Analysis of women community on Kaggle using statistical plots and graph representation of users and their skills.
In order to replicate the analysis you need to follow this steps:
- You need to have a Kaggle account. If you don't, go to https://www.kaggle.com/ and signup
- 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
- Install pipenv
Or if you are on macOs:
pip install --user pipenv
brew install pipenv
- Create pipenv environment for the project if one doesn’t already exist
pipenv shell
- Install dependencies from Pipfile
pipenv install
- Run the notebook
Analysis.ipynb
innotebooks
directory. In first steps, the data will be downloaded and preprocessed.
├── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience