Prediction of Electric Vehicle Price in India using Simple Linear Regression
Bibliographic record
Abstract
The demand for electric vehicles (EVs) has been steadily increasing in recent years, led by the factors like environmental concerns, government incentives, and improvements in EV technology. The EV’s growth is expected to increase in the coming years as EVs become more affordable and more models become available on the market. Predicting the price of electric vehicles provides valuable insights on the EV market and inform a range of business, consumer, financial, and policy decisions. Predicting the price of electric vehicles using simple linear regression involves building a linear regression model with a single independent variable usually the vehicle’s characteristics or features to predict the dependent variable the price.This work has predicted the price of Electric Vehicle using a data set prepared for the Indian context. It has been predicted that there is significant correlation between battery capacity in Ah and the vehicle price. The measured RMSE value is 26274.942642891292. The measured value indicates that the model is better at predicting the price of an electrical vehicle.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".