pyMarketLab offers comprehensive feature engineering tools that allow users to perform data transformations, determine feature importance, and select the most relevant features for their models. Explore the capabilities that make pyMarketLab a powerful tool for feature engineering.
Transform continuous data into categorical bins to simplify analysis and model building.
Create polynomial features to capture non-linear relationships in your data.
Apply log transformations to stabilize variance and normalize data distributions.
Convert categorical variables into numerical format using label encoding and one-hot encoding techniques.
Standardize or normalize your data to ensure all features contribute equally to the model.
Reduce the dimensionality of your data while retaining the most important information.
Determine the importance of features using methods such as univariate selection and KNN-based techniques.
Select the most relevant features for your models to improve performance and reduce complexity.