This is an open solution to the Santander Value Prediction Challenge.
We are building entirely open solution to this competition. Specifically:
- Check live preview of our work on public projects page: Santander Value Prediction Challenge.
- Source code and issues are publicly available.
Rules are simple:
- Clean code and extensible solution leads to the reproducible experimentations and better control over the improvements.
- Open solution should establish solid benchmark and give good base for your custom ideas and experiments.
- Clone repository and install requirements (check requirements.txt)
- Register to the neptune.ml (if you wish to use it)
- Run experiment:
neptune run --config neptune_random_search.yaml main.py train_evaluate_predict --pipeline_name SOME_NAME
- Clone this repository
git clone https://github.com/minerva-ml/open-solution-value-prediction.git
- Install requirements in your Python3 environment
pip3 install requirements.txt
- Register to the neptune.ml (if you wish to use it)
- Update data directories in the neptune.yaml configuration file
- Run experiment:
neptune login
neptune run --config neptune_random_search.yaml main.py train_evaluate_predict --pipeline_name SOME_NAME
- collect submit from
experiment_directory
specified in the neptune.yaml
You are welcome to contribute your code and ideas to this open solution. To get started:
- Check competition project on GitHub to see what we are working on right now.
- Express your interest in paticular task by writing comment in this task, or by creating new one with your fresh idea.
- We will get back to you quickly in order to start working together.
- Check CONTRIBUTING for some more information.
There are several ways to seek help:
- Kaggle discussion is our primary way of communication.
- Read project's Wiki, where we publish descriptions about the code, pipelines and supporting tools such as neptune.ml.
- Submit an issue directly in this repo.