• Vijay Pawar H G
• Sumit Prakash Dubey

This study explores feature selection and regression modeling strategies for predicting median house values using a housing dataset. Recursive Feature Elimination (RFE) with Ridge Regression is employed to identify the most influential features. Performance evaluation metrics such as Mean Squared Error (MSE) and R-squared (R2) are utilized to assess model accuracy. Furthermore, the correlation between property tax burden and house value is examined. The findings offer valuable insights into housing market dynamics and provide practical implications for real estate stakeholders and researchers aiming to enhance prediction accuracy and understand key factors influencing house prices.

Feature selection Regression modeling Recursive Feature Elimination (RFE) Ridge Regression Property tax impact Housing prices Predictive modeling Mean Squared Error (MSE) R-squared (R2) Exploratory data analysis Correlation analysis Multicollinearity Population and sample Boston Housing Dataset Data preprocessing Linear regression Feature engineering Model evaluation Dimensionality reduction Model performance.

"Exploring Feature Selection Techniques and Property Tax Impact on Housing Prices A Case Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e616-e620, February-2024, Available :http://www.jetir.org/papers/JETIR2402485.pdf

"Exploring Feature Selection Techniques and Property Tax Impact on Housing Prices A Case Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe616-e620, February-2024, Available at : http://www.jetir.org/papers/JETIR2402485.pdf