Predictive Demand Forecasting & Resource Allocation

Time-series forecasting and optimization for service demand and resource allocation.

About this project

Independent Research, Feb 2026. Developed predictive models to forecast service demand and resource needs using historical time-series and operational datasets. Cleaned and processed 150k+ records using Python (Pandas, NumPy) and SQL, handling missing values, outliers, and inconsistent formats. Applied regression and time-series forecasting (moving averages, ARIMA) to predict peak usage periods and workload spikes. Designed optimization models to allocate limited resources efficiently under budget and capacity constraints. Built automated reporting dashboards to track KPIs, trends, and forecast accuracy. Reduced manual reporting time with reusable ETL pipelines and scheduled data updates.