Regression Modeling for House Price Prediction in Java Island
Bibliographic record
Abstract
Houses have become significant commodity for many parties. For developers and buyers, houses prices are crucial considerations for property investment. Typically, house price predictions are made by experts. Automating predictions would ease various parties, including the government, in controlling price stability. House price predictions are extensively researched in various countries Like in China, Australia, Taiwan, Pakistan, India, Canada, Indonesia, and others. Generally, features for price prediction revolve around the size and furniture of the house without considering its location. House price in larger cities tend to be more expensive. Given this situation, this research focuses on the impact of city and province attribute on house price prediction. This research proposes to build a predictive model based on home size, furniture, and location. The model is built using several machine learning algorithms for regression measured by Mean Squared Error (MSE) metrics. Testing results show that predictions tend to improve when using location attributes for almost all algorithms. Additionally, simulations are conducted using other feature engineering and categorical encoding, increasing the MSE value of Random Forest up to $\mathbf{0. 0 0 0 6 8 1}$.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".