How is data modeling and hypothesis models done in predictive analytics?

Question

What supervised/unsupervised models are used for predictive analytics? Do we need a data scientiest to create model or will the predictive analytics solution have build in libraries and based on the clustering and deploy the models automatically? How can we determine the fit of the model and which model was used for the data set?

I have seen some products in which the system creates multiple models for each line of the dataset automatically without the need for data scientist. Is SAP predictive analytics solution on similar lines?

What supervised/unsupervised models are used for predictive analytics? Do we need a data scientiest to create model or will the predictive analytics solution have build in libraries and based on the clustering and deploy the models automatically? How can we determine the fit of the model and which model was used for the data set? I have seen some products in which the system creates multiple models for each line of the dataset automatically without the need for data scientist. Is SAP predictive analytics solution on similar lines?

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  1. Hi Prashant,

    SAP Predictive Analytics has rich set of functionalities both for Citizen Data Scientist/Business Analyst using Automated mode and Data Scientist – Expert Mode. So Automated mode provides built-in ML techniques (which are supervised) where a user does not need to select a specific algorithm for given business problem. It also takes care binning, missing value handling and encoding while generating predictive models. Expert mode on the other hand does gives flexibility to Data Scientists to choose algorithms and build complicated model chain via predictive libraries including R. The algorithms are not limited to training alone but does include data preparation, feature generation/selection, scoring and operationlization of models. There are lot of blogs , forums, media resources on community which can help you more on getting up to speed + the links above.

    Regards. Priti