Factors influencing housing prices: A comparative study using multiple linear regression and random forest
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 method is to construct a multiple linear regression model to examine the variables that significantly affect US home prices. The dataset has 13 variables and 545 observations, and it was obtained via Kaggle. This paper also considers comparing the multiple linear regression models and random forest model for predicting house prices and thus concludes which model can most accurately predict house prices. The result indicates that the factors including area, quantity of bedrooms and bathrooms, the existence of a basement, and state of the furnishings, etc. significantly impact housing prices. By comparing Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), the conclusion suggests that the multiple linear regression model marginally surpasses the random forest regarding the accuracy of the predictions, despite the fact that both models function similarly. This research offers practical applications for enhancing real estate market strategies and provides valuable insights into the factors influencing housing prices.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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