women_kaggle

Analysis of women community on Kaggle using statistical plots and graph representation of users and their skills.

Setup

In order to replicate the analysis you need to follow this steps:

1. You need to have a Kaggle account. If you don't, go to https://www.kaggle.com/ and signup
2. Get the credentials (API token) to download the data:
   • go to the 'My Account' tab of your user profile (https://www.kaggle.com//account)
   • select 'Create API Token'
   • place this file in the location ~/.kaggle/kaggle.json (on Windows in the location C:\Users<Windows-username>.kaggle\kaggle.json)
   • run this line to restrict usage of the token to your current user:

   chmod 600 /home/<username>/.kaggle/kaggle.json 

3. Install pipenv

   pip install --user pipenv

   Or if you are on macOs:

   brew install pipenv

4. Create pipenv environment for the project if one doesn’t already exist

   pipenv shell

5. Install dependencies from Pipfile

   pipenv install

6. Run the notebook Analysis.ipynb in notebooks directory. In first steps, the data will be downloaded and preprocessed.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── 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
│
├── 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, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── 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
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience