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The first reason is a need to help the computer user solve problems that require specialized knowledge or expertise. In many situations, users need guidance and counseling in order to solve the problem at hand. The solutions to many problems in business, science, and engineering depend on the application of sophisticated numeric algorithms or techniques. In such situations, users often need help in determining which specific algorithm or technique should be employed and in interpreting any computed results. In other situations, the need is more basic--for guidance in determining whether the problem at hand can be solved and, if so, whether the resources that can be brought to bear are sufficient.

Clustering with non numeric data


I have been working on this on and off for last couple of months, hence the delay. I tried out something very simple since our clients wanted to see "something" very quick. I created dummy (1 or 0) variables from the categorical variables. With this I ended up with 30 variables. I also had some numeric vars (like distance to closest competitor, guest scores etc) which I left aside for the time being since the clients were more interested in the dummy variables than the others.

Real-time 2019 Portuguese Parliament Election Results Dataset Machine Learning

This paper presents a data set describing the evolution of results in the Portuguese Parliamentary Elections of October 6$^{th}$ 2019. The data spans a time interval of 4 hours and 25 minutes, in intervals of 5 minutes, concerning the results of the 27 parties involved in the electoral event. The data set is tailored for predictive modelling tasks, mostly focused on numerical forecasting tasks. Regardless, it allows for other tasks such as ordinal regression or learn-to-rank.

Can you help me compare two fields with numeric values?


I have a query where I do a bunch of computations, and then at the end of it, I want to add a new field based on the result of a comparison of the numeric values of 2 other fields. I don't know what I am doing wrong. I tried using case in the eval, but I still get the same results.

My favourite R package for: summarising data


For example, to remove the "hist" column: Now we don't see the messy unicode characters, and we won't for the rest of our skimming session. UPDATE 2018-01-22: the geniuses who designed skimr actually did find a way to make the sparklines appear in Windows after all! Just update your skimr version to version 1.0.1 and you're back in graphical business, as the rightmost column of the integer variables below demonstrate. The output works well with kable.