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Neural_Network_for_MOID_Prediction
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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 4c2bfb1af9 Jupyter notebook used for 1) training the NN, 2) evaluating its performance on the NEOPOP test set and on the real NEOs dataset 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
NeuralNetwork.ipynb Jupyter notebook used for 1) training the NN, 2) evaluating its performance on the NEOPOP test set and on the real NEOs dataset 6 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
neopop_attr.csv DataFrames with the attributable elements 6 months ago
neos_attr.csv DataFrames with the attributable elements 6 months ago
neos_dataframe.csv Pre-processed NEOs DataFrame 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.