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I don't have enough reputation to comment though, but how many features does your data have? How about using Lasso Regression to select features (using 50 anomaly data and another random 50/100 normal data, assuming that normal data come from the same distribution) and then see when plotting normal vs. abnormal data points, are abnormal points separated ...


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As an Entry Level Analyst, I use these concepts in my day to day work T-Test ANOVA Chi-Square


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Things I still use in everyday life: t-Test Imputation techniques for dealing with missing data (such a pain!) ACF, PACF plots for times series data Standardization techniques (e.g. Z-scores) Regression diagnostics (less often) related to the one above: Shapiro-Wilk test for Normality of a distribution I'll keep editing this answer, adding things as they ...


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t-test and ANOVA are used pretty often, more in statistical data analysis which is a "must know" for a data scientist but not necessarily their everyday work. More you go toward medical/bio statistics or social sciences, you see them more used. In everyday life of a data scientist, the problem of feature selection, for example, is one of situations where ...


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Maybe you could start by actually and precisely defining what you want to do with your data. Apparently you'd like to predict some probability for each type of alert (which I assume is given by the TYPE column), and find which one gets the highest probability (that boiling down to finding what type of alert is the most likely to happen in a given time ...


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t.ppf is calculating a 1-tail inverse cdf. It looks like you're trying to look up the t-value for p=.95, but the value you are referring to in the table is a 2-tailed value of .95, meaning the one tailed value is 1 - (1-.95)/2 = .975. So In [104]: st.t.ppf(0.975, df=9) Out[104]: 2.2621571627409915


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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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First figure shows Frequency(Y-axis) distribution over varied values of Line data(X-axis). Similar information gets conveyed by your second figure as well, but second one provides a deeper insight to frequency fluctuation over smaller bins of Line data. Additionally in second figure, various types (Lognorm, Exponential, etc.) of distribution gets line traced ...


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The authors of Elements of Statistical Learning have come out with a new book (Aug 2013) aimed at users without heavy math backgrounds. An Introduction to Statistical Learning: with Applications in R Download from here: http://faculty.marshall.usc.edu/gareth-james/


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Start with some natural thresholds : >3* average, >4* good, >4,5*: excellent, 4,9+* perfect. Then you can correct your rating based on some averages, other metrics or even text (but that's hard). Honestly I am not sure it will work as ratings should be viewable and giving different status to customers with same average rating will get noticed. Leave them ...


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