Predicting Automobile Prices Using Neural Networks

The chief marketing officer (CMO) at an automobile agency was looking at a list of car model features, which he had received from the manufacturing plant. He was expected to provide the manufacturer's suggested retail prices of the cars to dealers the following week and had to decide on the base prices. The CMO asked a data scientist at the research lab to predict prices using the data of past car models. Each car model had different features that could affect the price. The data scientist decided to use feed-forward neural networks as a tool for predicting the prices of new models. After comparing different prediction models, he also wanted to determine which prediction model was suitable for car manufacturing plants.

This exercise is suitable for undergraduate- and graduate-level courses on management science. Students should have previous knowledge of basic statistical distributions, Microsoft Excel formulas, and R programming language. After working through the exercise and assignment questions, students will be able to do the following: ·Understand neural networks and their applications. ·Use R programming language. ·Compare and contrast the advantages and disadvantages of different prediction models and determine which is most suitable for car pricing.

car manufacturing; pricing; machine learning; regression; neural networks

Undergraduate/MBA

Manufacturing

Année de la problématique : 2018

Livraison : Livraison par lien de téléchargement

Nombre de pages : 3