Hello guys, sorry to disturb you all; can someone explain the machine learning pipeline please... I have some difficulties to understand the step of data preprocessing and feature engineering, both involve the manipulation of data, how to distinguish between them.
#Machine learning pipeline
raw hearth
raw hearth
anyone that can help please
north sun
roughly, from what I remember: data pre-processing includes data cleaning, missing values (removal or imputation techniques) but also standardization if applicable etc and feature engineering means that you use your processed data to build features such as standard deviation, mean but also more domain specific features etc.
naturally your features can only be constructed after you have prepared your data