House Prices - Advanced Regression Techniques

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.

Kaggle Competition Link :

https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

⚠️ Important Note About Models

xgb_model_30.pkl is 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.

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📌 Model Usage Clarification

• 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.

Clone or Download the Project

git clone https://github.com/pbhttai/House-Price-Predication.git
cd House-Price-Predication