A comparative study to forecast vehicle prices in automotive industry using machine learning techniques

Precise estimations in marketing is important in terms of getting results with higher financial returns and making more accurate strategic decisions. Estimations made only by expert opinion can be incorrect or insufficient and cause great financial damage to companies. In this study, a solution to this problem is presented to forecast vehicle prices in the automotive industry by using popular machine learning techniques. In recent years, machine learning techniques have been used in the literature for price estimation of computer, electronic products and online product sales, mainly fashion products, retail/market products. In this study, performances of Decision Tree, Random Forest, Support Vector, and Artificial Neural Networks regression techniques on an automotive sales dataset are evaluated and compared. In experiments, a sales dataset of 6019 samples with 13 features (serial number, new price, name, location, year, mileage driven, fuel type, transmission, owner type, mileage, engine, power, seats, price) was used, and a three-stage pre-processing was applied. In the last stage of this pre-processing, categorical values were converted into numerical data by Label, One Hot, Binary and Frequency coding techniques. In all analyses, K-Fold Cross Validation method was used in the estimation of price prediction error. As a result of the experiments, the best coding and the best estimation method on this data set were revealed comparatively. The results have presented some interesting points which makes this study a potential choice for relevant applications.