Update README.md

README.md

@@ -36,7 +36,9 @@ For educational purposes
 
 ## Training Procedure
 The data preprocessing steps applied include the following:
-- Dropping high cardinality features or no variance features. This include Transaction ID, Sender ID, Sender Account, Beneficiary ID, Beneficiary Account, Sender LOB, Sender Sector and Time
+- Dropping high cardinality features. This includes Transaction ID, Sender ID, Sender Account, Beneficiary ID, Beneficiary Account, Sender Sector 
+- Dropping no variance features. This includes Sender LOB
+- Dropping Time and date feature since the model is not time-series based
 - Transforming and Encoding categorical features namely: Sender Country, Beneficiary Country, Transaction Type, and the target variable, Label
 - Applying feature scaling on all features
 - Splitting the dataset into training/test set using 85/15 split ratio