Home > Engineering > Computer Engineering > Special Issue > Smart Innovations in Computer Science and Applications > House Price Prediction using Artificial Intelligence and Machine Learning

House Price Prediction using Artificial Intelligence and Machine Learning

Call for Papers

Volume-10 | Issue-5

Last date : 27-Oct-2026

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


House Price Prediction using Artificial Intelligence and Machine Learning


Shreyash S. Donarkar



Shreyash S. Donarkar "House Price Prediction using Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.642-648, URL: https://www.ijtsrd.com/papers/ijtsrd101648.pdf

House prices need careful guessing because choices here carry big money risks. Old ways of judging value usually depend on people looking closely, using their experience - this can bring bias or mixed results. As AI and ML grew stronger, number-based techniques started offering sharper estimates, changing how homes are priced. Looking at how different machine learning methods predict home values, this work uses organized real estate information. Features like size of the land, count of rooms, age of construction, space for vehicles, general condition ratings, and neighborhood details make up the data set. Cleaning steps - fixing gaps in records, turning categories into numbers, adjusting scale differences, eliminating odd entries - helped sharpen predictions. Instead of just one approach, four were tested: straight-line fitting, tree-style splitting, forest-based averaging, then boosting-driven refinement. Each was judged by average mistake size, error spread, plus explained variance - not magic, just math tracking accuracy. When tested, ensemble techniques did better than standard regression. Random Forest stood out by predicting most accurately. These outcomes show artificial intelligence models boost how well property values are estimated. Efficiency gets a clear lift from using such systems.

House Price Prediction, Artificial Intelligence, Machine Learning, Regression Models, Random Forest, Real Estate Analytics.


IJTSRD101648
Special Issue | Smart Innovations in Computer Science and Applications, March 2026
642-648
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin