Developing A Novel Approach to Forecast the Market Valuation of Newly Manufactured Cars by Leveraging A Comprehensive Array of Vehicular Attributes

Abstract

Company declares price for new manufactured car based on feature of car and brand name of company. This neural network model can helpful to manufacturer to estimate price of car based on features of car and its own brand name. This model is also useful for buyer to verify price of various new launched car in market. Numerous techniques, including time series analysis, technical analysis, fundamental analysis, statistical analysis, and others, are employed in an effort to forecast the value of newly introduced cars on the market, but none of these techniques has been shown to be a reliable source of information. A popular method of identifying an accurate automotive value based on historical data is to use Artificial Neural Networks (ANNs), a branch of Artificial Intelligence (AI). ANNs consist of two modules: a training session and price prediction based on previously taught data. For the training session, we employed the back propagation technique, and the network model for price prediction was the multilayer feed forward network.