I have to write a binary classifier for my company that should be as simple as possible and doesn't use machine learning libraries (and I also should not code too sophisticated algorithms by myself).
I just came up with the following idea: If I used a boxplot of my features and if the feature value of a new sample is bigger than the whiskers of the boxplot, I say the sample is in category A and if it is smaller than the whisker, it is in category B. Is this a good approach? (probably not) Are there classification algorithms that are simple enough so that it is feasible to code them without libraries?