bs3537/data-science

AirBnB Optimal Price Team 1

An API to predict daily AirBnB rates in Tokyo from property features.

Getting Started

This section is dedicated to software developers that want to contribute to this API. You must have pipenv installed on your local machine to continue.

Pipenv: Python Development Workflow for Humans

Environment Variables

If you have been provided rights, simply clone this repo. If not, you may fork and clone the repo:

git clone <url>

Once cloned, create a .env file in the main directory and add the following variables:

FLASK_APP=airbnb:APP 
FLASK_ENV=development

Then you can use the following commands to create a local pipenv environment and install all required dependencies:

pipenv shell
pipenv install

Start the API

Finally, you can run the following command to start the API:

flask run

Endpoints

POST /predict

Accepts the following JSON object parameters:

{
    beds: int
    baths: int
    season: int
    has_wifi: int
    allows_pets: int
    predicted_price: int 
}

Currently returns the same object. Working on predictive functionality.