it is quite evident that based on the sequence of steps depicted in the figurefeatures are first crafted and engineeringnecessary normalization and scaling is performed and finally the most relevant features are selected to give us the final set of features we will cover these three components in detail in subsequent sections following the same sequence as depicted in the figure feature extraction and engineering the process of feature extraction and engineering is perhaps the most important one in the entire machine learning pipeline good features depicting the most suitable representations of the data help in building effective machine learning models in factmore than often it' not the algorithms but the features that determine the effectiveness of the model in simple wordsgood features give good models data scientist approximately spends around to of his time in data processingwranglingand feature engineering for building any machine learning model hence it' of paramount importance to understand all aspects pertaining to feature engineering if you want to be proficient in machine learning typically feature extraction and feature engineering are synonyms that indicate the process of using combination of domain knowledgehand-crafted techniques and mathematical transformations to convert data into features henceforth we will be using the term feature engineering to refer to all aspects concerning the task of extracting or creating new features from data while the choice of machine learning algorithm is very important when building modelmore than oftenthe choice and number of features tend to have more impact toward the model performance in this sectionwe will be looking to answer some questions such as the whywhatand how of feature engineering to get more in-depth understanding toward feature engineering what is feature engineeringwe already informally explained the core concept behind feature engineeringwhere we use specific components from domain knowledge and specific techniques to transform data into features data in this case is raw data after necessary pre-processing and wranglingwhich we have mentioned earlier this includes dealing with bad dataimputing missing valuestransforming specific valuesand so on features are the final end result from the process of feature engineeringwhich depicts various representations of the underlying data let' now look at couple of definitions and quotes relevant to feature engineering from several renowned people in the world of data sciencerenowned computer and data scientist andrew ng talks about machine learning and feature engineering "coming up with features is difficulttime-consumingrequires expert knowledge 'applied machine learningis basically feature engineering --prof andrew ng this basically reinforces what we mentioned earlier about data scientists spending close to of their time in engineering features which is difficult and time-consuming processrequiring both domain knowledge and mathematical computations besides thispractical or applied machine learning is mostly feature engineering because the time taken in building and evaluating models is considerably less than the total time spent toward feature engineering howeverthis doesn' mean that modeling and evaluation are any less important than feature engineering