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September 10, 2025Open Access

A Transparent House Price Prediction Framework Using Ensemble Learning, Genetic Algorithm-Based Tuning, and ANOVA-Based Feature Analysis

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Automated system predicts house prices using ensemble learning and ANOVA, suggesting robust feature importance insights.

The integrated approach achieved a prediction accuracy of R2 = 0.9973 with CBRGA.
Analysis of Variance (ANOVA) identified key features influencing house prices, enhancing model interpretability.
Genetic algorithms were utilized for feature optimization, improving performance over conventional methods.
10-fold cross-validation ensured robustness, alongside explainable AI methods like SHAP and LIME for transparency.

Hussain et al. (2025) studied this question.

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September 10, 2025Open Access

View Full Paper

Ask AI

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Authors

Automated system predicts house prices using ensemble learning and ANOVA, suggesting robust feature importance insights.

The integrated approach achieved a prediction accuracy of R2 = 0.9973 with CBRGA.
Analysis of Variance (ANOVA) identified key features influencing house prices, enhancing model interpretability.
Genetic algorithms were utilized for feature optimization, improving performance over conventional methods.
10-fold cross-validation ensured robustness, alongside explainable AI methods like SHAP and LIME for transparency.

Hussain et al. (2025) studied this question.

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