Research on the Relationship between Influencing Factors and House Prices Changes
Bibliographic record
Abstract
In recent years, the change in house prices has also become a main factor affecting economic changes. There are lot of research shows that the relationship between economics and house prices. This study focuses on how house prices change and what factors can lead to the change in house prices. The data from the Kaggle website, containing property prices in Delhi. By using the linear regression model, calculate 12 factors which are Price, Area, Bedrooms, Bathrooms, stories, main road, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea, and furnishing status to study the degree and trend of impact on house prices changes. The results show that area, bathrooms, stories, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea will have a significant positive impact on price, and furnishing status will have a significant negative impact on price. Bedrooms have no impact on price. This study opens a new perspective and giving society a new thinking angle for the study of housing prices changes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".