International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

House Price Prediction Using Linear Regression Model

Author(s) Jaykumar Parekh
Country India
Abstract Machine learning is a subset of Artificial Intelligence. Artificial intelligence (AI) and machine learning (ML) are key technologies in solving problems and addressing a wide range of issues in many different fields. Due to its capacity to automate processes, analyze massive volumes of data, and make precise judgments. Generally, it is used in voice assistants, recommendation systems, autonomous vehicles as well as in fraud detection. It also plays a vital role in the real estate sector, accurate house price prediction helps buyers, sellers, and investors make accurate decisions. There is a need for technology to predict housing values because they rise annually. Predicting house prices can assist developers in setting a property's selling price as well as buyers in scheduling the ideal time to buy a home. Four factors influence the price of a house which are area, bedrooms, bathrooms, and location. This study uses a methodology to forecast the price of houses based on relevant features, specifically by applying a linear regression model. Through the use of machine learning methods such as Random Forest, K-Means, Decision Tree, and Linear regression. This strategy will make it easier for people to invest money in a legacy without going via a broker. The study's findings indicate that the Linear regression yields the best accuracy.
Keywords linear regression, machine learning, Artificial intelligence
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-12-31
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.11519
Short DOI https://doi.org/gtbtb4
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com