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There are a few different perspectives from which to determine the amount of data you need. Those include: Project complexity: Each parameter that your model has to consider in order to perform its task increases the amount of data that it will need for training. Training method: As your models is forced to understand a greater number of interlinking ...


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If you one hot encode when you are doing unsupervised learning, because this feature will have many more dimesions it would have much more weight in the final model than if it was read as categorical. You can implement Kmodes. In this question which is one of the most famous of the forum you can check an answer to your problem. And about if it is needed ...


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You can use one-hot-encoding for all categorical features. Then normalizing numerical features (as one-hot is 0 and 1 then maybe normalizing your numerical data to [0,1] will bean intuitive starting poit). Then apply a dimensionality reduction technique as probably you will produce a sparse matrix with maybe considerable number of dimensions. Then do your ...


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Welcome to the community, I do not know about other libraries, but gensim has a very good API to create word2vec models. In order to preprocess data, you have to decide first what things you are gonna keep in your vocab and whatnot. for ex:- Punctuations, numbers, alphanumeric words(ex - 42nd) etc. In my knowledge, the most generic preprocessing pipeline ...


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In general tidy data is great... but it can quickly become unreasonably large. This is the main reason why I usually try to refactor my data in a tidy format as late as possible in the process. Example: imagine a dataset containing $N$ instances, with columns feature1 ... featureX and result1... resultY, where the result? columns represent some value based ...


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I would separate value with representation in this case. Energy as you mentioned, in the real world, holds a very continuous value. However, we may choose (for various reasons) to represent this value in different forms. We can take values as they are (15.21252, 23.76535), we can round them into integers (15, 24), we can even decide to represent this ...


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Scikit uses numpy for pseudo-random number generation. So to fix random state in various scikit calls, you use numpy.random.seed(12345) and then use scikit. You would want to record the random seed when you log the model so you could reproduce the same run later. If your code (or something you call) also uses Python's random number generator, you would set ...


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