📜 Evaluation - Exploratory & Power Team Placement

Problem Statement

Determine the dynamics of which features predict New York City Airbnb rental prices.
Develop an algorithm to accurately predict prices using those features.

Training dataset and environment.yml (recipe to create a new conda env) attached below.

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


  • Download the environment recipe below (versions are specified in the file)
    stemaway_ML_env.yml (1.2 KB)

    NOTE: Please remove ‘glmnet’ if this library is causing an issue. You can create a submission without the help of this library.

  • 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_ML_env.yml
   -> This will attempt to build the environment. It may outright fail, or fail building a particular package and build the env up to the package
   conda activate stemaway_ML_env
   -> If the above step works, the environment was setup properly
   conda list 
   -> This will show the packages involved

Go to STEM-Away® Machine Learning Evaluation to submit the following files (you will need to create a zip of the 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

Please visit the recordings under Discussion Board to learn more about the setup, goals, possible algorithms and submission templates.


  • 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