I've been tasked with imputing missing values for a dataset of ca. 4000 firms and 225 key metrics (e.g. revenue, net income, EPS, PE etc.). Since I haven't found a thread on here which answers my question I'm just going to ask myself:
Firstly, the purpose of this task is to eventually impement a ML alogrithm which lets me categorize firms that perform better than the market (buy), similar to the market (hold) and worse than the market (sell).
Since this is my first time doing something like this in this kind of scale I'm unsure which imputation method I should use. I have looked at mean/median/most frequent value but this method seems to be too simplistic. Additionally, I have implemented a KNN imputer (from the scikit-learn package) but I find it very hard to asses whether this gives me good results or not.
Note: the implementation of the ML algo is in a jupyter notebook (python) and the data I'm referring to is available on kaggle: https://www.kaggle.com/cnic92/200-financial-indicators-of-us-stocks-20142018
Thanks for any hints or suggestions