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I agree with German C M, there is some structure in your data even though it's not fully structured. So the first task is to transform the data into features that can be exploited by ML. This is typical feature engineering: the idea is to try to organize the different types of elements in the data in a way susceptible to provide useful indications to the ...


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I don't have a precise answer to that because it depends on what you want to do with that data. Assuming that your task is supervised learning since is the most popular, just extract that feature will be enough for a model to discriminate between different cases. EDIT: Models like linear regression or NN works better under normality regime; in this case I ...


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Aren't yourself providing the answer? You can split the feature in two, namely, if feature_to_split is the feature you're talking about, you can create feature_to_split_ispresent which will take either 1 or 0 depending on the presence or absence of that specific characteristic, and feature_to_split_value which will take the actual value of that ...


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Before thinking of what type of algorithm you could use, I would think of how to properly preprocess your data. Depending on how many possible values you might have for each of your 8 possible types (if I understood correctly), you could construct a 0's and 1's dataset, it is, indicating the presence or absence of each possible value in each event. This ...


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If you want to perform linear regression with feature selection, you can formulate the problem as a MIO and solve it to optimality. Then you can check if its worth it to do the feature selection.


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Its best to remove such a variable. Reasons are following: Artificial imputation can add bias and result cannot be justified because 99% data for the particular variable was artificially created. The variables/features that you choose for building the predictive model should have low correlation with the target/outcome variable/feature. Because, variable ...


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Transfer learning is, as you said, retraining the final layer of a deep network. Not only is this useful for solving problems with limited training examples, but also when you don't have adequate computing resources to train a network from scratch. Some models have hundreds of millions of parameters, which could take weeks to train if you only have modest ...


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If you need to scale beyond 1000 entries in the future, a brute-force approach to find the exact neighbors will become increasingly prohibitive from a computational standpoint. To future-proof your solution, I would recommend looking into the well-researched field of approximate nearest neighbors (ANN) techniques. Obviously there is a speed/accuracy tradeoff,...


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The features you have selected are a good starting point, but are still (with the exception of tempo) quite "low level" compared to what might be most relevant for music recommendation systems. The Essentia project provides feature extractors for music, that cover both low-level, medium-level and (since Jan 2020) high-level music feature ...


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