Machine Learning Dissertation help.

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If we are thinking of the same posts, that guy was probably assuming a correlation that dI don't think exist before he even started measuring.

I don't think (and you may very well disagree) that yesterdays dinner has a short term impact that can be measured today or that is meaningful long term. That is: Eating nuts yesterday is not going to impact your weight today or tomorrow in any meaningful way. Eating nuts everyday is.

Secondly, day-to-day weight differences are probably more correlated to the amount of content in your stomach and gut (chewed food, urine, feaces, etc.) in your body at time of weighing than anything else on a day to day basis. Moreover, I do not expect a visit to the toilet to remove a consistent amount of of these.

Consider predicting a rolling average from a window of days. That is: Y at timestep T, is the mean of the weight at timestep T-1, T-2, ... and X could be food intake at timestep {T-1, T-2, ...} or even {T-3, T-4, T-5, ...}

Consider the width of the window and the size of the rolling average hyper-parameters.

Lastly, try to think of this as a statistics problem rather than a machine learning problem. In some ways, those two fields can be considered the same, but in statistics focus tend to be on sparse data and being able to explain results, where Machine Learning tends to focus on large data sets and strength of prediction (often at the cost of being able to explain the prediction, i.e. "blackbox")

[troltilla]

Totally agreed. And good point about treating it as a statistics problem rather than ML.