R Tutorial: Introduction to feature engineering in R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/feature-engineering-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, my name is Jose Hernandez and I am a data scientist. In this course, you will learn about feature engineering. Feature engineering involves adjusting and reworking the raw predictors you will use to represent a model to better uncover predictor-outcome relationships. This means you are in search of features that, in combination, will be the best representation of the sample data to answer a specific question. We will focus on tabular data, and in this context, feature engineering involves combining or decomposing information within existing features to create new ones. The engineering part of feature engineering means that you will be manually creating new columns in your data. For example, you are given data on adult incomes and are tasked with building a model that adequately predicts whether an individual is below or above a specific salary range. To take a look at existing features, we can use the table() and glimpse() functions. The modeling process will be iterative as you determine which features should be selected as inputs to your model. As you continue exploring your data, you may realize that some of your features are categories in the form of strings and can't be understood as inputs by the algorithm in their current form. The adult-underscore-incomes data contains raw features that are integers like age, and factors or character variables, like workclass. Let's assume you are interested in using the workclass column from the adult-underscore-incomes dataset as a feature in a model that has the same two-class income levels as the target variable. The workclass variable is a categorical string variable with eight classes, which correspond to the sector of a person's job. Most statistical and machine learning models do not understa