• Shivani Shukla

Abstract— Housing price prediction plays a pivotal role in real estate markets, aiding stakeholders in strategic decision-making processes. This study employs multiple linear regression (MLR) as a predictive modeling technique to forecast housing prices. Leveraging a comprehensive dataset encompassing various attributes such as location, size, amenities, and market trends, the research aims to develop an accurate and reliable predictive model. The methodology involves rigorous data preprocessing to address missing values, outliers, and inconsistencies, followed by feature engineering to extract meaningful insights. Through exploratory data analysis, significant relationships between predictor variables and housing prices are identified, guiding feature selection for the MLR model. Model performance is evaluated using metrics like Mean Squared Error (MSE), R-squared, and Root Mean Squared Error (RMSE) to ensure robustness and generalizability. Furthermore, the study examines the impact of individual predictors on housing prices by analyzing the coefficients of the MLR model. The findings demonstrate the efficacy of the MLR approach in accurately predicting housing prices and provide valuable insights into the factors influencing real estate market dynamics.

Housing Price Prediction, Multiple Linear Regression, Real Estate, Predictive Modeling, Data Analysis

"Predicting Housing Prices Using Multiple Linear Regression: A Comprehensive Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.f746-f751, March-2024, Available :http://www.jetir.org/papers/JETIR2403593.pdf

"Predicting Housing Prices Using Multiple Linear Regression: A Comprehensive Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppf746-f751, March-2024, Available at : http://www.jetir.org/papers/JETIR2403593.pdf