Authors: Associate Professor Dr. S. Thaiyalnayaki, Janga Kishore, Kareti Manoj, Jogu Ganesh, Kasaragadda Gopi Chand
Abstract: The rapid growth of urbanization in metropolitan cities has significantly influenced real estate markets and housing prices. Accurately estimating property values has become increasingly important for buyers, sellers, and real estate investors. This study presents a machine learning-based house price prediction system designed to analyze housing data and estimate property prices based on multiple influential factors. The dataset used in this research includes property attributes such as location, square footage, number of bedrooms, and number of bathrooms collected from metropolitan regions including Chennai and Bengaluru. The proposed system applies data preprocessing techniques to improve the quality of the dataset before model training. These preprocessing steps include handling missing values, encoding categorical variables, and performing feature scaling to ensure consistent data representation. After preprocessing, a predictive model based on Linear Regression is implemented to analyze the relationship
