Prediction Demand for Classified Ads Using Machine Learning: an Experiment Study

Classified ads prediction is a very interesting activity for organizations in order to increase the purchase quantity of a product and thereafter the possibility of sale. Used goods predicting can be done by calculating the probability of sale for each selected product. In this paper, we conduct an empirical analysis on classified ads prediction of Avito dataset in order to develop prediction models using three individual machine-learning techniques and five ensemble learners. We compare and evaluate the performance of the proposed models using Root Mean Square Error (RMSE) measure. The stacked generalization method was also used to combine the best-performed models to select the best one. The results show that the Extreme Gradient Boosting Machine algorithm (XGBoost) is the most accurate model with an RMSE value of 0.2253.