Binary Classification without machine learning tools 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?
How about implementing one of the most basic binary classifiers by hand without any libraries, Logistic Regression?
Here is the explanation of the Logistic Regression in subtle terms:

https://www.youtube.com/watch?v=6tByJTacCOc&amp;index=6&amp;list=PLBAGcD3siRDguyYYzhVwZ3tLvOyyG5k6K

Here is the description of writing a simplified version of a binary classifier in Python, which will take a few buckets of lines in the vectorized form:

https://www.youtube.com/watch?v=ERIFzbTdIm0&amp;index=14&amp;list=PLBAGcD3siRDguyYYzhVwZ3tLvOyyG5k6K

If you feel uncomfortable with the implementation or confused about something, I'm sure that if you watch the videos in between the two that I provide, you will be able to easily write the algorithm that the video describes. I hope I could help; if you are not happy with the offered solution, I will be around for alternatives. Have fun.