We have successfully engineered a state-of-the-art predictive system, employing advanced deep learning techniques, to forecast return rates for a vast inventory of over 100,000 apparel items. By integrating our system with an interactive API, we have empowered users with domain knowledge to seamlessly access and benefit from accurate return rate predictions.

Created a highly optimized deep learning-based predictive system to accurately forecast the demand for over 100K apparel goods. Implemented an interactive API to enable efficient utilization of the system by users with domain knowledge, providing them with demand predictions.

A well-trained intelligence system is applied to cluster time series data that understand the procedure to be followed in multiple time series clustering scenarios. Advanced Deep learning-enabled time series models are provided with the system to forecast time-based values and understand future trends.

We have developed robust, reliable, and scalable classification models using state-of-the-art technologies. These models effectively segregate new arrivals before their launch. To enhance efficiency, we have seamlessly integrated interactive interfaces with our internal systems. This integration enables us to identify the best and worst performing products efficiently.