Abstract: A car price prediction has been a high-interest research area, as itrequires noticeable effort and knowledge of the field expert. Considerable numbers of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learningtechniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application.
Keywords:Machine Learning, SVM Algorithm,Linear RegressionAlgorithm , Random forest regression,Decision Tree Regression, Jupiter, pycharm .
| DOI: 10.17148/IJARCCE.2022.111105