International Journal For Multidisciplinary Research

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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
Cite This House Price Prediction Using Linear Regression Model - Jaykumar Parekh - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.11519
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.11519
Short DOI https://doi.org/gtbtb4
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