more flexibility on data typeswhile is it definitely easier to use numeric data types directly with machine learning algorithms with little or no data transformationsthe real challenge is to build models on more complex data types like textimagesand even videos feature engineering helps us build models on diverse data types by applying necessary transformations and enables us to work even on complex unstructured data emphasis on the business and domaindata scientists and analysts are usually busy in processingcleaning data and building models as part of their day to day tasks this often creates gap between the business stakeholders and the technicalanalytics team feature engineering involves and enables data scientists to take step back and try to understand the domain and the business betterby taking valuable inputs from the business and subject matter experts this is necessary to create and select features that might be useful for building the right model to solve the problem pure statistical and mathematical knowledge is rarely sufficient to solve complex real-world problem hence feature engineering emphasizes to focus on the business and the domain of the problem when building features this listthough not an exhaustive onegives us pretty good insight into the importance of feature engineering and how it is an essential aspect of building machine learning models the importance of the problem to be solved and the domain is also pretty important in feature engineering how do you engineer featuresthere are no fixed rules for engineering features it involves using combination of domain knowledgebusiness constraintshand-crafted transformations and mathematical transformations to transform the raw data into desired features different data types have different techniques for feature extraction hence in this we focus on various feature engineering techniques and strategies for the following major data types numeric data categorical data text data temporal data image data subsequent sections in this focus on dealing with these diverse data types and specific techniques which can be applied to engineer features you can use them as reference and guidebook for engineering features from your own datasets in the future another aspect into feature engineering has recently gained prominence hereyou do not use handcrafted features butmake the machine itself try to detect patterns and extract useful data representations from the raw datawhich can be used as features this process is also known as auto feature generation deep learning has proved to be extremely effective in this area and neural network architectures like convolutional neural networks (cnns)recurrent neural networks (rnns)and long short term memory networks (lstmsare extensively used for auto feature engineering and extraction let' dive into the world of feature engineering now with some real-world datasets and examples