Update README.md

README.md

@@ -124,26 +124,29 @@ The dataset prepared using one-hot encoding (to enable the training of the H2O A
 
 Scripts are stored in the src/ directory and should be used in numerical order by name. The purpose of each script is described below:
 
-    01.install_packages.py    --> This script includes all python packages used across all scripts. 
-                                  The user should check which packages they do not yet have installed and install any missing ones.
-                                  It is recommended that all packages be installed to a unique Conda environment set up for handling this dataset & associated ML model.
+    01.install_packages.py      --> This script includes all python packages used across all scripts. 
+                                    The user should check which packages they do not yet have installed and install any missing ones.
+                                    It is recommended that all packages be installed to a unique Conda environment set up for handling this dataset & associated ML model.
                                
-    02.download_dataset.py    --> This script is used to download the dataset directly from the ORD data repository on GitHub. 
-                                  Further details can be found at https://github.com/open-reaction-database 
+    02.download_dataset.py      --> This script is used to download the dataset directly from the ORD data repository on GitHub. 
+                                    Further details can be found at https://github.com/open-reaction-database 
         
-    03.sanitize_data.py       --> This script uses the MolVS package to convert the molecular SMILES strings in the original dataset into canonical SMILES strings 
-                                  (i.e., to perform'sanitization'). The user should input the original dataset saved as a .csv file (Here, "Ahneman_ORD_Data.csv"). 
-                                  The script will output a new .csv file ("Sanitized_Ahneman_ORD_Data.csv") that is identical in structure to the original, 
-                                  but with the sanitized SMILES strings. 
+    03.sanitize_data.py         --> This script uses the MolVS package to convert the molecular SMILES strings in the original dataset into canonical SMILES strings 
+                                    (i.e., to perform'sanitization'). The user should input the original dataset saved as a .csv file (Here, "Ahneman_ORD_Data.csv"). 
+                                    The script will output a new .csv file ("Sanitized_Ahneman_ORD_Data.csv") that is identical in structure to the original, 
+                                    but with the sanitized SMILES strings. 
     
-    04.prepare_data_for_ML.py --> This script takes the sanitized dataset as an input and performs one-hot enconding in order to prepare the data to be used in the 
-                                  H2O AutoML model. A new .csv file ("Prepared_Data.csv") is created to save the dataset after one-hot encoding. 
+    04.prepare_data_for_ML.py   --> This script takes the sanitized dataset as an input and performs one-hot enconding in order to prepare the data to be used in the 
+                                    H2O AutoML model. A new .csv file ("Prepared_Data.csv") is created to save the dataset after one-hot encoding. 
                             
-    05.run_autoML_updated.py  --> This script takes in the one-hot encoded reaction data and splits it into training and test sets (70%/30%). 
-                                  The data is used to train an H2O AutoML model (maximum 8 models, omitting stacked ensemble models). After training
-                                  the H2O AutoML model, the best-performing model suggested by AutoML is selected and analyzed by SHAP analysis. A loss curve
-                                  is also generated for the model, along with a plot comparing the predicted reaction yields from the validation set to the actual
-                                  yields included in the original dataset.
+    05.run_autoML_updated.py    --> This script takes in the one-hot encoded reaction data and splits it into training and test sets (70%/30%). 
+                                    The data is used to train an H2O AutoML model (maximum 8 models, omitting stacked ensemble models). After training
+                                    the H2O AutoML model, the best-performing model suggested by AutoML is selected and analyzed by SHAP analysis. A loss curve
+                                    is also generated for the model, along with a plot comparing the predicted reaction yields from the validation set to the actual
+                                    yields included in the original dataset.
+                                    
+    06.upload_to_huggingface.py --> This script was used to upload datasets used and generated for this project to this Huggingface repository. 
+                                    The datasets package must be installed to run this script. 
 
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 license: mit