A machine learning project to predict housing prices using the Ames
Housing dataset.
Built with Python, scikit-learn, XGBoost, and Streamlit
for an interactive web UI.
This project demonstrates a full ML workflow:
- Data preprocessing (cleaning, feature engineering, log-transforms)
- Exploratory Data Analysis (EDA) with compact visualizations
- Feature selection (handling multicollinearity, ranking important features)
- Model training & evaluation:
- Linear Regression
- Random Forest
- XGBoost
- Streamlit app for interactive predictions:
- Train models directly in the app
- Predict sale price from top-K features
- Compact UI with sliders, toggles, and number inputs
- Shows expected error range (±MAE)
hause-price-regression/
│
├── data/ # raw and processed datasets
├── artifacts/ # trained models (ignored in git)
├── scripts/ # training, prediction, transform logic
├── app.py # Streamlit app entry point
├── train.py # model training script
├── transform.py # preprocessing and feature engineering
├── requirements.txt # dependencies
└── README.md # project description
-
Clone the repo:
git clone https://github.com/jaronimas-codes/hause-price-regression.git cd hause-price-regression -
Create a virtual environment:
python -m venv venv source venv/bin/activate # on Linux/Mac venv\Scripts\activate # on Windows
-
Install dependencies:
pip install -r requirements.txt
### Launch Streamlit app
```bash
streamlit run app.py
Then open http://localhost:8501 in your browser and follow the workflow.
- 🎯 Top-K feature editing: adjust the most important features\
- 🧮 Prefilled defaults: dataset medians (binary → checkbox, int → number input, float → number input)\
- 📈 Prediction with uncertainty: shows predicted price and ±MAE range\
- 🖥️ Compact UI: 4-column layout for efficient editing
- Python 3.11
- Pandas, NumPy
- scikit-learn
- XGBoost
- Streamlit
- Matplotlib