In my previous blog I showed you how you can create a neural network to predict the hardenability of steel. This was done with the help of R and Keras. I also showed you how R functionality can be integrated in SQL templates. This time I will cover the subject of forecasting and how this can be done using R. Since integrating R code in SQL templates always follows the same procedure, I will not be covering that subject again in this blog. The main goal here is to show you some of the possibilities R has to offer regarding forecasting. I will show you how to prepare a dataset, fit different types of models to your data, using those models to forecast, visualize your results, and calculate evaluation metrics. ForecastingForecasting is a method of predicting the future based on past data. For example, company X has kept track of all sales orders of product Y for the last 2 years and wants to predict next month's demand for Y. A simple method that company X can use is to sum up demand p
With the arrival of SQL Server 2016 Microsoft added the R Services add-on, which enables the execution of R scripts directly from T-SQL. Starting with SQL Server 2017, R Services was renamed to machine learning services to reflect the fact that Python is now supported as well. Both languages are highly popular in data science and machine learning. Python is an easy to learn general purpose language, while R was developed mainly for statisticians. Although there is a large amount of overlap between the languages, people with a background in statistics seem to prefer R over Python. On the other hand people new to programming or without a sufficient background in statistics prefer Python because of it's easy to learn syntax. Personally I think it is more a matter of choosing the right tool for the job. I would use Python for creating complex deep learning models and use R to analyze the results afterwards. In this blog I will show you that it is actually possible to do both using R
In our previous blog, we discussed why we came to the conclusion that translating natural language into a formal language is the best approach for us to create linguistic interfaces for Thinkwise applications. It was mentioned that an existing project called Genie follows this approach and we decided to build a prototype to test whether or not Genie is the best framework for us. The purpose of this blog is to share our story about how we built our first prototype using the Insights demo application. The prototype is available at (https://nlpdemo.thinkwise.app/)To make it more formal, for our prototype we need to construct a neural semantic parser from the Insights data model using Genie.A semantic parser is basically a tool that can translate from natural language into a machine-understandable form. In our case, this will be a translation to the ThingTalk Virtual Assistant Programming Language (VAPL). The term neural refers to the fact that the generated semantic parser is a large neur
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