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Project for applied classical ML course at the Weizmann institute

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.idea
.idea
 
 
.ipynb_checkpoints
.ipynb_checkpoints
 
 
data
data
 
 
final_project_ml
final_project_ml
 
 
.gitignore
.gitignore
 
 
AMLLS Project.pdf
AMLLS Project.pdf
 
 
AMLLS_Final_Project_Report_Yahel_Tal_Shiri_2024.pdf
AMLLS_Final_Project_Report_Yahel_Tal_Shiri_2024.pdf
 
 
CTGAN_1_new.csv
CTGAN_1_new.csv
 
 
CTGAN_2_new.csv
CTGAN_2_new.csv
 
 
CTGAN_3_new.csv
CTGAN_3_new.csv
 
 
CTGAN_4_new.csv
CTGAN_4_new.csv
 
 
CTGAN_5_new.csv
CTGAN_5_new.csv
 
 
Class_ML_Project.py
Class_ML_Project.py
 
 
Copula_1.csv
Copula_1.csv
 
 
Copula_2.csv
Copula_2.csv
 
 
Copula_3.csv
Copula_3.csv
 
 
Copula_4.csv
Copula_4.csv
 
 
Copula_5.csv
Copula_5.csv
 
 
Diabetic_readmission_essentials.py
Diabetic_readmission_essentials.py
 
 
EDA_Shiri.ipynb
EDA_Shiri.ipynb
 
 
EDA_numeric_portion.ipynb
EDA_numeric_portion.ipynb
 
 
EDA_yahel.ipynb
EDA_yahel.ipynb
 
 
Functions_ML_Project.py
Functions_ML_Project.py
 
 
Main_AMLLS_code.py
Main_AMLLS_code.py
 
 
Main_AMLLS_code_final.py
Main_AMLLS_code_final.py
 
 
README.md
README.md
 
 
after_pipeline_dataset.csv
after_pipeline_dataset.csv
 
 
catboost_hyperparmeter_tuning_cv.py
catboost_hyperparmeter_tuning_cv.py
 
 
default_models_CT_GAN_dataframe.csv
default_models_CT_GAN_dataframe.csv
 
 
default_models_original_dataframe.csv
default_models_original_dataframe.csv
 
 
default_models_smote_dataframe.csv
default_models_smote_dataframe.csv
 
 
id_fold.csv
id_fold.csv
 
 
pip_requirements_yahel.txt
pip_requirements_yahel.txt
 
 
requirements.txt
requirements.txt
 
 
train_fold_1.csv
train_fold_1.csv
 
 
train_fold_2.csv
train_fold_2.csv
 
 
train_fold_3.csv
train_fold_3.csv
 
 
train_fold_4.csv
train_fold_4.csv
 
 
train_fold_5.csv
train_fold_5.csv
 
 
validation_fold_1.csv
validation_fold_1.csv
 
 
validation_fold_2.csv
validation_fold_2.csv
 
 
validation_fold_3.csv
validation_fold_3.csv
 
 
validation_fold_4.csv
validation_fold_4.csv
 
 
validation_fold_5.csv
validation_fold_5.csv
 
 

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ML-project-diabetes-

our project for the ML course

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Project for applied classical ML course at the Weizmann institute

Topics

random-forest scikit-learn ml datascience gan xgboost classification diabetes catboost lgbm imbalanced-classification smote-sampling ctgan imblearn-metrics

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