| Popis: |
Cars’ auto auctions generally face a specific regression in the profits due to the unfortunate decision-making of auctioning cars without any prior research. This paper examines the possibility of increasing the profits of a particular cars’ auto auction located in New England, Connecticut. Based on the collected data from the auction this study aims to predict the selling probability of cars and their selling prices; thus, increase its profits through the application of data mining techniques and utilization of multiple predictive models. Three classifiers were applied to predict the selling probability of cars; logistic regression, decision tree, and neural network which were later incorporated to produce three scenarios that would increase the accuracy of predictions and consequently ensure their effectiveness, whereas the multiple linear regression was administrated to predict the cars' selling prices. Subsequently, an agent-based model was built to produce a simulation that adequately represents the results of this research in reality and create a model ready to operate under any suitable data, which would serve many auto auctions besides the one upon which this study is based. By comparing the baseline and predicted profit, this study concludes the effectiveness of its methods in raising the profit of the auto auction where the highest increment by the models is 19.49%. This study has several recommendations for future examination. Further research could be conducted to identify interested buyers, their exact percentage and based on determining the buying trends of the interested buyers if a car is predicted as sold in the model but was not, further studies could investigate the expected time of its selling or if it will be sold at all. |
