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Project Name

This project is a starting Pack for MLOps projects based on the subject "road accident". It's not perfect so feel free to make some modifications on it.

Project Organization

├── LICENSE
├── 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.
│
├── logs               <- Logs from training and predicting
│
├── 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
│   │   ├── check_structure.py    
│   │   ├── import_raw_data.py 
│   │   └── 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
│   └── config         <- Describe the parameters used in train_model.py and predict_model.py

Steps to follow

Convention : All python scripts must be run from the root specifying the relative file path.

1- Create a virtual environment using Virtualenv.

`python -m venv my_env`

Activate it

`./my_env/Scripts/activate`

Install the packages from requirements.txt

`pip install -r .\requirements.txt` ### You will have an error in "setup.py" but this won't interfere with the rest

2- Execute import_raw_data.py to import the 4 datasets.

`python .\src\data\import_raw_data.py` ### It will ask you to create a new folder, accept it.

3- Execute make_dataset.py initializing ./data/raw as input file path and ./data/preprocessed as output file path.

`python .\src\data\make_dataset.py`

4- Execute train_model.py to instanciate the model in joblib format

`python .\src\models\train_model.py`

5- Finally, execute predict_model.py with respect to one of these rules :

  • Provide a json file as follow :

    python ./src/models/predict_model.py ./src/models/test_features.json

test_features.json is an example that you can try

  • If you do not specify a json file, you will be asked to enter manually each feature.

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