House Price Prediction Using Machine Learning
Ieee account.
- Change Username/Password
- Update Address
Purchase Details
- Payment Options
- Order History
- View Purchased Documents
Profile Information
- Communications Preferences
- Profession and Education
- Technical Interests
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
IMAGES
VIDEO
COMMENTS
House Price Index (HPI) is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from HPI to predict individual housing price.
According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. To analyze the key elements and the best predictive models for home...
The main objective of this research paper is the estimation of the market worth of a land, house, property which will help customers to buy and sell property without moving to a specialist.
Our research paper [1] will helps you to predict the price of the house to a good accuracy. The main motive of our research paper is to predict the price [2] of the house by analyzing the customer needs and their financial income.
This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city.
In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. We will implement a linear regression algorithm on our dataset.
In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data ...
As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society.
The paper presents the new technology of 3D printing of buildings for the sustainable houses of the future. 3D printing building technology is a new construction technique started with the invention of 3D printer.
In this research, the main goal is to identify and analyse the most important factors that determine housing prices in Dortmund, a former industrial city in the Western part of Germany. To this end, we compare two different statistical methods to determine a stable method with valid results.