Evaluation - (Power Team Placement)

Problem Statement

Write an algorithm to predict prices of vacation rentals using Airbnb listing metadata.

Dataset and env files attached below. (to be added)

Submissions will be graded on model accuracy, performance and clarity of code.

Note: We are using an open kaggle dataset that has Airbnb listings in New York and their associated prices, sourced from insideairbnb.com

Go to STEM-Away® Machine Learning Evaluation (to be added) to submit the following files:

  • train.py - code used to train and save your model
  • predict.py - defines a function called ‘predict.py’ that takes as input test data and returns your model’s predictions for that test data
  • model.json (or any other serialized file type) - the actual model itself that gets loaded in predict.py


  • A conda env that specifies versions is attached.
    Please execute the steps below to ensure your submission can be checked. Executing these steps also ensures that everyone engineers their submission with identical environments.
   conda env create -f stemaway_submission_env.yml
   conda activate stemaway_submission
  • This evaluation is mandatory for placement in Machine Learning Power Teams.

  • While we encourage all students to attempt the evaluation, this evaluation is not mandatory for placement in Machine Learning Exploratory Teams.

  • Your first submission is the one that is graded. If you are not sure about the material, please wait until the corresponding training is held.

  • Highest level of academic integrity is expected from all members. Subtle changes are made in the test to discourage copying of answers.


Colin Magdamo, STEM-Away® Principal Mentor