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

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Vanessa Vichi fb3ba5006d Saved weights of Models 1,2 after a 500-epoch training (for both models) and a 1000-epoch training for Model 1		6 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
trained_models	Saved weights of Models 1,2 after a 500-epoch training (for both models) and a 1000-epoch training for Model 1	6 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	6 months ago
neos_dataset_preprocessing.ipynb	Jupyter Notebook for pre-processing of the NEOs DataFrame	6 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.