Sole Galli
Best Data Science Competition Websites in 2026
Want to practice your data science skills? These data science competition websites have the projects...
Should You Use Imbalanced-Learn in 2025?
I discuss the latest evidence on the use of undersampling and SMOTE for imbalanced data...
Is Boruta dead?
The most exhaustive discussion on boruta in machine learning. Learn what it is, 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...
Leveraging Data Science for Finance
In this article, I share my experience about leveraging data science for finance while working...
Imputing missing data with Scikit-learn’s simple imputer
Implement the most common missing value imputation methods, like mean, median, and most frequent imputation...
Data in Science, Interpreting its Meaning and Significance
Discover the scientific meaning of data: its role in research, analysis, and impact in shaping...
Partial Dependence Plots with Python: A Comprehensive Guide
Discover partial dependence plots, how they help you understand your machine learning model's predictions, and...
Understanding Permutation Feature Importance for Model Interpretation
Permutation feature importance is obtained by randomly shuffling the feature values and assessing the decrease...
Feature Selection with Wrapper Methods in Python
Learn what wrapper methods for feature selection are, their advantages and limitations, and how to...
Feature Selection with Filter Methods in Python
Discover what filter methods for feature selection are, their advantages and limitations, and how to...
Feature Selection with Embedded Methods
Learn what embedded methods for feature selection are, their advantages and limitations, and how to...
Cost-Sensitive Learning: Beyond the Accuracy in Imbalanced Classification
Find out what cost-sensitive learning is and how to implement it with Python.
Mastering Feature Importance in Machine Learning with Python
Find out how to calculate feature importance scores with Python.
Feature Importance vs. Feature Selection: How are they related?
Understand the relationship and difference between feature importance and feature selection.
Overcoming Class Imbalance with SMOTE: How to Tackle Imbalanced Datasets in Machine Learning
Find out more about SMOTE, how it works, and how to implement it in Python....
The Role of Undersampling in Tackling Imbalanced Datasets in Machine Learning
Undersampling techniques for imbalanced datasets in Python.
Exploring Oversampling Techniques for Imbalanced Datasets
Oversampling techniques for imbalanced datasets in Python.
Unlocking the Power of Time Series Forecasting in Machine Learning and Data Science Applications
Overview of statistical and machine learning models for time series forecasting.
Dealing with Imbalanced Datasets in Machine Learning: Techniques and Best Practices
Discover the techniques used to handle imbalanced datasets in machine learning, what they actually do,...
Feature scaling in machine learning: Standardization, MinMaxScaling and more…
Discover why and how we scale variables in Python for machine learning.
Master Data Binning in Python using Pandas
Find out what data binning is, why we do it, and how to implement it...
Hyperparameter Tuning For Machine Learning
Learn about grid, random search, and Bayesian optimization for hyperparameter tuning for machine learning, and...
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.
One-hot encoding categorical variables
Discover different variants of one hot encoding, including encoding of specific or frequent categories, and...
Data science and machine learning books
Discover five books that expose the controversial policies and surveillance abuses of companies that use...
Feature selection in machine learning with Python
Discover multiple algorithms for feature selection in machine learning and how to implement them in...
Recursive feature elimination with Python
Recursive feature elimination is the process of selecting features sequentially, in which features are removed...
Feature selection with Lasso in Python
The Lasso regularization can be used to select features in machine learning since it has...
Mutual information with Python
What is the mutual information, how can we calculate it in Python, and how do...
Data discretization in machine learning
Why and how should we discretize data in machine learning.
Variance stabilizing transformations in machine learning
The logarithm, power, and square root are variance stabilizing transformations. How and why are they...
Population Stability Index and feature selection in Python
Find out what the Population Stability Index is and how to use it to monitor...
Feature Selection in Machine Learning
Discover different methods for feature selection for machine learning, what their advantages and limitations are,...
Feature engineering for machine learning: What is it?
Discover different methods for feature engineering for machine learning, what their advantages and limitations are,...