An international e-commerce company based wants to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers. The company sells electronic products. (https://www.kaggle.com/datasets/nayanack/shipping/data)

The data analysis gives us an idea about the distribution of late arrivals and their relation to business characteristics, such as the mode of shipment, warehouse block, cost of the product, weight of the product, prior purchases by respective customers and customer care calls related to the shipment.

As a result of a Gridsearch we found a tuned algorithm that best classifies late arrivals which can be used for prediction and prevention.