Real Estate Price Prediction Using ML: a Survey-Based Study

EasyChair Preprint 12541

Abstract

This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse of the correlation between variables to determine the most important factors that affect real estate prices and house prices. There are two datasets used in this study which called public and local which contain real estate prices . The accuracy of the prediction is evaluated by checking the root square and root mean square error scores of the training model. The test is performed after applying the required pre-processing methods and splitting the data into two parts. However, one part will be used in the training and the other in the test phase. We have also presented a binning strategy that improved the accuracy ofthe models

Keyphrases: Artificial Neural Network, Lasso Regression, Multiple Linear Regression, Real Estate Price Prediction, Ridge Regression, machine learning, random forest regression

@booklet{EasyChair:12541,
  author    = {Yash Chothani and Jay Lathiya and Param Jadav and Darshak Dhangan},
  title     = {Real Estate Price Prediction Using ML: a Survey-Based Study},
  howpublished = {EasyChair Preprint 12541},
  year      = {EasyChair, 2024}}