https://github.com/nancyhamdan/wids-2022
Predicting building energy consumption as part of the WiDS 2022 Datathon
https://github.com/nancyhamdan/wids-2022
ensemble gradientboostingregressor lgbm lgbmregressor random-forest randomforest-regressor regression
Last synced: 17 days ago
JSON representation
Predicting building energy consumption as part of the WiDS 2022 Datathon
- Host: GitHub
- URL: https://github.com/nancyhamdan/wids-2022
- Owner: nancyhamdan
- Created: 2022-03-28T22:50:12.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-28T23:29:13.000Z (about 3 years ago)
- Last Synced: 2025-02-17T11:13:44.158Z (3 months ago)
- Topics: ensemble, gradientboostingregressor, lgbm, lgbmregressor, random-forest, randomforest-regressor, regression
- Language: Jupyter Notebook
- Homepage:
- Size: 11.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Predicting Building Energy Consumption - WiDS 2022 Datathon
This is my solution for the [WiDS 2022 Datathon](https://www.kaggle.com/competitions/widsdatathon2022)
To help combat climate change, this year's WiDS datathon was to predict building energy consumption using a dataset that details buildings characteristics and weather conditions of the area the buildings are at. My solution included minimal feature enginnering and used a blend of a LightGBM and a Random Forest model to get the final predictions. My solution's RMSE score of 22.573 on private test data ranked 81/829 in the datathon, you can view the leaderboard [here](https://www.kaggle.com/competitions/widsdatathon2022/leaderboard).