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.
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