Youcef Elmouhtadi LAGRAA

Collaborated with sports scientists to implement and validate new formulas and approaches, improving the correlation between heart rate and speed spectra for endurance athletes. - Improved heart rate-speed correlation by 5% by implementing new data cleaning and feature engineering approaches, achieving 68% model accuracy for athlete performance prediction. - Optimized data preprocessing by identifying and removing warmup time and recovery phases, enhancing dataset relevance for active performance analysis. - Developed a fatigue-adjusted heart rate formula to account for physiological decline, increasing data accuracy for prolonged exercise insights. - Normalized speed data for varied terrains (uphill/downhill), enabling fair performance comparison across different running environments. - Built and validated a linear regression model to predict heart rate-speed relationships, delivering actionable insights for athlete training optimization. End-to-End Data Workflow: Led data visualization, feature engineering, and model validation, translating complex physiological data into actionable insights for athlete performance optimization.