Walking past a construction site, Caterpillar's signature bright yellow machinery is one of the first things you'll notice. Caterpillar sells an enormous variety of larger-than-life construction and mining equipment to companies across the globe. Each machine relies on a complex set of tubes to keep the forklift lifting, the loader loading, and the bulldozer from dozing off.
Like snowflakes, it's difficult to find two tubes in Caterpillar's diverse catalogue of machinery that are exactly alike. Tubes can vary across a number of dimensions, including base materials, number of bends, bend radius, bolt patterns, and end types.
Currently, Caterpillar relies on a variety of suppliers to manufacture these tube assemblies, each having their own unique pricing model. This competition provides detailed tube, component, and annual volume datasets, and challenges you to predict the price a supplier will quote for a given tube assembly.
This project has the purpose to predict how a supplier will quote a price on a given tube assembly. Knowing this information in advance we can determine the optimal quote which will minimize the expenses of the company.
The overall goal of this project is to predict quotation price given by a supplier with at least 95% of accuracy. The input given for this project is a dataset containing information about tube assemblies, price quotations and a set of components.