This project was made during the Data Scientist course of Datascientest and used the datasets described below:
- Alex Tavkhelidze - GitHub
- Arif Haidari - LinkedIn, GitHub
- Bernd Brinkmann - GitHub
- Luigi Menale - LinkedIn, GitHub
Coordinator Romain Lesieur
├── .github
│ └── workflows <- Contains the template for the Pull Request
│
├── README.md <- The top-level README for developers using this project.
├── ROADMAP.md <- The summary plan for developers using this project.
│
├── data <- Should be in your computer but not on Github (only in .gitignore)
│ └── 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 name, and a short `-` delimited description, e.g.
│ `NAME_x.x_<step>`.
│
├── references <- Data dictionaries, manuals, links, and all other explanatory materials.
│
├── reports <- The reports that you'll make during this project as PDF
│ └── 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`
│
├── poetry.lock <- Dependencies used by poetry.
├── pyproject.toml <- Package manager.
│
└── streamlit <- Source code for use in this project.
│
├── pages <- Here are the dynamic pages created with Streamlit
│
├── web <- Contains all the static paged that are parsed using Streamlit
│ ├── img <- Images used inside the Streamlit app
│ └── classes <- Images that will show the prediction of each class
│
├── utils.py <- Utility module for presenting and managing the Tensorflow methods
└── app.py <- main Streamlit app
Please refer here
We will use this to mount the GDrive folder.
Project based on the cookiecutter data science project template. #cookiecutterdatascience