Dealing with missing values by filling them with averages, medians, or educated guesses so the model doesn't crash or become biased.
In the world of machine learning, there is a common saying: "Garbage in, garbage out." You can have the most sophisticated neural network on the planet, but if the data you feed it is messy or irrelevant, the results will be mediocre at best. This is where comes in. It is the process of using domain knowledge to transform raw data into "features" that better represent the underlying problem to the predictive model. While algorithms are the engines of AI, feature engineering is the fuel that makes them run efficiently. Why Features Matter More Than Models Feature Engineering for Machine Learning and Da...
Identifying data points that are so extreme they might skew the model’s understanding of "normal" behavior. Dealing with missing values by filling them with