Skip to content

Latest commit

 

History

History
 
 

12-capstone

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
README.md
README.md
 
 

README.md

12. Capstone Project

Now apply everything we learned so far yourself.

That will include:

  • Thinking of a problem that's interesting for you and finding a dataset for that
  • Describing this problem and explaining how a model could be used
  • Preparing the data and doing EDA, analyzing important features
  • Training multiple models, tuning their performance and selecting the best model
  • Exporting the notebook into a script
  • Putting your model into a web service and deploying it locally with docker
  • Bonus points for deploying the service to the cloud

Datasets

Deliverables

For this project, you repository/folder should contain the following:

  • README.md with
    • Description of the problem
    • Instructions on how to run the project
  • Data
    • You should either commit the dataset you used or have clear instructions how to download the dataset
  • Notebook (suggested name - notebook.ipynb) with
    • Data preparation and data clearning
    • EDA, feature importance analysis
    • Model selection process and parameter tuning
  • Script train.py (suggested name)
    • Training the final model
    • Saving it to a file (e.g. pickle)
  • Script predict.py (suggested name)
    • Loading the model
    • Serving it via a web serice (e.g. with Flask)
  • Pipenv and Pipenv.lock
    • or equivalents: conda environment file, requirements.txt or pyproject.toml
  • Dockerfile for running the service
  • Deployment
    • URL to the service your deployed or
    • Video or image of how you interact with the deployed service

Cheating and plagiarism

Plagiarism in any form is not allowed:

  • Taking somebody's else notebooks and projects (in full or partly) and using it for the capstone project
  • Re-using your own projects (in full or partly) from other courses and bootcamps
  • Re-using your midterm project from ML Zoomcamp

Violating any of this will result in 0 points for this project.

Submit the results

Submit your results here: https://forms.gle/eLpDSidx8phJxPfH8

Deadline

The deadline for submitting is 14 December 2021 (Tuesday), 18:00 CET. After that, the form will be closed.

FAQ

Check the FAQ from the midterm project:

Peer reviewing

To evaluate the projects, we'll use peer reviewing. This is a great opportunity for your to learn from each other.

  • To get points for your project, your need to evaluate 3 projects of your peers
  • You get 3 extra point for each evaluation
  • Find the projects to review here

Function for computing the hash of your email:

from hashlib import sha1

def compute_hash(email):
    return sha1(email.lower().encode('utf-8')).hexdigest()

Tip: you can use https://nbviewer.org/ to render notebooks if GitHub doesn't work

Evaluation Criteria

The project will be evaluated using these criteria:

  • Problem description
  • EDA
  • Model training
  • Exporting notebook to script
  • Model deployment
  • Reproducibility
  • Dependency and environment management
  • Containerization
  • Cloud deployment

Criteria

Submitting evaluations

  • To submit the evaluations, use this form: https://forms.gle/tHksJjmshDnMM8N57
  • You will need to submit this form three times - one for each evaluated project
  • Deadline: 20 December (Monday), 17:00 CET. After that, the form will be closed.

Community notes

  • Add your notes here