This repo contains an Assignment for betclick
- clone the repo
- `pip install -r "requirements.txt"``
cd ./src
Run with docker:
change env variable PASSWORD in docker-compose
sudo docker-compose up --build
- fit the model on data using:
python main.py
The script will: - download the data, and optionnally ask a password for unzipping - label the data and drop leaky rows - preprocess and write serializables necessary for inference on disk
python main.py --predict
- predict whether each customer in a subsample of the dataset is a potential churner.
- write a file on disk in the preds folder the first column rerpresents customer_key the second column is the target
| customer_key | is_churner |
|---|---|
| 10390929 | True |
| 10390926 | True |
| 10390926 | False |
| 10390926 | True |
| 10390926 | False |
| 10390926 | False |
python main.py --predict --private_file "my_data_file.csv"
The data should have the same format as the original one, it might be necessary to handle mix typed columns.