we will now look at definition of feature engineering by dr jason brownleedata scientist and ml practitioner who provides lot of excellent resources over at regard to machine learning and data science dr brownlee defines feature engineering as follows "feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive modelsresulting in improved model accuracy on unseen data --dr jason brownlee let' spend some more time on this definition of feature engineering it tells us that the process of feature engineering involves transforming data into features taking into account several aspects pertaining to the problemmodelperformanceand data these aspects are highlighted in this definition and are explained in further detail as follows raw datathis is data in its native form after data retrieval from source typically some amount of data processing and wrangling is done before the actual process of feature engineering featuresthese are specific representations obtained from the raw data after the process of feature engineering the underlying problemthis refers to the specific business problem or usecase we want to solve with the help of machine learning the business problem is typically converted into machine learning task the predictive modelstypically feature engineering is used for extracting features to build machine learning models that learn about the data and the problem to be solved from these features supervised predictive models are widely used for solving diverse problems model accuracythis refers to model performance metrics that are used to evaluate the model unseen datathis is basically new data that was not used previously to build or train the model the model is expected to learn and generalize well for unseen data based on good quality features thus feature engineering is the process of transforming data into features to act as inputs for machine learning models such that good quality features help in improving the overall model performance features are also very much dependent on the underlying problem thuseven though the machine learning task might be same in different scenarioslike classification of -mails into spam and non-spam or classifying handwritten digitsthe features extracted in each scenario will be very different from the other by now you must be getting good grasp on the idea and significance of feature engineering always remember that for solving any machine learning problemfeature engineering is the keythis in fact is reinforced by prof pedro domingos from the university of washingtonin his paper titled" few useful things to know about machine learningavailable at cacm pdfwhich tells us the following "at the end of the daysome machine learning projects succeed and some fail what makes the differenceeasily the most important factor is the features used --prof pedro domingos