Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 9, 2025ITM Web of ConferencesOpen Access

A Robust Ensemble-Based Framework for House Price Estimation: Integrating XG-Boost with SHAP and Web Deployment

View Full Paper
Ask AI
Bookmark
Share

Authors

EAEzil Sam Leni ATRT RevathiSDSridhar Devarajan

Discussion

Loading...

Member takes

Overview

Framework improves house price estimation in real estate stakeholders, suggesting enhanced accuracy and usability.

Key Points

  • The ensemble-based framework increases house price prediction accuracy, indicating an R-squared value of 0.87.
  • Integration of XGBoost and SHAP enhances model interpretability and trust, supporting users in the real estate market.
  • The study utilizes over 21,000 records from the King County housing dataset, emphasizing robust data preprocessing techniques.
  • Interactive web deployment with Streamlit allows real-time predictions, making the system user-friendly and actionable.

Cite This Study

A et al. (2025) studied this question.

synapsesocial.com/papers/68e7d631bd66d359be626703https://doi.org/10.1051/itmconf/20257901023
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Robust Ensemble-Based Framework for House Price Estimation: Integrating XG-Boost with SHAP and Web Deployment2025
  2. 2House price prediction using machine learning2024 · 1 citations
  3. 3Comparative Analysis of Machine Learning Models for House Price Prediction: From Linear Regression to Boosted Trees2025 · 1 citations
  4. 4A Transparent House Price Prediction Framework Using Ensemble Learning, Genetic Algorithm-Based Tuning, and ANOVA-Based Feature Analysis2025 · 15 citations
  5. 5House Price Prediction App2025