🏙️ Toronto Condo Affordability Predictor
A spatio-temporal machine learning system that predicts neighborhood-level condo prices and helps users plan down savings and rent affordability
Housing affordability is a major challenge for students and young adults in Toronto.
This project uses open municipal and federal datasets to:
- Predict condo rent prices across Toronto neighborhoods using machine learning (LightGBM/XGBoost).
- Estimate affordability timelines for users based on their income and other metrics.
- Visualize attainable neighborhoods through an interactive web application.
- 🗺️ Spatio-temporal price forecasting
- 💰 Personalized affordability
- 📊 Interactive map with attainable neighborhoods
- 📦 Fully open-data pipeline using CMHC, and Census data
- ⚙️ Quantile regression for uncertainty estimates
| Component | Technology |
|---|---|
| Backend / ML | Python, scikit-learn, XGBoost |
| Database | DuckBD, dvc |
| Frontend | Streamlit |
| API Layer | FastAPI |
| Visualization | Plotly, Folium |
| Version Control | Git + GitHub |
git clone https://github.com/Western-Artificial-Intelligence/condo-cost-predictor.git
cd condo-cost-predictor
STRCUTURE ONCE WE HAVE ALL SET UP
condo-cost-predictor/
├── data/ # Open datasets (CMHC, StatCan, City of Toronto)
├── notebooks/ # Exploratory and model development notebooks
├── src/
│ ├── data_pipeline/ # ETL scripts for data cleaning and joins
│ ├── models/ # Training, validation, and evaluation scripts
│ ├── api/ # FastAPI endpoints
│ └── app/ # Streamlit front-end
├── requirements.txt
├── README.md
└── LICENSE
MAKE SURE YOU DO THIS
- START AN ENV
python3 -m venv venv
source venv/bin/activate # macOS/Linux
venv\Scripts\activate # Windows
- DOWNLOAD DEPENDECIES
pip install -r requirements.txt
-after when we have it running we will use
streamlit run app.py