Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms
This work addresses sleep quality prediction for personalized recommendations, but it is incremental as it applies existing methods to a new dataset.
The study examined how integrating feature selection techniques with regression algorithms affects sleep quality prediction, finding optimal combinations for performance.
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.