Machine Learning to Examine the Foraging Periods of Bees

EasyChair Preprint no. 12353

Abstract

This research project on Machine Learning to Examine the Foraging Periods of Bees aims to: 1) Study the number of honey bee foragers and data preprocessing for analysis, 2) Analyze the relationship between the number of honey bee foragers during different time intervals. The research process begins by defining periods from 5:00 a.m. to 4:00 p.m. for counting the number of honey bee foragers, with intervals of 30 minutes. Data preprocessing techniques are applied, and a suitable data schema is designed for data science purposes. In the subsequent steps, the Polynomial Regression algorithm is employed as part of the Machine Learning process to create a model that fits well with data exhibiting polynomial relationships. The relationship between the independent and dependent variables is considered in the form of a polynomial equation with a maximum degree of positivity. This research sets the polynomial degree to 7, yielding results with significant correlation values. The model is capable of efficiently examining the data's variability daily. This is evident from the R-squared and adjusted R-squared values, which approach 1. The insights derived from the analysis are valuable and can be integrated into the next research phase concerning bees.

Keyphrases: data preprocessing, machine learning, polynomial regression

@Booklet{EasyChair:12353,
  author = {Nattakan Thonhual},
  title = {Machine Learning to Examine the Foraging Periods of Bees},
  howpublished = {EasyChair Preprint no. 12353},

  year = {EasyChair, 2024}}