By: Alondra Caridad, Sam Droge, Sarah Huerta, Atticus Wong
In this project, we analyzed the El Nino dataset, which contains 178,080 observations of oceanographic and meteorological measurements recorded by a series of moored buoys positioned throughout the equatorial Pacific. The data were collected using the Tropical Atmosphere Ocean (TAO) array, developed under the international Tropical Ocean Global Atmosphere (TOGA) program and maintained by the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL).
In particular, we used this data to predict the occurences of El Nino. This repo contains the EDA and machine learning models that our group produced while doing so. The dataset can be found here.
The presentation can be found in Final_Presentation.
This project used Python 3.11 for data science on deepnote, along with various libraries.