Engineering categorical data and other features
This section will explore the handling of categorical variables in feature engineering for data science and machine learning projects. Categorical variables contain discrete values that represent different groups or categories. Effectively preprocessing and engineering these variables is essential to extract valuable insights and enhance the predictive power of machine learning models. We will dive into various techniques and best practices to transform categorical variables into meaningful numerical representations.
One-hot encoding
One-hot encoding is a popular technique for converting categorical variables into binary vectors. Each category is represented as a binary feature, with a value of 1 if the data point belongs to that category and 0 otherwise. For example, consider a categorical feature, Color, with the categories Red, Blue, and Green. After one-hot encoding, this feature will be split into three binary features –...
