Demand Prediction of Newly Launched Product
To build a Machine learning model that is able to predict the demand of a newly launched product with features not currently present in any other currently available product. Since new model had features never seen before the dataset set created comprised of products which nearly resembled the product/feature. K-means and K-modes clustering was applied in a pipeline in order to get the cluster from the dataset that matched most with the product. The prices of these products over the last 5 years was then passed to a Bass-Diffusion Model for predicting the demand.