House Price Prediction Using Machine Learning

Abstract

Prices change often in the real estate market, which makes it a dynamic business. It's one of the most important ways to use machine learning to predict the prices of real estate based on the present situation with the most accuracy. The main goal of the study paper is to predict the real prices of places and houses by using the right machine learning (ML) algorithms. The suggested article looks at some important factors and parameters for figuring out how much a property is worth. To figure out how much a house will cost, you will also need to use more regional and statistical methods. After using some machine learning methods and algorithms, the paper explains how the house pricing model works. Using the dataset from a reputable website in the proposed system makes it possible to get a thorough analysis of the data points. To improve the accuracy, algorithms like Linear regression and sklearn are used. During model building, almost all data similarities and cleaning, outlier removal and feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tuning, k fold cross-validation, etc. are covered.