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 This project is intended to compute an estimated value of risk for a given database.
 
-    1. Pull meta data of the database and create a dataset via joins
+    1. Pull meta data of the database  and create a dataset via joins
     2. Generate the dataset with random selection of features
-    3. Compute risk via SQL using group by
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+    3. Compute risk via SQL using group by
+## Python environment
+
+    The following are the dependencies needed to run the code:
+
+        pandas
+        numpy
+        pandas-gbq
+        google-cloud-bigquery
+
+        
+## Usage
+
+    *Generate The merged dataset
+    
+    python risk.py create --i_dataset <in dataset|schema> --o_dataset <out dataset|schema> --table <name> --path <bigquery-key-file>  --key <patient-id-field-name> [--file ]
+
+    * Cmpute risk
+
+    python risk.py compute --i_dataset <dataset> --table <name> --path <bigquery-key-file>  --key <patient-id-field-name> 
+## Limitations
+    - It works against bigquery for now
+    @TODO:    
+        - Need to write a transport layer (database interface)
+        - Support for referential integrity, so one table can be selected and a dataset derived given referential integrity
+        - Add support for journalist risk
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