Predicting Warehouse Location of Online Shopping Platforms with Machine Learning Algorithm – A Case Study

Online shopping has turned into the most convenient way to purchase products as it is more user friendly and the availability of the product is vast and also the range of products that the customers can select is abundant. Due to increasing customers in online shopping platforms, it is necessary for efficient warehouse location selection strategies to meet the increasing demand of the customers. By ensuring that all products are available and cutting down on delivery times, warehouse location optimization can improve customer satisfaction, which in turn boosts sales on e-commerce platforms. Using a dataset containing the required parameters, a Machine Learning algorithm can be deployed to optimize the warehouse location of virtual shopping platforms by analysing the total number of orders in each location. Various algorithms, such as KNN, SVM, and Random Forest algorithm, can be used to predict whether there is a need for a warehouse in a particular location. This study aims to determine which algorithm is best suited for this model.