Analyzing and Predicting Sleep Cycles: A Data-Driven Approach to Sleep Pattern Optimization

Sleep is an essential factor in maintaining overall health and well-being, yet modern lifestyles often disrupt natural sleep cycles. This project, Sleep Cycle Analysis Based on Daily Routine , aims to uncover the relationship between daily routines and sleep quality. The study utilizes a dataset comprising variables such as screen time, physical activity, meal schedules, work hours, and sleep duration. By applying the Random Forest algorithm, a robust machine learning model is developed to analyze and predict the impact of these factors on sleep patterns. The algorithm’s ability to handle non-linear relationships and feature importance analysis makes it ideal for uncovering complex interactions within the data. Results are visualized to provide insights and actionable recommendations for improving sleep quality. This research contributes to understanding how daily habits influence sleep, enabling data-driven strategies for healthier lifestyles.