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Small challenge to find the best phone operator to use based on call price

License: MIT License

Jupyter Notebook 100.00%
pandas big-data big-data-analytics data-analysis phone-number cheapest pricelist data-cruncher

cheapest-phone-call's Introduction

cheapest-phone-call

Small challenge to find the best phone operator to use based on call price

Methodology

  • We'll be using Python as programing language as it is pretty straightforward to build a fast prototype of the solution, and also as it offers many libraries aimed at making large datasets manipulation easier (pandas, numpy, ...)

  • We'll be using a Jupyter notebook in order to have a maximum of ergonomy and a beautiful visual presentation. Also, there will be no need to download or re-run the code as the results is already displayed in the notebook.

  • Unit Testing will be done within the same notebook, in separated cells to show directly that everything runs as expected.

  • In case we want to have a production-ready prototype that deals with larger datasets, one of the best choices I propose is to use Apache Spark with the Scala API, as it is especially aimed for Big Data, and because it runs on the JVM.

How To

  • $ git clone https://github.com/redouane-dev/cheapest-phone-call.git
  • $ cd cheapest-phone-call
  • Create a virtual environment to avoid dealing with dependency issues and conflicts : $ virtualenv -p python3 venv
  • $ source ./venv/bin/activate. You'll start seeing a (venv) before the shell prompt.
  • (venv) $ pip install -r requirements.txt in order to install all the requirements.

Now you should be all set to run the notebook

  • (venv) $ jupyter notebook prototyping.ipynb

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