Predictive Analytics for House Pricing Using Python and Machine Learning

Authors

  • A Emmanuel Raju, Sukumar, Lapkashi, Deekshit Kumar

Keywords:

enhance, algorithms, providing

Abstract

Accurately predicting house prices is essential for real estate investors, buyers, and policymakers to make informed decisions. Traditional valuation methods rely on manual assessments and statistical models, which often fail to capture complex patterns in housing market trends. This study explores Predictive Analytics for House Pricing Using Python and Machine Learning, leveraging supervised learning algorithms to analyze key factors such as location, square footage, number of bedrooms, amenities, and market conditions. The proposed system implements regression models (Linear Regression, Decision Tree, Random Forest, and XGBoost) alongside feature engineering techniques to improve prediction accuracy. The dataset is preprocessed using data cleaning, normalization, and outlier detection, ensuring optimal model performance. Experimental results demonstrate that machine learning algorithms outperform traditional pricing models, providing faster, more accurate, and data-driven predictions. This research highlights the potential of AI-powered real estate valuation systems to enhance pricing accuracy, market analysis, and decision-making in the real estate sector.

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Published

02.10.2024

How to Cite

A Emmanuel Raju. (2024). Predictive Analytics for House Pricing Using Python and Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 2367–2374. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7343

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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