Elemental Motor Works

overview at elemental motor works, i built predictive models from the ground up to solve a real operational problem, forecasting hourly rental demand in a market where getting it wrong meant lost revenue or idle fleet. short engagement, focused mandate, tangible output. the work demand forecasting models implemented multiple regression-based predictive models in r to forecast hourly rental demand, incorporating time-series patterns, seasonal effects, and external factors like weather and local events to improve forecast accuracy and operational planning. feature engineering engineered high-value predictive features from raw transactional and external datasets, including lag variables, categorical encodings, and interaction terms. developed and compared linear regression and random forest models using rmse and r² to select the optimal approach for deployment. business recommendations & dashboard delivered data-backed recommendations on dynamic pricing strategies and targeted promotional windows. built an interactive tableau dashboard to visualize demand forecasts, kpi trends, and scenario analyses, enabling stakeholders to explore insights in real time. impact improved fleet utilization and pricing strategy through accurate demand forecasting dynamic pricing recommendations tied directly to model outputs interactive dashboard enabling real-time scenario planning for leadership skills demonstrated r · predictive modeling · random forest · linear regression · feature engineering · tableau · time-series analysis · business intelligence