Global Terrorism Project

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Description

Welcome ! This is a Data Science project focus in Global Terrorism, built with Python3, jupyter, Sphynx and docker.

Getting started

Prerequisites

IDE

You can choose the IDE you whant, but you need to have some packages installed with it like Python for the live linter. Here an exemple of the needed packages with Visual Studio Code IDE:

• Python

Python3

Since it's a Python-Based project, you need to install some software :

• macOS: Follow the install instructions for your MacOs distribution to install python.

• Windows: Note that you can't run iOS app on windows. Follow the install instructions for your windows distribution to install python.

• Linux: Follow the install instructions for your linux distribution to install python.

Docker

• macOS / Windows : Follow the install instructions for you OS distribution to install docker

• Linux : Follow the install instructions for you Linux distribution to install docker

Install the project

First you need to clone the repository:
Do not forget to upload your SSH Key into github and having the right access.

git clone https://github.com/werayn/global-terrorism

From within the root directory, first remove git tracking from the project

make clean

If you have not already done so, build the Docker image (you will only need to do this once).

make docker-build

If you would like to install additional packages in the container you can add them to requirements.txt and rebuild the image with the previous script.

Run it

When everything is installed, if you want to run it in local, do the following:

make docker-run

This will open a bash shell within the Docker container.
You can now access the full file structure of the project from within the container.

You can open an interactive environment by running :

make local-jupyter

then navigating to https://localhost:8888/ in a browser. You will see a login screen asking for a password or token. Copy the token from your console. Return to the bash console by entering Ctrl-C.

To exit container just run inside :

exit

Test it

First test your environment like this

make test-env

when everything is installed and run well, if you want to run every unit tests, do the following:

make test

Deployement

You need to install all the dependencies before trying to deploy it:

Documentation

if you want to know everything about our project just run :

make gen-doc

and then open the build/index.html in your favorite browser to see our documentation.

Project Organization

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├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interm         <- Intermediate data that has been transformed
│   ├── organized      <- Raw datasets that have been renamed or reorganized into a new folder structure but have not been changed at all      
│   ├── processed      <- The final, canonical data sets for modeling
│   └── raw            <- The original, immutable data dump
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── guide              <- A set of markdown files with documented best practices, guidelines and rools for collaborative projects
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g
│                         `1.0-jqp-initial-data-exploration`
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment
│
└── src                <- Source code for use in this project.
    │
    ├── data           <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │   │                 predictions
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── visualize.py

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Contributing

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

Authors

• Junique Virgile - Initial contributor - Junique Virgile

License

• Apache License 2.0