This repository contains the codes used to explore the data generated by NEOPOP, build the neural network, tune the hyperparameters and analyse the results.

You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

Go to file

Vanessa Vichi 2077014614 Conversion from Keplerian elements to attributable elements		7 months ago
NEOPOP_output_files	Output files of the NEOPOP run	7 months ago
split_dataset	NEOPOP DataFrame split into training, validation, test	7 months ago
.gitignore	Initial commit	7 months ago
LICENSE	Initial commit	7 months ago
README.md	Initial commit	7 months ago
baseline_performance.ipynb	Jupyter notebook for evaluating the baseline performance: comparison of various metrics for the baseline model, the linear regression model and the polynomial regression model of degrees 2 and 3	7 months ago
dataframe.csv	NEOPOP DataFrame	7 months ago
dataset_splitting.ipynb	Jupyter notebook for splitting the NEOPOP dataset into training, validation and test (with checks over the distribution of the various parts)	7 months ago
expl_data_analysis.ipynb	Jupyter notebook for preliminary data exploration	7 months ago
kep_to_att.ipynb	Conversion from Keplerian elements to attributable elements	7 months ago
neos_dataset_preprocessing.ipynb	Jupyter Notebook for pre-processing of the NEOs DataFrame	7 months ago
sbdb_query_results.csv	NEOs DataFrame	7 months ago
weight_initialization.ipynb	Jupyter notebook for choosing the best initialization technique	7 months ago

README.md

Neural_Network_for_MOID_Prediction

This repository contains the codes used to explore the data generated by NEOPOP, build the neural network, tune the hyperparameters and analyse the results.