Hotel review's rating prediction model

This project uses Keras LSTM model to predict rating given along with hotel review based on its text. Trip Advisor Hotel Reviews dataset was used to train the model. Model uses pre-train word vectors created with GloVe algorithm to create embedding layer.

Final model is deployed on https://reviews-rating-predictor.herokuapp.com/ as REST API with FastAPI.

Summary of EDA and model selection is here.

Usage

To get a prediction, you need to send a POST request to the /predict endpoint. Place json that contains {"review_text":"<text of review>"} in request's body. Example with curl:

curl \
  --header "Content-Type: application/json" \
  --request POST \
  --data '{"review_text":"<text of review>"}' \
  https://reviews-rating-predictor.herokuapp.com/predict

The app is hosted on a free plan of Heroku, therefore application is switched to sleep mode after 30 minutes of inactivity. You may need to wake app instance up, you can do this with a GET request sent to /status endpoint. After a few moments app should be operating.

For more specific API documentation, see https://reviews-rating-predictor.herokuapp.com/docs , where you can also send POST requests to get prediction result (in case you don't have a tool to send POST requests).

Content

• main.py - main Python file with FastAPI app
• model.py - Python file with model loading and it's functions
• /notebooks - folder with Jupyter notebooks with EDA/preprocessing and model selection
• /lstm_model - folder with prediction model and preprocessing objects
• Procfile - file with command to run on Heroku
• runtime.txt - file to specify runtime on Heroku
• requirements.txt - requirements suited for deploying on Heroku (do not include libraries required to EDA and training)
• requirements_training.txt- requirements for EDA and model training

Tools

• Keras/Tensorflow - building model
• Keras Tuner - searching hyperparameters
• FastAPI - API framework