In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a ...

ABSTRACT: This study investigates the impact of wage structure on employee satisfaction, motivation, and retention in Thailand’s textile manufacturing industry. Despite the recognized importance of ...

ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...

The current project uses a Random Forest model for yield prediction. While this works, we can also implement the task using a Deep Learning Artificial Neural Network (ANN) for potentially better ...

Running Python scripts is one of the most common tasks in automation. However, managing dependencies across different systems can be challenging. That’s where Docker comes in. Docker lets you package ...

When you install Python packages into a given instance of Python, the default behavior is for the package’s files to be copied into the target installation. But sometimes you don’t want to copy the ...

Implement Linear Regression in Python from Scratch ! In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code ...

In this video, we will implement Multiple Linear Regression in Python from Scratch on a Real World House Price dataset. We will not use built-in model, but we will make our own model. This can be a ...

Adaptive Lasso is an extension of the standard Lasso method that provides improved feature selection properties through weighted L1 penalties. It assigns different weights to different coefficients in ...

1 Institute of Geology and Geophysics, Ministry of Science and Education, Baku, Azerbaijan 2 School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan In recent years, seismological ...