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That to me doesn't seem like messy data at all, it is just a csv file with a ; delimiter. Depending on the region settings excel can use different delimiters when saving data as .csv file, ; being one of them. By default pandas assumes a , as the delimiter, which in this case does not apply. Try reading it in by specifying the correct delimiter using the sep ...


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What you are talking about is called feature engineering. Basically it is done to reduce the dimensionality of the dataset. What we are doing is combining 2 or more features which provide the same info, into one feature. For example I had this dataset where I had to predict the price of a used car. There were 2 features month of registration and year of ...


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You might want to apply one-hot encoding instead. These are not really continuous features. If you consider each day of the week or month of the year a category, then you can instead treat them as categorical variables. The year is trickier as it does not repeat itself. I would suggest to maybe instead of using the year to use a date difference: which can ...


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You specifically talk about account names, and so I assume they can be treated as strings. One way to compare closeness of strings is the Levenshtein distance, defined as: the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. It just so happens there is a nice library that ...


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