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license: mit
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# 🏡 House Price Predictor (Kaggle + Hugging Face)
This project is a complete machine learning pipeline for predicting house prices in Ames, Iowa, using structured data and transformer-based text embeddings. It was developed as part of the [Kaggle House Prices - Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) competition.
The model is published on the Hugging Face Hub:
👉 https://huggingface.co/DanteChapterMaster/house-price-predictor
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## 📦 Project Highlights
- ✅ Exploratory Data Analysis (EDA)
- ✅ Feature Engineering from domain knowledge
- ✅ Model training: Ridge, Lasso, Random Forest, XGBoost, and Stacking
- ✅ NLP augmentation: BERT embeddings from generated property descriptions
- ✅ Full model pipeline with preprocessing (ColumnTransformer)
- ✅ Deployment-ready model saved with `joblib`
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## 📊 Features
**Numerical Features:**
- `GrLivArea`, `TotalBsmtSF`, `GarageCars`, etc.
**Categorical Features:**
- `Neighborhood`, `HouseStyle`, etc. (one-hot encoded)
**Generated Features:**
- Log-transformed target
- Interaction terms
- Transformer-based embeddings from property descriptions
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## 🤖 Model Card
- **Type:** Regressor
- **Algorithm:** XGBoost in Scikit-learn `Pipeline`
- **Target:** `SalePrice` (log-transformed)
- **Evaluation:** Root Mean Squared Error (RMSE) |