Paper Title
HOUSING PROGNOSIS USING MACHINE LEARNING
Article Identifiers
Keywords
Ensemble Algorithms, Gradient Boosting Regressor, XG Boost Regressor, Random forest.
Abstract
Accurately predicting housing prices is integral to real estate investment and decision-making processes. This paper conducts a thorough investigation into the application of machine learning models for precise house price estimation. Our primary aim is to develop robust predictive models beneficial to industry experts and individuals seeking property valuation. The research initiates by assembling a well-structured dataset, incorporating diverse features such as property characteristics, location attributes, and economic indicators. Leveraging this dataset, a comprehensive exploration of machine learning algorithms, spanning regression techniques, ensemble methods, and deep learning models, is conducted. Each model undergoes rigorous pre processing, feature engineering, and hyperparameter tuning to optimize its predictive performance. Our evaluation encompasses an array of comprehensive metrics, including mean squared error, root mean squared error, mean absolute error, and R-squared, to assess the models' predictive capabilities. The outcomes underscore the effectiveness of the proposed models in capturing intricate data relationships and delivering highly accurate price estimations.
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How To Cite (APA)
KASIBHOTLA SAI NEERAJ KUMAR, BOPANNA ADITYA, & DAVU RAJA LAXMINARAYANA (January-2024). HOUSING PROGNOSIS USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(1), c775-c780. https://ijnrd.org/papers/IJNRD2401299.pdf
Issue
Volume 9 Issue 1, January-2024
Pages : c775-c780
Other Publication Details
Paper Reg. ID: IJNRD_211595
Published Paper Id: IJNRD2401299
Downloads: 000122255
Research Area: Engineering
Author Type: Indian Author
Country: KALLURU, TELANGANA, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2401299.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2401299
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