Aruodas.lt Apartment Price Predictor API

A Flask API able to predict the price of an apartment from provided attributes. The model was trained on apartment data from Aruodas.lt with the help of the scrape_aruodas package.

Aruodas.lt Apartment Price Predictor API currently supports:

• POST requests
• Single or multiple apartment predictions with a single request
• Storing the data in a remote Heroku database
• Invalid input handling

Upcomming Features!

• Visual user interface

Tech

Aruodas.lt Apartment Price Predictor API uses a number of open source projects to work properly:

• Python - The programming language for of this project
• Scrape_Aruodas - Scrape_Aruodas is a web scraper designed to scrape Aruodas.lt apartment listings
• Flask - Flask is a lightweight WSGI web application framework
• Scikit-learn - Scikit-learn is a free software machine learning library for the Python programming language
• Pickle - Python object serialization
• JSON - JSON encoder and decoder
• NumPy - The fundamental package for scientific computing with Python

Installation

• Aruodas.lt Apartment Price Predictor API requires at least Python version 3.7

Installation as a standalone python project:

1. Clone the repository to a local directory

2. Create a virtual environment

   $ python -m venv venv

3. Activate the virtual environment

   $ venv\Scripts\activate.bat

4. Install required libraries

   $ pip install git+https://github.com/valdas-v1/scrape_aruodas
   $ pip install -r requirements.txt

5. Update database connection credentials in database.py

   self.engine = create_engine(YOUR_DATABASE_URI)

Running the API

Run the API as any other Python file

$ python app.py

Using the API

To use the API, make a POST request to the route of /predict with the sample JSON structure of

{
    "inputs": [
        {
            "Area": 100,
            "Build year": 1985,
            "Building type": "Brick",
            "Equipment": "Fully equipped",
            "Floor": 1,
            "Heating system": "Central thermostat",
            "No. of floors": 9,
            "Number of rooms": 3,
            "Renovation year": 2010,
            "city": "Vilnius",
            "region": "Šnipiškės",
            "street": "Rinktinės g."
        }
    ]
}

Body of expected response

{"predicted_prices": [225679.79271469155]}

License

MIT