Regressive model to predict clients' probability to return to the online shop.
├── README.md <- The top-level README for developers using this project.
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├── data <- Data files and methods to transform it
│ ├── ab_test <- Test data for AB test
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│ ├── iteration_1
│ ├── iteration_2
│ └── iteration_3 <- Subsequent iterations of data delivered by client
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├── models <- Trained and serialized models, model predictions, or model summaries
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├── notebooks <- Jupyter notebooks.
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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├── microsevice <- Source code for microservice used in this project.
│ ├── __init__.py <- Makes src a Python module
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│ ├── features <- Methods to turn raw data into features for modeling
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│ └── models <- Methods to train models and then use trained models to predictions
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├── tests <- Tests and scripts to test AB test
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└── requirements.txt <- File with all python dependecies
- Create an environment with
python -m venv venv - Activate environment with
./venv/Scripts/activate - Install requirements with
pip install -r requirements.txt - Run microservice with
python ./microservice/main.py
Project partially based on the cookiecutter data science project template. #cookiecutterdatascience