Predicting Used-Vehicle Resale Value in Developing Markets: Application of Machine Learning Models to the Kazakhstan Car Market
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".