How to tackle too many outliers in dataset I boxplot all of my columns with seaborn boxplot in order to know how many outliers that i have, surprisingly there're too many outliers and so i can remove the outliers because i'm afraid with too many outliers it will have bad impact to my model especially impacting the mean,median, variance which will further impact the performance of my model. Then i found this QnA about too many outliers the answer said that the case is not really an outliers. The answer makes sense but i'm still afraid it will give bad impact to the performance of my model. What is the best thing should i do?

These are two of examples of what i mean, 9 out of 10 columns of my data are exactly like this and i'm really worried about this because you see, it's not only there're too many outliers, it also happens in almost every (9 of 10 columns in total) columns.
It looks like most of your data is at 0, right? Either:

Those values are not really 0 but 'missing' and should be treated as missing, rather than as 0. If a lot is missing, this will indeed be problematic for models that do not handle these, because you have to infer some value for them. You can't really drop all these if there are a lot. You could impute a mean or pick a model that handles missing values. Or:

The distribution of these values isn't normal/Gaussian, in which case there's no strong reason to believe that values outside the IQR are 'outliers'. In particular looks like it could be exponential, or a mixture of two distributions in the second case. Just use the values as is. For linear models, some transformation of the input might be better, like its log, but depends a lot on what you're doing.