This page estimates your calorie deficit using a pre-fit OLS multiple linear regression model. A positive result means a predicted deficit (good). A negative result means a predicted surplus. This is an educational demo, not medical or nutrition advice. See README github.com/abmarz/predical for more info.
Use the same units/scales described under each variable to ensure the most precise and accurate prediction
Length of the session(s) today (collective if more than one) in hours (1.5 is an hour and a half, 1.75 is an hour and 45 minutes, and so on and so forth) This is zero if you haven't trained today.
Primary workout type for the session (Cardio is the reference level so if you only did cardio this is None/Other) For multiple types (i.e. HIIT and Strength) in one day, select the longest or most frequent.
Body weight at the time of the session.
Age in years. Floats won't make much of a difference here so don't even think about it ;p .
Scale of 1–5: 1 = not experienced (need help), 3 = moderately experienced (can manage but with doubts), 5 = very experienced (confident in sessions).
Number of workout days per week. Floats actually help here because sometimes we get in a solid workout but a half-baked one as well. Use the precise number of workouts per week as a baseline and go holistically as to what decimal point to add on to that baseline
Dataset feature on a 1–4 categorical scale. The dataset this model is trained on unfortunately did not provide a definition for this variable. The closest inference as to what it denotes is the level that best matches general daily activity (outside the workout type). 1 being sedentary, 4 being very active.
Protein intake in grams today.
Sugar intake in grams today.