TITLE: Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms 

ABSTRACT: This research aims to examine the usefulness of integrating various feature
selection methods with regression algorithms for sleep quality prediction. A
publicly accessible sleep quality dataset is used to analyze the effect of
different feature selection techniques on the performance of four regression
algorithms - Linear regression, Ridge regression, Lasso Regression and Random
Forest Regressor. The results are compared to determine the optimal combination
of feature selection techniques and regression algorithms. The conclusion of
the study enriches the current literature on using machine learning for sleep
quality prediction and has practical significance for personalizing sleep
recommendations for individuals.
This research aims to examine the usefulness of integrating various feature
selection methods with regression algorithms for sleep quality prediction. A
publicly accessible sleep quality dataset is used to analyze the effect of
different feature selection techniques on the performance of four regression
algorithms - Linear regression, Ridge regression, Lasso Regression and Random
Forest Regressor. The results are compared to determine the optimal combination
of feature selection techniques and regression algorithms. The conclusion of
the study enriches the current literature on using machine learning for sleep
quality prediction and has practical significance for personalizing sleep
recommendations for individuals.
Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms
http://arxiv.org/abs/2303.02467v1