Feature selection in automobile price prediction: An integrated approach

Abstract

Machine learning models for predictions enable researchers to make effective decisions based on historical data. Automobile price prediction studies have been a most interesting research area in machine learning nowadays. The independent variables to model the price and the price predictions are equally important for automobile consumers and manufacturers. Automobile consulting companies determine how prices vary in relation to the independent variables and they can then adjust the automobile's design, commercial strategy, and other factors to fulfill specified price targets. Furthermore, the model will assist management in comprehending a company's pricing patterns. The ability of machine learning systems to predict outcomes is entirely dependent on the effective selection of features. In this paper, we determine the influencing features on automobile price using an integrated approach of LASSO and stepwise selection regression algorithms. We use multiple linear regression to build the model using the selected features. From the experimental results using the automobile dataset from the UCI machine learning repository, the influencing features on automobile price are width, engine size, city mpg, stroke, make, aspiration, number of doors, body style, and drive wheels. Training data accuracy for predicting price was found to be 92%, and testing data accuracy was found to be 87%. The proposed approach supports selecting the most important characteristics of predicting the price of automobiles efficiently and effectively. This research will aid in the development of a model that uses the selected attributes to predict the price of automobiles using machine learning technologies.