Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

In an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This ...

Abstract: Industrial and agricultural use requires fruit colour, size, shape, and texture classification. Classification enhances sorting, grading, quality control, and customer satisfaction.

While many countries have registries of livestock farms, it can be challenging to obtain information on their primary production type. For example, for Swiss farms registered as keeping cattle, a ...

ABSTRACT: Background: Chronic pain management presents significant challenges in clinical practice, particularly in selecting pharmacological treatments that balance efficacy and safety. GABAergic and ...

This repository contains the code for a K-Nearest Neighbors (KNN) model built to classify customer segments in Türkiye using the teleCust1000T dataset. The project includes data cleaning, ...

from pycaret.datasets import get_data data = get_data('poker') from pycaret.classification import * s = setup(data, target = 'CLASS', session_id = 123) from sklearnex ...