feature engineering is indeed both an art and science to transform data into features for feeding into models sometimes you need combination of domain knowledgeexperienceintuitionand mathematical transformations to give you the features you need by solving more problems over timeyou will gain the experience you need to know what features might be best suited for problem hence do not be overwhelmedpractice will make you master feature engineering with time the following list depicts some examples of engineering features deriving person' age from birth date and the current date getting the average and median view count of specific songs and music videos extracting word and phrase occurrence counts from text documents extracting pixel information from raw images tabulating occurrences of various grades obtained by students the final quote to whet your appetite on feature engineering is from renowned kagglerxavier conort most of you already know that tough machine learning problems are often posted on kaggle regularly which is usually open to everyone xavier' thoughts on feature engineering are mentioned as follows "the algorithms we used are very standard for kagglers we spent most of our efforts in feature engineering we were also very careful to discard features likely to expose us to the risk of over-fitting our model --xavier conort this should give you good idea what is feature engineeringthe various aspects surrounding it and very basic introduction into why do we really need feature engineering in the following sectionwe will expand more on why we need feature engineeringits benefits and advantages why feature engineeringwe have defined feature engineering in the previous section and also touched upon the basics pertaining to the importance of feature engineering let' now look at why we need feature engineering and how can it be an advantage for us when we are building machine learning models and working with data better representation of datafeatures are basically various representations of the underlying raw data these representations can be better understood by machine learning algorithms besides thiswe can also often easily visualize these representations simple example would be to visualize the frequent word occurrences of newspaper article as opposed to being totally perplexed as to what to do with the raw textbetter performing modelsthe right features tend to give models that outperform other models no matter how complex the algorithm is in general if you have the right feature seteven simple model will perform well and give desired results in shortbetter features make better models essential for model building and evaluationwe have mentioned this numerous times by nowraw data cannot be used to build machine learning models get your dataextract featuresand start building modelsalso on evaluating model performance and tuning the modelsyou can reiterate over your feature set to choose the right set of features to get the best model