Luckily enough, if we possess sufficient domain knowledge, we can apply it in creating domain-specific features; we utilize our business experience and insights to identify what in the data and formulate what correlates to the prediction target from the data. For example, in Chapter 9, Stock Prices Prediction with Regression Algorithms, we designed and constructed feature sets for stock prices prediction based on factors investors usually look at when making investment decisions.

While particular domain knowledge is required, sometimes we can still apply some general tips in this category. For example, in fields related to customer analytics, such as market and advertising, time of the day, day of the week, month are usually important signals. Given a data point with the value 2017/02/05 in the date column and 14:34:21 in the time column, we can create new features including afternoon, Sunday, and February. In retail, information over a period of time is usually aggregated to provide better insights. The number of times a customer visits a store for the past three months, average number of products purchased weekly for the previous year, for instance, can be good predictive indicators for customer behavior prediction.