Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The production of cars has been decreasing in most countries since the COVID-19 pandemic from 2020 to 2021.Due to this, the used car market has grown to be a booming industry by itself.Recent advances in online portals and platforms have made it possible to get more information about the factors that determine used car values.Hence, car price prediction has become a high-interest field of research.This paper aims to investigate the power of machine learning to build a model that will be able to predict the approximate price of a used car by utilizing the "Saudi Arabia Used Cars" Dataset which is collected from the Syarah platform and available on the Kaggle platform.The model assists both customer and seller to estimate the approximate price of a used car in the market.Three different Machine learning techniques were utilized which are Linear Regression, Random Forest, and XGBoost which score an MSE of 0.15, 0.10, and 0.19 respectively.The Random Forest Regressor algorithm outperformed other algorithms where it achieves the best result on the three evaluated metrics RMSE, MSE, and R-squared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it