House Market Prediction Using Machine Learning
This study compares tree-based machine learning algorithms for predicting Bucharest residential apartment prices. Using a dataset from March 2025, comprehensive preprocessing—including imputation, categorical encoding, and feature engineering (e.g., distance to public transport)—was applied. Models...
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| Format: | Article |
| Language: | English |
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Bucharest University of Economic Studies
2025-01-01
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| Series: | Database Systems Journal |
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| Online Access: | https://www.dbjournal.ro/archive/36/36_6.pdf |
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| _version_ | 1850114245738364928 |
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| author | Nicușor-Andrei ANDREI |
| author_facet | Nicușor-Andrei ANDREI |
| author_sort | Nicușor-Andrei ANDREI |
| collection | DOAJ |
| description | This study compares tree-based machine learning algorithms for predicting Bucharest residential apartment prices. Using a dataset from March 2025, comprehensive preprocessing—including imputation, categorical encoding, and feature engineering (e.g., distance to public transport)—was applied. Models were optimized via grid search with 5-fold cross-validation and evaluated using RMSE, MAE, and R². Results show XGBoost outperforms Random Forest and Decision Tree models across all metrics. |
| format | Article |
| id | doaj-art-0fa20893a91f4c609a74e0bcafbdebad |
| institution | OA Journals |
| issn | 2069-3230 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Bucharest University of Economic Studies |
| record_format | Article |
| series | Database Systems Journal |
| spelling | doaj-art-0fa20893a91f4c609a74e0bcafbdebad2025-08-20T02:36:57ZengBucharest University of Economic StudiesDatabase Systems Journal2069-32302025-01-011615564RePEc:aes:dbjour:v:16:y:2025:i:1:p:55-64House Market Prediction Using Machine LearningNicușor-Andrei ANDREI0The Bucharest University of Economic Studies, RomaniaThis study compares tree-based machine learning algorithms for predicting Bucharest residential apartment prices. Using a dataset from March 2025, comprehensive preprocessing—including imputation, categorical encoding, and feature engineering (e.g., distance to public transport)—was applied. Models were optimized via grid search with 5-fold cross-validation and evaluated using RMSE, MAE, and R². Results show XGBoost outperforms Random Forest and Decision Tree models across all metrics.https://www.dbjournal.ro/archive/36/36_6.pdfhouse marketmachine learningprice predictiontree-based algorithmsxgboost |
| spellingShingle | Nicușor-Andrei ANDREI House Market Prediction Using Machine Learning Database Systems Journal house market machine learning price prediction tree-based algorithms xgboost |
| title | House Market Prediction Using Machine Learning |
| title_full | House Market Prediction Using Machine Learning |
| title_fullStr | House Market Prediction Using Machine Learning |
| title_full_unstemmed | House Market Prediction Using Machine Learning |
| title_short | House Market Prediction Using Machine Learning |
| title_sort | house market prediction using machine learning |
| topic | house market machine learning price prediction tree-based algorithms xgboost |
| url | https://www.dbjournal.ro/archive/36/36_6.pdf |
| work_keys_str_mv | AT nicusorandreiandrei housemarketpredictionusingmachinelearning |