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Should You Use Imbalanced-Learn in 2025?
Find out the latest evidence on the use of undersampling, oversampling and SMOTE for imbalanced data and whether the Python library imblearn is still relevant.
Is Boruta dead?
The most exhaustive discussion on boruta in machine learning. Learn what it is, its advantages and limitations, and its Python implementation.
Multiple Imputation with Chained Equations (MICE) – what is it?
Discover what MICE (multivariate imputation of chained equations) is, and how to apply it with Python to impute missing data.
KNN imputation of missing values in machine learning
KNN imputation is a simple imputation technique to replace missing data for machine learning while preserving the variable distribution.
Leveraging Data Science for Finance
In this article, I share my experience about leveraging data science for finance while working in a fintech company.
Imputing missing data with Scikit-learn’s simple imputer
Implement the most common missing value imputation methods, like mean, median, and most frequent imputation with sklearn’s simple imputer.
Data in Science, Interpreting its Meaning and Significance
Scientific meaning of data stands as a cornerstone in...
Partial Dependence Plots with Python: A Comprehensive Guide
Partial dependence plots are a model agnostic...
Understanding Permutation Feature Importance for Model Interpretation
Permutation feature importance is a technique used in...