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RF_models
RF_models
 
 
csv_files
csv_files
 
 
data
data
 
 
figures
figures
 
 
py_scripts
py_scripts
 
 
unet_models
unet_models
 
 
00_project_overview.ipynb
00_project_overview.ipynb
 
 
01_data_check.ipynb
01_data_check.ipynb
 
 
02_min_max.ipynb
02_min_max.ipynb
 
 
03_hist_1D.ipynb
03_hist_1D.ipynb
 
 
04_hist_2D.ipynb
04_hist_2D.ipynb
 
 
05_RF_data.ipynb
05_RF_data.ipynb
 
 
06_DL_data.ipynb
06_DL_data.ipynb
 
 
07_RF_model.ipynb
07_RF_model.ipynb
 
 
08_RF_model_balanced_accuracy.ipynb
08_RF_model_balanced_accuracy.ipynb
 
 
09_RF_class_weighted_model.ipynb
09_RF_class_weighted_model.ipynb
 
 
10_RF_bootstrap_class_weighted_model.ipynb
10_RF_bootstrap_class_weighted_model.ipynb
 
 
11_balanced_RF_model_balanced_accuracy.ipynb
11_balanced_RF_model_balanced_accuracy.ipynb
 
 
12_easy_ensemble_RF_model.ipynb
12_easy_ensemble_RF_model.ipynb
 
 
13_balanced_RF_model_f1.ipynb
13_balanced_RF_model_f1.ipynb
 
 
14_balanced_RF_model_precision.ipynb
14_balanced_RF_model_precision.ipynb
 
 
15_RF_data_expanded.ipynb
15_RF_data_expanded.ipynb
 
 
16_balanced_RF_model_f1_expanded.ipynb
16_balanced_RF_model_f1_expanded.ipynb
 
 
17_RF_model_results.ipynb
17_RF_model_results.ipynb
 
 
18_unet_model_tf2_training.ipynb
18_unet_model_tf2_training.ipynb
 
 
19_unet_model_performance_ld3_fr16.ipynb
19_unet_model_performance_ld3_fr16.ipynb
 
 
20_unet_model_performance_ld3_fr16_p6.ipynb
20_unet_model_performance_ld3_fr16_p6.ipynb
 
 
21_unet_model_performance_ld3_fr64.ipynb
21_unet_model_performance_ld3_fr64.ipynb
 
 
22_unet_model_performance_ld3_fr64_udw.ipynb
22_unet_model_performance_ld3_fr64_udw.ipynb
 
 
23_DL_data_expanded.ipynb
23_DL_data_expanded.ipynb
 
 
24_unet_model_performance_ld3_fr64_expanded.ipynb
24_unet_model_performance_ld3_fr64_expanded.ipynb
 
 
Final_presentation.pdf
Final_presentation.pdf
 
 
Final_presentation.pptx
Final_presentation.pptx
 
 
MSG_ML_conda_environment.yml
MSG_ML_conda_environment.yml
 
 
Readme
Readme
 
 

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Capstone project of the EPFL Applied Data Science: Machine Learning course

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