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67

I want to reframe your question. Don't think about switching, think about adding. In data science you'll be able to go very far with either python or r but you'll go farthest with both. Python and r integrate very well, thanks to the reticulate package. I often tidy data in r because it is easier for me, train a model in python to benefit from superior ...


32

Of course, this type of questions will also lead to primarily opinion-based answers. Nonetheless, it is possible to enumerate the strengths and weakness of each language, with respect to machine learning, statistics, and data analysis tasks, which I will try to list below. R Strengths R was designed and developed for statisticians and data analysts, so it ...


6

I didn't have this choice because I was forced to move from R to Python: It depends on your environment: When you are embedded in an engineer department, working technical group or something similar than Python is more feasible. When you are surrounded by scientists and especially statisticians, stay with R. PS: R offers keras and tensorflow as well ...


4

I would say yes. Python is better than R for most tasks, but R has its niche and you would still want to use it in many circumstances. Additionally, learning a second language will improve your programming skills. My own perspective on the strengths of R vs Python is that I would prefer R for a small, single-purpose program involving tables or charts, or ...


2

We cannot do homework for students in this network, however I can suggest that several items affecting cost and several usage patterns are missing and the number of rules is shy by an order of magnitude. I wholeheartedly agree with the educational directives you received. Consider first developing your lists further to include peripherals like DVD burner, ...


1

The labels are not unique for the input domain [0,20]. Think about sin(x)=0, x could be 0, pi, 2*pi, 3*pi, ..., n*pi, all are correct from a mathematical point of view, but this is not reflected in your MSE loss. At this point your NN has to guess the correct label from your input data. Predicting the mean of your input data is the safest bet for the network....


1

As others have said, it's not a "switch". But is it worth adding Python to your arsenal? I would say certainly. In data science, Python is popular and becoming ever more popular, while R is receding somewhat. And in the fields of machine learning and neural networks, I'd say that Python is the main language now -- I don't think R really comes close here in ...


1

Yes. Since you have only one type of data, cluster analysis may be a good choice. You can also try '1-class learning' approaches, although I have found these to be unreliable in the past. An example of a cluster analysis algorithm in R is kmeans. There are many others. These approaches will reveal points that typify large portions of the dataset. By ...


1

Let start by the concrete question, and follow talking about the general problem. a) The concrete question "find items that are particularly frequently purchased through online stores by paying shipping fee" needs few or none usage of applied AI, just a few of statistics. The question talks about "item purchased" and "buy method", thus, we have an ...


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