Machine Learning

House Price Prediction

This regression project predicts house prices using multiple features including location, size, number of bedrooms, bathrooms, and amenities. The ensemble approach combining XGBoost and Random Forest achieves an R² score of 0.89.

Model Accuracy

89%

Model Type

XGBoost & Random Forest

Category

Machine Learning

Tech Stack

5 Tools

Key Features

  • Feature engineering from property data
  • Ensemble methods (XGBoost + Random Forest)
  • Hyperparameter optimization with GridSearchCV
  • Cross-validation for model robustness
  • Price range predictions with confidence intervals

Technologies

PythonXGBoostScikit-LearnPandasSeaborn

Performance Metrics

R² Score

89

RMSE

45000

MAE

35000

MAPE

12

Feature Importance

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