kimnganngng / wids_datathon2023

A project obtained rank 139/709 of Women in Data Science Datathon 2023 using CatBoost and LightGBM by Python

WiDS_Datathon2023

A project obtained rank 139/709 of Women in Data Science Datathon 2023 using CatBoost and LightGBM to forecast sub-seasonal temperatures ((temperatures over a two-week period) within the United States.

This model using a pre-prepared dataset consisting of weather and climate information for a number of US locations, for a number of start dates for the two-week observation, as well as the forecasted temperature and precipitation from a number of weather forecast models. (Environment: GPU T4 x2)

Source for more clarification: https://www.kaggle.com/kimnganngng/competitions?tab=completed

Datathon source: https://www.kaggle.com/competitions/widsdatathon2023

Data source: https://www.kaggle.com/competitions/widsdatathon2023/data