diff --git a/README.md b/README.md
index 6c76b2146225b563f432691b4fc10ed58f83672f..ee11f5c01a9c19bfc51e405c62e4b31f119d7565 100644
--- a/README.md
+++ b/README.md
@@ -38,12 +38,13 @@ This repository is organized as follows:
     * [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.
+    * [fit_final_models.py](./model_selection/fit_final_models.py): Saving fitted model for each selected final model.
     * [output](./model_selection/output):
         * [hyperparam](./model_selection/output/hyperparam): Excel file containing the optimal hyperparameters for each model in each pipeline.
         * [cv_metrics](./model_selection/output/cv_metrics): Material related to the results of cross-validation: scores, ROC and Precision-Recall curves and boxplots for each metric. 
         * [testing](./model_selection/output/testing): Material related to the results of testing the tuned models: scores, ROC and Precision-Recall curves and confusion matrices.
+        * [fitted_models](./model_selection/output/fitted_models): Final selected trained models.
 * [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.