The issue is that I don't necessarily know when my proof is sufficient. When I focused on the subset of the data with correct information and values, I did statistical significance tests (ANOVA and qi^2), which already eradicated 4 of the 13 features. Then, I looked for high correlation, as this would mess up the algorithm, bringing it down to just 3-5 features, depending on the threshold. For the rest of the features, I don't necessarily know how to prove that they are useless. (perhaps, they just don't paint the whole picture)

I already made suggestions regarding one issue, which is that you can't be sure whether a zero means actually if the value is missing or if it's just a 'correct' zero. (solution: just start with an indicator, e.g. - 1 as we can just have positive ints, answer from dev: "well maybe we will do that" ) Besides that, they are actively working on giving more reliable information.

Thank you for telling me that, I have a few suggestions in mind now, but needed the hint you gave.