Python Tutorial: Numeric variables

Want to learn more? Take the full course at https://learn.datacamp.com/courses/feature-engineering-for-machine-learning-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- As mentioned in the previous lesson, most machine learning models will require your data to be in numeric format. However, even if your raw data is all numeric, there is still a lot you can do to improve your features. Numeric features can be used to represent a huge array of different characteristics and measurements. Pretty much anything that can be quantitatively measured can be recorded as numeric data. For example, age, the price of an item, counts, and even spatial data such as coordinates. Depending on the use case, numeric features can be treated in several different ways. We will work through a few of the considerations and possible feature engineering steps to keep in mind when dealing with numeric data. One of the first questions you should ask when working with numeric features is whether the magnitude of the feature is its most important trait, or just its direction. For example, if you had a dataset of restaurant health and safety ratings containing the number of times a restaurant had major violations, you might care far more about whether the restaurant had any major violations at all (as you would rather not take any chances), over whether it was a repeat offender. Looking at this toy dataset containing restaurant IDs and the number of times they had major violations, we can see that some restaurants have no major violations but many have one or more. We will be creating a new binary column representing whether or not a restaurant committed any violation. Here we first create a new column Binary_Violation and set it to zero. Then, we use the dot loc notation to find all rows where Number_of_Violations is greater than zero and set the Binary_Violation column to 1. As you can see here, all rows where Number_