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You are absolutely right. This method is called customer segmentation. Here we cluster customers based on many features like their demographic and income. Suppose we get to know a particular set of people who belong to high income group/rich demographic are not spending much then we can do promotions to increase sales to get potential new customers hence it ...


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This is a textbook multi-armed bandit problem where Morpheus needs to learn the correct policy about offering pills. As you’ve said the Neos are independent, and making the assumption that there is a best pill overall, we need an algorithm that will experiment with each of the pills to find out which one is most likely to be accepted. This is the same as ...


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In your specific case our test "Neos" may not take a pill at all since Morpheus only offers one pill of a specific color. We would have to either amend our multi-class model to include "No color / Rejection" or the binary model would work much better. From a practical stand-point I would use a multi-class model here for one simple reason: ...


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There are certain points that one should keep practicing that always prepare data first properly and make it uniform shape by applying the transformation technique or any other feature engineering methodology. As u mentioned problem in question i assume that u suppose to do iterative training. In that case when new data comes it comes with new features so ...


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You have to create what is known as a modeling pipeline this includes creating so-called transformers. These transformers basically apply all necessary data transformations on your raw data to make the input "fit" for the trained model. This might include things like One-Hot-Encoding, etc. It is important to "fit" these transformers on ...


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MAPE and MASE are common metrics to use for time series, which you may not be familiar with. MAPE - Mean Absolute Percent Error: MASE - Mean Absolute Scaled Error: Reference: https://blogs.oracle.com/datascience/7-ways-time-series-forecasting-differs-from-machine-learning You can consider also using multiple metrics for your assessment, not just one, as ...


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In general the most common metrics mentioned in several articles (e.g. like this one) are the same we also commonly use for non-time series prediction: MAE Mean absolute error MSE Mean squared error RMSE Root mean squared error Outside of linear regressions I have not seen R² used that often to validate prediction models. In fact it isn't even one of the ...


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I've tangled with modeling pricing systems over the last two years and one of my key learnings applies here: Available sales data is often a bad basis for straight-forward prediction tasks and the reason for this is fairly simple: If you classify all prices (or transactions of a given product at a price x) into "Accepted" and "Not accepted&...


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You want to manually label some cases and then extend that "manual labeling" to the rest of the data. This is a supervised learning excercise with prior manual labeling by you. Let's suppose you have partitioned a random, suitably sized training data set. Now you need to model a classification algorithm via the classical modeling pipeline and use ...


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The problem you are talking about is unsupervised sentiment analysis. You can try: VADER: It gives the polarity of the sentence based on which you can tag your training data. But this library has certain limitations - it can't sense sarcasm and sometimes the accuracy is not that great. But for initial understanding, you can check this library. Text Blob - ...


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