Abstract:
The burgeoning trade in used vehicles has necessitated further research into price
prediction. In developing nations, the abundance of second-hand cars and limited supply of
new ones has led to a preference for used vehicles. Consequently, the analysis of vendor
data becomes imperative for gaining valuable insights. Sellers are increasingly seeking
accurate price predictions to maximize their profits. The assessment of used car prices
necessitates a thorough understanding of the features that influence value. Although the
inclusion of multiple features can enhance prediction accuracy, the list of these features is
non-exhaustive. This study seeks to examine the effectiveness of various regression
techniques such as Linear, Decision Tree, SVM machines, Neural Network, and Bagged
Trees, alongside machine learning algorithms, in predicting the selling price of used cars
based on the associated features. Evaluation metrics will be utilized to identify the most
proficient model by examining the performance and error rate of each model. The deep
neural network model demonstrates exceptional performance, as indicated by its low RMSE
and MSE values, suggesting high efficiency. Some models, including cubic SVM, fine
Gaussian SVM, and wide neural network, exhibit a robust correlation (R) in accurately
connecting input and output variables. Furthermore, narrow, medium, bilayered, and
trilayered neural networks display commendable performance in recording variable
correlations. After comparing various models, Bagged Trees were identified as the most
cost-effective option per square meter, due to their advantageous pricing and performance
