Recently, a member of Data School Insiders asked the following question in our private forum: I'm new to Machine Learning. When I want to create a predictive model, what are the techniques I should use to do "feature engineering"? Great question! Let's start with the basics: What is feature engineering? Feature engineering is the process of creating features (also called "attributes") that don't already exist in the dataset. This means that if your dataset already contains enough "useful" features, you don't necessarily need to engineer additional features. But what is a "useful" feature? It's a feature that your Machine Learning model can learn from in order to more accurately predict the value of your target variable. In other words, it's a feature that helps your model to make better predictions! Datetime example Let's pretend you have a dataset with a "datetime" column: If your goal is to predict the temperature, you might use the datetime column to engineer an integer hour feature (0-23), since the hour of the day is a useful predictor of the temperature.


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