Learn essential techniques for transforming raw data into informative features for machine learning models. This course covers data preparation, feature creation, scaling, encoding, and selection methods using Python libraries like Pandas and Scikit-learn.
Prerequisites Basic Python, Pandas required
Level:
Data Preparation
Implement strategies for handling missing data and outliers effectively.
Feature Transformation
Apply various scaling and transformation techniques to numerical features.
Categorical Feature Encoding
Compare and apply different methods for encoding categorical variables.
Feature Creation
Generate new features from existing data using interaction terms and polynomial features.
Feature Selection
Utilize filter, wrapper, and embedded methods for selecting relevant features.
Practical Implementation
Apply feature engineering techniques using Pandas and Scikit-learn in practical scenarios.