https://github.com/rasulkireev/kickstarter-data-analysis

https://github.com/rasulkireev/kickstarter-data-analysis

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/rasulkireev/kickstarter-data-analysis
• Owner: rasulkireev
• License: mit
• Created: 2019-09-27T19:44:40.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-08-23T17:53:41.000Z (over 2 years ago)
• Last Synced: 2024-12-16T21:26:35.586Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 29.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
kickstarter-data-analysis

==============================

Analyzing Kickstarter data to predict the success of any given project.

## Data

You can get the datasets from:

https://drive.google.com/file/d/1DnUBwVchA7DjBj_Md1z9L8yJpr3qAQMD/view?usp=sharing

https://drive.google.com/file/d/1LCV_qBez8NgfE11DqGnjJ8--nPntXqX0/view?usp=sharing

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.testrun.org

--------

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