Sleep Analysis
Take the Next Step Toward Smarter Healthcare
Predicting Sleep Efficiency Using Python and Machine Learning
Division of Computing Science and Mathematics Faculty of Natural Sciences University of Stirling
Oluwatobi Josiah Olufunmilayo
Abstract
Sleep efficiency, a key indication of sleep quality, is the ratio of time spent sleeping to total time spent in bed. Accurate prediction of sleep efficiency is critical for diagnosing sleep problems and improving general well-being. This project uses Python and machine learning to estimate sleep efficiency based on physiological and behavioural data, such as wearable device metrics, sleep studies, and demographics.
The study entails substantial data preparation, feature engineering, and exploratory data analysis to discover key drivers of sleep quality. To get the highest prediction performance, several machine learning models are constructed and refined via hyperparameter tuning, such as linear regression, random forest, and neural networks. To ensure accuracy and reliability, models are tested with metrics such as Root Mean Squared Error (RMSE) and R-squared (R²).
The findings show that machine learning can accurately predict sleep efficiency, emphasizing the importance of parameters such as sleep duration, heart rate variability, and pre-sleep activity. The study gives useful insights into the factors that influence sleep quality and lays the groundwork for the development of individualized therapies to increase sleep efficiency. This initiative demonstrates the value of data-driven approaches in sleep research, paving the path for more effective strategies to improve sleep health via predictive modelling and personalized suggestions.
