Warm Greetings from 360DigiTMG. This Short informative video is about Feature Engineering.

Feature Engineering on numeric data: find a better way of representing the numeric variable in the model

Feature Engineering on Categorical data: Transforming each categorical attribute into a numeric representation

Feature Engineering on Text data: If we can use these contexts as features and feed them to our model then the model will be able to understand the sentence better

Feature Engineering on Image data: The process of taking raw data and extracting features that are useful for modeling

Feature Extraction: The process of transforming raw data into numerical features that can be processed while preserving the information in the original data set.

Feature Selection: The process of reducing the number of input variables when developing a predictive model.

Feature Scaling: Method used to normalize the range of independent variables or features of data

Feature Transformation: Mathematical transformation in which we apply a mathematical formula to a particular column (feature) and transform the values

Feature Construction: Feature construction (also known as constructive induction or attribute discovery) enriches data by adding derived features.

Feature Engineering on Temporal Data: The concept of deriving features from the process of raw data"

Feature Engineering | Learn Data Science using Animation

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