Slumbr - Sleep Quality Monitoring and Assessment System

Abstract

Slumbr is a sleep quality monitoring and assessment system designed to help users understand and evaluate their sleep patterns using machine learning. The system collects sleep-related information such as sleep duration, sleep quality indicators, lifestyle factors, and other relevant attributes to assess overall sleep quality. The project applies data preprocessing and machine learning techniques to analyze sleep-related data and generate sleep quality predictions. A user-friendly interface is provided to allow users to enter relevant information and view the resulting assessment. The system is intended as a software-based assessment and monitoring tool and does not replace professional medical diagnosis or clinical evaluation. This report presents the design, development, implementation, and evaluation of the Slumbr system, including data preprocessing, feature preparation, machine learning model development, prediction, application integration, and testing. The project demonstrates the application of machine learning techniques to a practical sleep quality assessment problem.