How To Deal With Null Values For A Health Prediction Dataset?

hi! So I have this dataset where the objective is to predict a student’s health risk, but I’m a lil confused about how to handle the null values. These are the % of null values for the columns:

 id 0.000000 health_condition 0.000000 sleep_duration 11.012943 heart_rate 1.135073 bmi 2.013946 calorie_expenditure 7.658878 step_count 2.016554 exercise_duration 1.000017 water_intake 6.300211 diet_type 1.000017 stress_level 12.000064 sleep_quality 8.452690 physical_activity_level 5.306715 smoking_alcohol 4.141791 gender 3.097141 dtype: float64id 

What would you recommend I do for these values? If I were to drop the columns <5%, I would be losing nearly 100,000 values (out of 700,000) which I don’t think is all that good. I thought of using K-means to fill the null BMI values but I don’t know.

I would appreciate any advice! Thanks 🙂