Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.

Shrinking governing boards, diversifying beyond alumni and business executives, and mandating free speech vetting in leadership hires could reverse the chill on free speech and equip trustees to ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

These are my go-to libraries for Python data crunching.

Check out which players are running unsustainably hot or cold going into Week 13, and what it might mean for your start-sit decisions.

Introduction Mothers’ experiences at birth and respectful maternal care are critical to achieving Sustainable Development Goal number 3 in Tanzania. However, little is known about the differences in ...

There is a set of points in the plane whose elements correspond to the observations that are used to generate a simple least-squares regression line. Each value of the independent variable in the ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Linear to Logistic regression Introduction In this lesson, you'll be introduced to the logistic regression model. You'll start with an introductory example using linear regression, which you've seen ...

This lesson will be more of a code-along, where you'll walk through a multiple linear regression model using both statsmodels and scikit-learn. Recall the initial regression model presented. It ...