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algorithm and methods used for creating the DS/ML pipeline

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README.md
README.md
 
 
Soroosh_feature_importance.py
Soroosh_feature_importance.py
 
 
Soroosh_utilities.py
Soroosh_utilities.py
 
 
s04-predict-Ic-IcNorm-DNN-Reg.ipynb
s04-predict-Ic-IcNorm-DNN-Reg.ipynb
 
 
s05-Predict-IcNorm.ipynb
s05-Predict-IcNorm.ipynb
 
 
t03-mssa.ipynb
t03-mssa.ipynb
 
 
t04-timeseries_importance.ipynb
t04-timeseries_importance.ipynb
 
 
t05-matching(problem formulation & understanding).ipynb
t05-matching(problem formulation & understanding).ipynb
 
 
t06-sets_alignment-estimation(assumption1).ipynb
t06-sets_alignment-estimation(assumption1).ipynb
 
 
t06-sets_alignment-search(assumption2).ipynb
t06-sets_alignment-search(assumption2).ipynb
 
 
u01-data-generation.ipynb
u01-data-generation.ipynb
 
 
u02-regressions.ipynb
u02-regressions.ipynb
 
 
u03-regression-and-feature-importance.ipynb
u03-regression-and-feature-importance.ipynb
 
 
u04-regression-and-feature-importance-wandb-reduced-features-increasd-window_size.ipynb
u04-regression-and-feature-importance-wandb-reduced-features-increasd-window_size.ipynb
 
 
u04-regression-and-feature-importance-wandb.ipynb
u04-regression-and-feature-importance-wandb.ipynb
 
 
u05-synthetic-data-generator(Andrey's_proposal).ipynb
u05-synthetic-data-generator(Andrey's_proposal).ipynb
 
 
u06-TGAN.ipynb
u06-TGAN.ipynb
 
 
u06-TimeGAN-no-permutation-physics.ipynb
u06-TimeGAN-no-permutation-physics.ipynb
 
 
u06-TimeGAN-non-stats.ipynb
u06-TimeGAN-non-stats.ipynb
 
 
u06-synthetic-data-generator_final.ipynb
u06-synthetic-data-generator_final.ipynb
 
 

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SuperOX

This repository is a public version of my ML/DS solutions for the project at HSE-Lambda, for creating a Machine Learning and Data Science Pipeline for SuperOX Company to Analysis the production process.

(Here, I shared only the jupyter notebook and not the production solutions)

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algorithm and methods used for creating the DS/ML pipeline

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