• Prabhu Tejas
• Syed Shoaib

The real estate market has experienced notable price volatility, offering both potential gains and hazards for investors. The emergence of technological tools has enabled both individual and corporate investors to estimate property values with fewer data points than traditional methods require. In this study, a dataset consisting of the Kuala Lumpur Real Estate market was compiled, yet the absence of a sophisticated analytical framework for price evaluation can result in considerable financial setbacks, particularly for solo investors. This research delves into the potential of various machine learning algorithms to refine the process of property valuation when applied to actual market data.

Kuala Lumpur Market, Real Estate Price Prediction, Data preprocessing, Exploratory data analysis (EDA), Feature selection Methods, Correlation metrics, OLS, PLS, Ridge Regression, Decision Tree, Linear regression, Property Valuation Accuracy, Model Building, Price Prediction, Machine learning Algorithms

"Real Estate Prognostication: A Comparative Study of Regression based Machine learning Models for Real Estate Market Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.h289-h295, April-2024, Available :http://www.jetir.org/papers/JETIR2404735.pdf

"Real Estate Prognostication: A Comparative Study of Regression based Machine learning Models for Real Estate Market Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. pph289-h295, April-2024, Available at : http://www.jetir.org/papers/JETIR2404735.pdf