🏡 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 competition.

The model is published on the Hugging Face Hub: 👉 https://huggingface.co/DanteChapterMaster/house-price-predictor

📦 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

📊 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

🤖 Model Card

Type: Regressor
Algorithm: XGBoost in Scikit-learn Pipeline
Target: SalePrice (log-transformed)
Evaluation: Root Mean Squared Error (RMSE)