This project develops an intelligent Python-based framework for monitoring daily energy patterns to optimize personal productivity. Unlike traditional time-centric tools, it analyzes individual physiological fluctuations and their impact on cognitive performance. The system allows users to track energy levels on a 5-point scale, storing data in a secure, local SQLite database. By leveraging machine learning, specifically the KNN algorithm and Linear Regression, it identifies recurring "Peak Performance Windows". Personalized scheduling recommendations are generated via a Streamlit dashboard, aligning high-cognition tasks with peak energy states. The framework integrates Pandas for data processing and Scikit-learn for pattern recognition and predictive analysis. Interactive visualization tools are used to present energy trends and analytical results in a clear and intuitive manner. Developed according to the SETF methodology, the study demonstrates a 67% correlation between energy and task completion. Findings show a 42% increase in productivity scores compared to traditional time-blocking methods. The project introduces an energy-based model as a scientific alternative to traditional time-based management. It provides a deeper understanding of the relationship between biological rhythms and behavioral performance. This work paves the way for more intelligent, personalized, and sustainable productivity management systems. This work was conducted at Arab International University(AIU),Syria. The official website of the university is :https://www.aiu.edu.sy
Hussine Mourtada (Mon,) studied this question.