Dealing with missing values by filling them with averages, medians, or educated guesses so the model doesn't crash or become biased.
If one feature is measured in millions (like house prices) and another in single digits (like the number of bedrooms), the model might mistakenly think the larger numbers are more important. Scaling brings everything into a consistent range. Feature Engineering for Machine Learning and Da...
Feature engineering isn't a single step; it’s a toolbox of different techniques: Dealing with missing values by filling them with
Should we dive deeper into a specific technique like or perhaps look at automated feature engineering tools? Feature Engineering for Machine Learning and Da...