Walkthrough: /reports
Summarize project in 500 words...
├── README.md <- Description of project and context, overview of project and workflow.
├── data <- Scripts to clean data
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Scripts for training models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ and a short `-` delimited description, e.g. `1.0-initial-data-exploration`.
│
│
├── references <- Take-home challenge explanatory materials given.
│
├── reports <- Overview of analysis as jupyter notebook or PDF.
│ └── figures <- Scripts for 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`
Project based on the cookiecutter data science project template. #cookiecutterdatascience
Report workflow was based on Chip Huyen's book Machine Learning Systems Design