README.md

# Title

[Intro]

This repository is organized as follows:
* [EDA](./EDA): 
    * [output](./EDA/output): Plots about feature distributions, correlations and importance.
    * [EDA.ipynb](./EDA/EDA.ipynb): Exploring and filtering data, handling missing values, encoding variables, building the final pre- and post- pandemic datasets, and generating plots for feature distributions, correlations and importance.
* [gen_train_data](./gen_train_data): 
    * [gen_train_data.ipynb](./gen_train_data/gen_train_data.ipynb): Generating training and testing datasets.
* [model_selection](./model_selection): 
    * [hyperparam_tuning.py](./model_selection/hyperparam_tuning.py): Tuning models through a random search of hyperparameters.
    * [cv_metric_gen.py](./model_selection/cv_metric_gen.py): Generating cross-validation metrics and plots for each of the tuned models.
    * [cv_metrics_distr.py](./model_selection/cv_metrics_distr.py): Generating boxplots for each cross-validation metric and tuned model.
    * [test_models.py](./model_selection/test_models.py): Testing tuned models with test dataset.
* [explainability](./explainability):
    * [fit_final_models.py](./explainability/fit_final_models.py): Saving fitted model for each selected final model.
    * [compute_shap_vals.py](./explainability/compute_shap_vals.py): Computing SHAP values for final models.
    * [compute_shap_inter_vals.py](./explainability/compute_shap_inter_vals.py): Computing SHAP interaction values for final models.
    * [shap_plots.py](./explainability/shap_plots.py): Generating SHAP summary plots for the SHAP and SHAP interaction values computed. Comparing major differences between pre- and post-pandemic groups.
    * [output](./explainability/output): 
        * [plots](./explainability/output/plots): SHAP and SHAP interaction summary plots.

[Outro]