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Predict Covid per country

Predict new cases of covid-19 infections
Explore the tutorial »


gif of app functionality


About The Project

This is the API Code for my tutorial article:

It paints a picture for developing a machine learning Python API from start to finish and provides help in more difficult areas like the setup with AWS Lambda.

You will find the end result on Rapidapi:

Data

We will use the dataset from https://ourworldindata.org/coronavirus-source-data in csv format.

Built With

  • Github (Code hosting),
  • Anaconda (Dependency and environment management),
  • Docker (for possible further usage in microservices)
  • Jupyter Notebook (code development and documentation),
  • Python (programming language),
  • AWS, especiall AWS Lambda and S3(for deployment),
  • Rapidapi (market to sell)

See machine learning notebook

covering rough data preparation, training, tuning and prediction.


Getting started

git clone https://github.com/Createdd/ml_api_covid.git
docker build -t ml_api_covid .
docker run -d -p 80:8080 ml_api_covid

Getting started with development notebooks

git clone https://github.com/Createdd/ml_api_covid.git
  • Create conda environment conda create --name NAME python=3.7
  • Register new environment in jupyter ipython kernel install --name NAME--user
  • Activate conda environment conda activate PATH_TO_ENVIRONMENT
pip install -r requirements.txt

Note: If you want to to do exploration with Jypter Notebook you would need to install the Conda environment as the Docker setup only works for the production part (Flask server) of the app.

About the author

Daniel is an entrepreneur, software developer and lawyer. His knowledge and interests currently revolve around programming machine learning applications and all its related aspects.

Connect on:

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Remaining ideas:

  • Create an upload script
  • Create a script for deployment. meaning to
    • uninstall unused deps
    • install prod deps
    • do zappa deploy dev

About

This is the API Code for my tutorial article. It paints a picture for developing a machine learning Python API from start to finish and provides help in more difficult areas like the setup with AWS Lambda.

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