Today, more and more businesses are adopting advanced analytics for mission critical decision making in areas such as fraud detection, healthcare and manufacturing. Typically, the data scientists first build out the predictive models and only then can businesses deploy those models in a production environment and consume them for predictive actions
Here are few examples that we walk through which would help data scientists create and publish their models and help a developer integrate them into their application
Click here to Know More about Microsoft R Server Operationalization.
Flight Prediction Service
In this example, we use historical on-time performance and weather data to predict whether the arrival of a scheduled passenger flight will be delayed.
We have the R script that will help in creating sample model using sample data set provided. Users can use this to publish FlightPredict service in there MRS Operationalization environment and then consume it using a console app provides as part of this example.
Click here for Flight Predict Service example on GitHub.
Loan Predict Service
In this example, we use Loan data to predict charge off loans, we use R Scoring Engine to predict “bad” loans. Loans that indicate good repayment behavior are considered “good” and loans that indicate less than perfect repayment behavior are considered “bad”. We use sample loan data set which contains several feature such as loan amount, interest, principal etc…, Here is a blog that talks about how to select a feature for loan scoring engine.
Click here for Loan Predict Service example on GitHub.
Operating Margin for Hotels
The examples is to identify a operating margin that will be used to setup a new hotel.
Click here for Predict Operating Margin example on GitHub.