedback to improve the model's accuracy and utility by
        analysing it.  hey can automate any or all of the feedback gathering, model assessment, refining, and redeployment
        phases to speed up the model refresh process and improve results.
         he iterative nature of the problem solving process is sho n by this methodology's  o . As data scientists have a
        better understanding of the data and models, they typically return to a prior stage to make changes.  odels aren't built
        once, deployed, and forgotten about  instead, they're constantly refined and adapted to changing situations through
        feedback, refinement, and redeployment. As a result, both the model and labour that goes into it can continue to add
        value to the business for as long as the solution is required.

         o ensure a strong result, each stage needs to be continually improved, altered, and t eaked.  he three goals of the
        frame ork are as follo s
             nderstand the question at hand first.
             econd, decide on a strategy or method for analysis to address the issue.

             ollect, comprehend, prepare, and model the data.
         he ultimate ob ective is to get the data scientist to the point  here a data model can be created to provide the ans er.





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