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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 2 years ago
NEOPOP_output_files Output files of the NEOPOP run 2 years ago
split_dataset NEOPOP DataFrame split into training, validation, test 2 years 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 2 years ago
.gitignore Initial commit 2 years ago
LICENSE Initial commit 2 years 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 2 years ago
README.md Initial commit 2 years 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 2 years ago
dataframe.csv NEOPOP DataFrame 2 years 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) 2 years ago
expl_data_analysis.ipynb Jupyter notebook for preliminary data exploration 2 years ago
kep_to_att.ipynb Conversion from Keplerian elements to attributable elements 2 years ago
neopop_attr.csv DataFrames with the attributable elements 2 years ago
neos_attr.csv DataFrames with the attributable elements 2 years ago
neos_dataframe.csv Pre-processed NEOs DataFrame 2 years ago
neos_dataset_preprocessing.ipynb Jupyter Notebook for pre-processing of the NEOs DataFrame 2 years ago
sbdb_query_results.csv NEOs DataFrame 2 years ago
weight_initialization.ipynb Jupyter notebook for choosing the best initialization technique 2 years 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.