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.
Winsorization: Handling Outliers in Machine Learning
Learn to handle outliers effectively with Winsorization—a powerful technique that keeps your data intact. This guide includes Python code examples and compares Winsorization to other common outlier treatment methods.
A Comprehensive Guide to Complete Case Analysis
Learn Complete Case Analysis (CCA) for handling missing data in machine learning, including advantages and limitations and Python examples.
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.
Your Guide to Missing Values Imputation
Find out more about missing values, how they appear in the data, and how you can replace them with unbiased estimates for machine learning.
Target Encoder: A powerful categorical encoding method
Most machine learning models don't natively handle...
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.
Master Data Binning in Python using Pandas
Find out what data binning is, why we do it, and how to implement it in Python.
Unlock Data’s Potential: A Step-by-Step Data Transformation Guide
Data is the lifeblood of any organization. Learn how to transform it to unlock its full potential.