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There are probably a good few options, as usual I would start with something simple (possibly similar to what you have in mind). Simple year1 vs year2 or year2 vs year3 global comparison: proportion of customer who go from number X in year N to number Y in year N+1. A heatmap would probably work well with this kind of data, and if you want some fancy ...


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I suggest you take a look at the TidyTuesday repo, where every week they post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. The repo also contains other resources, like data science books. Together with the repo, I suggest the TidyTuesday videos by David Robinson, where he creates screencasts of complete data ...


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The current approach use 70/30 or 80/20, the most used is 80/20 (train/test). However there is other things you should check, for example if you data is balanced. If your data is not balanced you might want to use undersample or oversample.


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When making decisions about which data to use in a model you have to be aware of several pitfalls. One of them is information leakage i.e. including data that contains information that you shouldn't have at the time of prediction. Both Duration and Goals are data points that you do not have at the time of prediction (that is before a match) and therefore ...


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If you only consider the process of making something from your data (excluding data extraction etc), I'd say something like : Visualising your data to understand it Cleaning your data, removing useless features etc Feature engineering : keeping only relevant features, creating new features from existing ones (combining a few one, making ratio of 2 features ...


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