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How to Handle Missing Data With Scikit Learn's Imputer Module

In this article, you will learn how to use Scikit-Learn Imputer module to handle missing data to streamline the data
KDnuggets 2:43 pm on June 5, 2024

The Iterative Imputer from scikit-learn is an experimental feature to iteratively impute missing data by modeling each feature as dependent on others. The example demonstrates its usage: initializing the imputer, fitting it to data, transforming the dataset, and rounding results for display.

iterative_imputer = IterativeImputer(max_iter=1, random_state=0)
iterative_imputed_data = iterative_imputer.fit_transform(df)
iterative_imputed_df = round(pd.DataFrame(iterative_imputed_dat, columns=df.columns,2)
print(iterative_imputed_df)

https://www.kdnuggets.com/how-to-handle-missing-data-with-scikit-learns-imputer-module

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How to Handle Missing Data With Scikit Learn's Imputer Module

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