The statement of my exercise says : distribution of feature_3 is a hint of how the data is generated. I try to understand what I should infer from that for the rest of my ETL or ML model..
I have plotted the Q-Q plot of this feature. The distribution seems fairly normal. What can I infer from this information for the rest of my ETL or ML model ?
Most of machine learning models assume an underlying data distribution for them to function well.
So, coming back to your question, there are some ML techniques that assume that the data fed into them are normally (or Gaussian) distributed. These are Gaussian naive Bayes, Least Squares based (regression) models, LDA, QDA. So the statement you are referring to implies that your data was generated using such an algorithm and are normally distributed. See, here for a brief visual explanation of this and here for an explanation on the importance of normal distribution in Machine Learning.
In addition, please note that there are other algorithms (e.g. SVMs, Random Forests used for regression/classification, Decision trees, Gradient Boosted Trees etc) that do not assume any type of underlying data distribution.
