This is the solution code from the 'Robi Axiata Datathon 2019' problem set of from EASY category No. 04

Please follow this Wiki section to get an idea on how I strategise my initiatives to reach the common goal.

https://github.com/rafatrock/axiata-robi-datathon-19-easy-04/wiki

To run the desired operations you need to install all the dependencies. Since this is a competion where we need to deal with gigagntic amount of data, the best approach is to prepare ourselved first to handle that data and then start implementing the code. In this tutorial I already discussed different strategies on how to deal with Big Data. Please follow the steps so that you can ready to conduct the massive computational operations.

https://github.com/rafatrock/bigdata-prepare-python

You can either upload the .py file or .ipynb file in the cloud environment and run the script to get the desired outcome.

To plan and execute the desired operations and Python codes I utilised Jupyter Notebook to make this journey interactive. Please follow this Notebook file to see how the operations been performed and their outputs.

https://github.com/rafatrock/axiata-robi-datathon-19-easy-04/blob/master/robi_datathon_19_easy_04.ipynb

The output is a table with two column to display the desired outcomes.

Quantile 0.10:
[3.7233584084182585]
Quantile 0.25:
[6.905825849288114]
Quantile 0.75:
[16.975276533928643]

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

We use SemVer for versioning. For the versions available, see the tags on this repository.

Rubaiyat Islam Rafat - Initial work - rafatrock

This project is licensed under the MIT License - see the LICENSE.md file for details