Feature Engineering Techniques and use cases

 

Feature Engineering Technique	Description	Use Cases
Imputation	Replacing missing values in data with appropriate values.	Dealing with missing data in datasets.
Normalization/Scaling	Scaling numerical features to a standard range.	Ensuring features have the same scale.
Encoding	Transforming categorical variables into numerical format.	Handling categorical data in models.
One-Hot Encoding	Creating binary columns for each category in a feature.	Dealing with nominal categorical data.
Label Encoding	Assigning unique integers to each category in a feature.	Handling ordinal categorical data.
Binning	Grouping numerical values into bins or categories.	Simplifying complex numerical data.
Feature Extraction	Creating new features from existing ones.	Reducing dimensionality, capturing patterns.
Polynomial Features	Generating higher-degree polynomial features.	Capturing nonlinear relationships in data.
Logarithm Transformation	Applying logarithmic function to features.	Handling data with exponential growth patterns.
Interaction Features	Combining two or more features to create new ones.	Capturing relationships between variables.
Time-Based Features	Extracting date and time components from timestamps.	Incorporating time-based patterns in data.
Text Preprocessing	Cleaning and transforming text data into numerical features.	NLP and text analysis tasks.
Feature Scaling	Bringing numerical features to a common scale.	Improving model convergence in some algorithms.
Feature Selection	Selecting the most relevant features for modeling.	Reducing dimensionality, improving model efficiency.
Aggregation	Summarizing data at a higher level of granularity.	Creating aggregated statistics for groups.
Domain-Specific Engineering	Crafting features based on domain knowledge.	Incorporating specific expertise in modeling.