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machine learning algorithms dont always work so well on raw data part of our jobs as engineers and scientists is to transform the raw data to make the behavior of the system more obvious to the machine learning algorithm this is called feature engineering feature engineering starts with your best guess about what features might influence the thing youre trying to predict after that its an iterative process where you create new features add them to your model and see if the result improved lets take a simple example where we want to predict whether a flight is going to be delayed or not in the raw data we have information such as the month of the flight the destination and the day of the week if I fit a decision tree just to this data Ill get an accuracy of 70% what else could we calculate from this data that might help improve our predictions well how about the number of flights per day there are more flights on some days than others which may mean theyre more likely to be delay
