Impute Missing Values with Feature Engine

Feature-engine offers a dedicated module for the imputation of missing data, simplifying one of the most common challenges in data preprocessing.

Whether you're dealing with numerical or categorical data, Feature-engine supports various imputation methods, including mean, median, and mode imputation, as well as more advanced techniques like random data extraction and imputation with arbitrary values or strings.

Additionally, Feature-engine provides options to drop observations with missing data or add missing indicators, ensuring your dataset is complete and ready for analysis. These tools are essential for preparing your data for machine learning models and aligning with their expectations regarding data distribution.

Explore how Feature-engine can streamline your data preprocessing workflows and improve the performance of your machine learning models

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