Features in Wellness:
    Pain [0, 1] - no NaNs
    Illness [0, 0.5, 1] - no NaNs
    Menstruation [0, 1] - 16 NaNs, filled with 0. Not a big statistical difference, so this is fine
    Nutrition [0, 0.5, 1] - 837 NaN, filled with 0. Not a useful feature
    NutritionAdj [0, 1] - 745 NaN, filled with 0. Again not useful
    USGMeasurement [0, 1] 168 NaN, filled with 0.
    USG [1.0...] 4382 NaN, not a useful feature
    TrainingReadiness [0..1] - no NaNs

Useful features include Pain, Illness, Menstruation, TrainingReadiness
The others either have too many NaNs present to extract any useful meaning or are just unhelpful features
to begin with, like Nutrition.


Notnormalized_with_0NaN_wellness.csv:

- The only feature of significance that had NaN values put into it were Menstruation, as only 16 NaNs were present
and wouldn't present any statistical difference either way.

- Working in the notnormalized_with_0NaN_wellness csv should be functional, just have to remove any string columns
before putting into algorithms as they are not removed in this version