LightGBM Predicting Water Pump Functionality🔗
Water Pump functionality prediction. Based on the DrivenData challenge Pump it Up: Data Mining the Water Table
See the exploration in Streamlit Cloud 🎈
For better performance, grab the code from github repo
Trying out LightGBM for a @drivendataorg competition assessing water pump functionality. Still need to work on the pre-processing before I go posting any scores though 😆— @firstname.lastname@example.org (@GarsBar35Plus) April 20, 2022
🕹 @streamlit Demo: https://t.co/P4XzP1t9vC
🐙 Github Repo: https://t.co/nn1Ose9tL4#30DaysOfStreamlit
Using data from Taarifa and the Tanzanian Ministry of Water, can we predict which pumps are functional, which need some repairs, and which don't work at all?
Predict one of these three classes based on a number of variables about what kind of pump is operating, when it was installed, and how it is managed.
A smart understanding of which waterpoints will fail can improve maintenance operations and ensure that clean, potable water is available to communities across Tanzania.
- Training and test data from drivendata, along with submission format
- Details in problem description
Feature Exploration and Engineering🔗
Much of this was guided by the DrivenData Competition forum, specifically this user's EDA + Catboost example (I haven't tried out all of his data processing steps... yet)
This package was created with Cookiecutter and the
gerardrbentley/cookiecutter-streamlit project template.
- Cookiecutter: https://github.com/audreyr/cookiecutter
Created: June 7, 2023