Data Preprocessing
Should You Use Imbalanced-Learn in 2025?
I discuss the latest evidence on the use of undersampling and SMOTE for imbalanced data...
Winsorization: Handling Outliers in Machine Learning
Handle outliers with Winsorization, a powerful data preprocessing technique. Includes Python code examples.
A Comprehensive Guide to Complete Case Analysis
Learn Complete Case Analysis (CCA) for handling missing data in machine learning, including advantages and...
Multiple Imputation with Chained Equations (MICE) – what is it?
Discover what MICE (multivariate imputation of chained equations) is, and how to apply it with...
KNN imputation of missing values in machine learning
KNN imputation is a simple imputation technique to replace missing data for machine learning while...
Your Guide to Missing Values Imputation
Find out more about missing values, how they appear in the data, and how you...
Target Encoder: A powerful categorical encoding method
Target encoder is Python implementation of the target encoding method for highly cardinal categorical variables....
Imputing missing data with Scikit-learn’s simple imputer
Implement the most common missing value imputation methods, like mean, median, and most frequent imputation...
Master Data Binning in Python using Pandas
Find out what data binning is, why we do it, and how to implement it...
Unlock Data’s Potential: A Step-by-Step Data Transformation Guide
Data is the lifeblood of any organization. But not in its raw state. Data transformation...
Mastering data preprocessing: Techniques and best practices
Discover how to preprocess your data to make it suitable for machine learning.