Insights and thoughts on design, development, and creativity
Learn about the CRISP-DM framework developed by IBM, its structured approach to business analytics, and how it has personally helped me understand and tackle data analytics problems.
Explore how Scikit-Learn powers machine learning across industries, from healthcare diagnostics to music recommendation systems and financial investing.
Compare two popular impurity measures—Entropy and Gini Impurity—used in decision tree algorithms. Understand their differences, advantages, and when to use each in machine learning.
Explore the differences between Lasso and Ridge Regression, two popular regularization techniques in linear models. Learn about their strengths, weaknesses, and implementation examples in Python.
Learn how to use Grid Search to find the best hyperparameters for your machine learning model. Improve model performance with this powerful technique.
Discover how to apply Randomized Search for hyperparameter optimization in your machine learning models.
Explore the fundamental differences between regression and classification tasks in machine learning, along with models that are best suited for each type.
Learn how to test for main effects and interactions using Python's statsmodels with the public Tips dataset.