Leveraging advanced machine learning techniques and domain expertise, this project focuses on optimizing retail inventory forecasting to enhance sales and reduce stock-outs. Using Python and frameworks like SARIMA, Prophet, and Pytorch, I developed scalable forecasting models tailored for dropship SKUs and retail hierarchies. These models improved accuracy by 6% through revamped Seasonal ARIMA and trend analysis, ensuring optimal inventory allocation and replenishment.

The solution also included imputation logic enhancements, improving data quality for informed decision-making. Additionally, I designed clustering algorithms using Dynamic Time Warping, enabling better segmentation of stores based on sales patterns, which supported precise forecasting. The project integrated tools like Tableau and Looker Studio for intuitive data visualization, empowering stakeholders with actionable insights to drive sales and efficiency.

With a focus on scalability and operational readiness, this AI-driven inventory forecasting solution delivers measurable business impact, addressing key retail challenges with cutting-edge technology.