features raw data is hardly used to build any machine learning modelmostly because algorithms can' work with data which is not properly processed and wrangled in desired format features are attributes or properties obtained from raw data each feature is specific representation on top of the raw data typicallyeach feature is an individual measurable attribute which usually is depicted by column in two dimensional dataset each observation is depicted by row and each feature will have specific value for an observation thus each row typically indicates feature vector and the entire set of features across all the observations forms two-dimensional feature matrix also known as feature set features are extremely important toward building machine learning models and each feature represents specific chunk of representation and information from the data which is used by the model both quality as well as quantity of features influences the performance of the model features can be of two major types based on the dataset inherent raw features are obtained directly from the dataset with no extra data manipulation or engineering derived features are usually what we obtain from feature engineering where we extract features from existing data attributes simple example would be creating new feature age from an employee dataset containing birthdate by just subtracting their birth date from the current date the next major section covers more details on how to handleextractand engineer features based on diverse data types models features are better representations of underlying raw data which act as inputs to any machine learning model typically model is comprised of data featuresoptional class labels or numeric responses for supervised learning and machine learning algorithm the algorithm is chosen based on the type of problem we want to solve after converting it into specific machine learning task models are built after training the system on data features iteratively till we get the desired performance thusa model is basically used to represent relationships among the various features of our data typically the process of modeling involves multiple major steps model building focuses on training the model on data features model tuning and optimization involves tuning specific model parametersknown as hyperparameters and optimizing the model to get the best model model evaluation involves using standard performance evaluation metrics like accuracy to evaluate model performance model deployment is usually the final step whereonce we have selected the most suitable modelwe deploy it live in production which usually involves building an entire system around this model based on the crisp-dm methodology will focus on these aspects in further detail revisiting the machine learning pipeline we covered the standard machine learning pipeline in detail in which was based on the crispdm standard let' refresh our memory by looking at figure - which depicts our standard generic machine learning pipeline with the major components identified with the various building blocks