AirBnB Price Prediction

This is a complete production project which makes a prediction based in airbnb_listings dataset.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Folder structure

• airbnb
  • api.py: Flask API application
  • request.py: The request for Flask API
  • build_model.py: iinitiates a new model, trains the model, and pickle
  • model
    • XGBRegressor.pkl
  • requirements.txt: list of packages that the app will import
  • data: directory that contains the data file
    • listings_reduced.csv
  • template: the initial page where the new data to be predicted will be added
    • index.html

Prerequisites

What things you need to install the software and how to install them

requirements.txt

This is a text file that holds all the library versioning requirements. It consists of all the external (non pre-installed Python libraries) libraries used to execute the code within the pipeline.

Installing

# Install libraries in requirements.txt
pip install -r requirements.txt

# If you want to run, build and create the pickle file again just:
python build_model.py
If not, it's ok, a version of the pickle model is included in: model/XGBRegressor.pkl

Run the application

For start running the complete application just:

#Run the next command in your cmd/terminal
python api.py

Open the url that it shows you, it would be:

http://127.0.0.1:5000/

And a window, where all fields must be filled in, must be opened (btw sorry for my poor front-end skills). After filling all the fields, just press the button "Predict price" and the predicted price will appear.

Authors

• Mariana Alanis - Initial work -