Use built-in python powered TURBOARD technology. You won't need to carry out data science operations in other tools and move output data in excel sheets. This would be a burden and would require extra work to connect statistical output with your current data. Even integration with 3rd party tools would work just to ease this cumbersome processes a bit.
With TURBOARD data science abilities are packed in application. You don't need to know about powerful infrastructural tools of python for data analytics. You will just use your mouse in an apt UX and prepare the analyses.
No-hassle start for data science, no need to prepare data set for a separate tool. Use TURBOARD view and filters to prepare and clean data. Create samplings in TURBOARD View interface. Use powerful TURBOARD abilities to clean data and move forward to analyzing and modeling data in TURBOARD Analytics UX.
When you come up with a model and apply that in TURBOARD, you apply that model not just to the sampling but to the whole set. So basically you use the same TURBOARD filters hand-in-hand with analytics powered expressions and mix analytics with your current business intelligence filters. As an example you may check the regression line in one region compare with whole country data or you may check in which branches your predictions are more accurate.
In order to predict a target value from several preferably independent variables regression algorithms try to find a formula that match best in the sampling.
Imagine you know the data on taxi rides in a city, kilometers, minutes and fee for each ride. Regression will produce the best formula that will fit most of previous taxi rides. r2 is a quality metric for regression and tells percent of sampling that are perfectly defined by that predicted formula. If r2 is 0.9 that means the formula we found in regression defines 90% of taxi rides (some people may bargain for less or sometimes taxi drivers may keep the change).
In TURBOARD you can use linear and polynomial regression models.
Given a target variable a classification algorithm will learn how data would decide that specific target variable and in new parameters will predict an output.
SVM and Logistic Regression are available algorithms that can be used without any single line of coding.
Given any number of parameters clustering will create groups of entities depending on their proximity
TURBOARD has Kmeans and Meanshift in it's pre-built statistical models repository
Different with competitor BI What-If reports as TURBOARD can use statistical model within what-if, so a %20 increase in price will not boost turnover 20% as machine will predict there will be less sales.