Feature engineering A-Z

Let's say we have the data on consumption statistics of some kind and it has a time stamp on it: In this example, the "Date" column could easily be used to extract additional features and generate powerful insights such as variations of consumption on weekdays or weekends or at a particular time in the year (see yellow highlights below). Feature synthesis is the opposite of feature extraction. In this case, one or more features are combined into creating new features that are more informative than they are individually. Let's say, in a house price dataset you have two columns: floor_space (sqft) and total_house_price (US$). You could use them individually in your analysis but you could also create a new calculated feature called price_per_sqft (US$/sqft). Feature scaling/transformation refers to a variety of methods applied in data preprocessing to rescale or normalize data into a different range.