Abstract
Nowadays, due to increasing competition and to have an advantage over their competitors, companies are increasingly opting for solutions based on artificial intelligence techniques for classification, forecasting, decision support, or prediction. Estimating the price of a product in a given context is one of the challenged tasks for researchers. In this paper, we present a state of the art of solutions based on supervised learning algorithms for the task of predicting the price of used cars. Then we propose a prediction system for second-hand cars in the Moroccan context. For this purpose, we collected data using a web crawler, and then applied feature selection, listwise deletion and other pre-processing techniques. As a result, XGBoost gives the best performance with an R2 of 90.7%. The model was deployed using a flask application making it easier to use for common users.
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Benabbou, F., Sael, N., Herchy, I. (2022). Machine Learning for Used Cars Price Prediction: Moroccan Use Case. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_25
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