Data Science for COVID-19 analysis
Applied Data Science for COVID-19 track
The goal of this project is to develop a COVID-19 analysis prototype
The final result is shown in a dynamic dashboard where we will select the countries and calculated data sets like the current Doubling Rate of confirmed cases.
Techniques used are Python pandas,scikit learn,Facebook prophet ,plotly,Dash.
For the development we have used a Industry standard CRISP-DM in which we have defined project goal and Business Understanding.
Business understanding (where we have defined the goal of the project). Data Understanding (where do we get data and cleaning of data). Data Preparation (data transformation and visualization). Modeling (Statistics, Machine Learning, and SIR Simulations on COVID Data). Deployment (how to deliver results, dynamic dashboards in python).
The final result is a dymanic dashboard in which the raw data from the Johnhopkins is visvalised the data in the form of a dashbaoard , where we can select four different options suhc as Timeline confirmed , Timeline confirmed filtered , Timeline doubling rate , Timeline doubling rate filtered.
In the later stages of the project we tried to implement FB prophet where we can predict the future virus spread and predictive analysis.
After that we tried using a mathematical modelling for infectious disease and SIR Simulations on COVID Data
├── 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