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variables_of_exoplanets
kxm1k4m1/variables_of_exoplanets
variables_of_exoplanets is a machine learning model from kxm1k4m1. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Predicting the Variables of Exoplanets : Machine Learning Approach
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Updated Oct 6, 2024
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At a glance
- Access
- Public
- Created
- Oct 6, 2024
- Updated
- Oct 6, 2024
- SHA
- 8a5bd4e2
Model card
From the Hugging Face model README
Nasa_Space_APP_2024 From Boeing 777 Team
Predicting the Variables of Exoplanets : Machine Learning Approach
What does it do?
In this task, AutoGluon is used to predict key variables of exoplanets, including mass (MP), radius (RP), temperature (TP), atmospheric metallicity (logZ), and carbon-to-oxygen ratio (C/O). The predictions are based on the transit depth of exoplanets as measured through specific filters, such as Sloan and Bessel
How does it work?
AutoGluon works by analyzing the transit depth data, which is processed through various machine learning algorithms. It automatically handles data preprocessing and model selection, optimizing hyperparameters to determine the best predictive model for the exoplanet variables. The framework takes the filtered transit depth data as input and outputs the predicted values for the exoplanet characteristics.
What benefits does it have?
Space Exploration: Accurate predictions help identify potentially habitable exoplanets, advancing our understanding of the universe. Cost-Effective Research: AutoGluon streamlines machine learning processes, saving time and resources for more research projects. Tools & Coding Language
Development Tools: Google Colab
Programming Languages: Python
Software: Canva
Link to our github : https://github.com/KamiK4M1/Nasa_Space_APP
- Created
- Oct 6, 2024
- Updated
- Oct 6, 2024