Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques
xgb_model_30.pklis created specifically for the Streamlit application.
This model was trained using only 30 selected features to simplify user input and improve deployment usability.
The full-feature model (trained using all available features) is stored separately inside the models/xgb_model.pkl folder and is intended for experimentation, comparison, and advanced evaluation.
Same for **model_columns.pkl.
model/xgb_model_30.pkl→ Used for Streamlit App (UI-friendly version)models/xgb_model.pkl→ full-feature training models (research & development version)
⚡ Please ensure you load the correct model depending on your use case.
git clone https://github.com/pbhttai/House-Price-Predication.git
cd House-Price-Predication