This is an educational personal project for Web Scraping and Machine Learning. All data was scraped from the VivaReal website.
areas Total interior habitable area in m2;
bedrooms Total amount of bedrooms. When scraped data contained intervals (e.g. "2-4"), the mean was used, so this feature is of float type;
bathrooms Total amount of bathrooms. When scraped data contained intervals (e.g. "2-4"), the mean was used, so this feature is of float type;
parkingSpots Total amount of parking spots. When scraped data contained intervals (e.g. "2-4"), the mean was used, so this feature is of float type;
type housing type. Can only be "apartment" or "house";
neighborhood neighborhood in Salvador/Bahia in which the housing is located;
neighborhood_area_price value defined by calculating:
(sum of neighborhood's house prices) / (sum of neighborhood's house areas) if the type is "house" or
(sum of neighborhood's apartment prices) / (sum of neighborhood's apartment areas) if the type is "apartment";
prices housing price in BRL;
If you have make installed, you can use make <target> for interaction with the app.
make init creates the python virtual environment and installs dependencies;
make load_data runs the data Scraping/Cleaning pipeline;
make train_xgb trains and pickles a XGBoost model for the API;
make app runs the API locally and make app_dev runs it on development mode (auto reload).
The server will be running on http://127.0.0.1:8000/ and you can check the docs on http://127.0.0.1:8000/docs.
Alternatively, you can run the commands manually:
Set a Python Virtual Environment. You may use python3 -m venv .venv on your terminal inside the project folder or any other method you prefer.
With your venv created, activate it with source .venv/Scripts/activate on Windows or source .venv/bin/activate on Linux/Mac.
Install the dependencies with pip install -r requirements.txt.
To run the server locally, run uvicorn app:app on the terminal.
And voila, the server will be running on http://127.0.0.1:8000/ and you can check the docs on http://127.0.0.1:8000/docs.