https://github.com/kenza-ai/the-wise-ml-example

The Wise Machine Learning Example
https://github.com/kenza-ai/the-wise-ml-example

Last synced: about 2 months ago
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The Wise Machine Learning Example

• Host: GitHub
• URL: https://github.com/kenza-ai/the-wise-ml-example
• Owner: Kenza-AI
• License: other
• Created: 2017-11-11T16:13:43.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-07T23:43:34.000Z (over 2 years ago)
• Last Synced: 2025-02-14T17:40:41.045Z (4 months ago)
• Language: Python
• Size: 127 KB
• Stars: 1
• Watchers: 5
• Forks: 1
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
[![Build Status](https://travis-ci.org/Kenza-AI/the-wise-ml-example.svg?branch=master)](https://travis-ci.org/Kenza-AI/the-wise-ml-example)

The Wise ML Example

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

A sample ML Kenza example.

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.

    │

    ├── 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`

    │

    ├── 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

    │   │   │                 

    │   │   └── 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


Project commands

----------------

Run `make` to see all available commands:

![commands](images/commands.png)

Make sure to commit/push changes to Git before running Kenza related commands.