HOUSE PRICE PREDICTION SURVEY USING MACHINE LEARNING

Abstract

It is very important for all the stake- holders in the real estate market, including buyers, sellers, policymakers, and financial institutions, to precisely predict house amount.

Predictive modeling has established as a useful tool to analyze huge patterns in housing data with the advancement of data science and ML. It explains the work of machine learning models to predict house price using a data set consisting of a no of influential factors like location, size of property, number of rooms, amenities, and proximity to the facilities. The holistic methodology that underlines the research process from preprocessing data and feature selection to implementing many ML models such as Linear Regression, Random Forest, Gradient Boosting, and Neural Networks-ensures this paper will provide a critical model to be used for a quick check of the predictor accuracy using metrics such as MAE,RMSE, and R² scores. It identifies key drivers of house prices and points out avenues where ML can be implemented in supporting decisions within the real estate market. This study fills gaps left open in previous work by the standard of the data, feature engineering, and model generalization. It presents strong bedrock for further work and suggests ensemble methods such as Gradient Boosting and Random Forest to be more precise and easier to interpret than simpler models. The research identifies potential to use machine learning in reinvigorating and refining real estate valuation techniques and indicates directions for adding further external factors, such as economic trend indicators or even urban planning data that may further improve prediction models. Results contribute to the rising data-driven real estate decision-making field.