diff --git a/test_setup.py b/test_setup.py
index f5b0798..cf54b13 100644
--- a/test_setup.py
+++ b/test_setup.py
@@ -1,22 +1,85 @@
-# Databricks notebook source
-configs = {"fs.azure.account.auth.type": "OAuth",
-           "fs.azure.account.oauth.provider.type": "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider",
-           "fs.azure.account.oauth2.client.id": dbutils.secrets.get(scope = "data-lake", key = "sp-app-id"), #Service Principal App ID
-           "fs.azure.account.oauth2.client.secret": dbutils.secrets.get(scope = "data-lake", key = "sp-password"), #Service Principal Key
-           "fs.azure.account.oauth2.client.endpoint": dbutils.secrets.get(scope = "data-lake", key = "sp-token-endpoint")} #directory id
+# FUTURE SERVICE PRINCIPAL STUFF FOR MOUNTING
+secrets = {}
+secrets["blob_name"] = dbutils.secrets.get(scope = "data-lake", key = "blob-name")
+secrets["blob_key"] = dbutils.secrets.get(scope = "data-lake", key = "blob-key") 
+secrets["container_name"] = dbutils.secrets.get(scope = "data-lake", key = "container-name")
+secrets["subscription_id"] = dbutils.secrets.get(scope = "data-lake", key = "subscription-id")
+secrets["resource_group"] = dbutils.secrets.get(scope = "data-lake", key = "resource-group")
+secrets["ml_workspace_name"] = dbutils.secrets.get(scope = "data-lake", key = "ml-workspace-name")
+secrets["alg_state"] = dbutils.secrets.get(scope = "data-lake", key = "alg-state")
+try:
+  secrets["created_by"] = dbutils.secrets.get(scope = "data-lake", key = "created-by")
+except Exception as e:
+  print("falling back to user set created_by")
+  secrets["created_by"] = "dacrook"
+           
+#create the config variable to pass into mount
+configs = {"fs.azure.account.key." + secrets["blob_name"] + ".blob.core.windows.net":"" + secrets["blob_key"] + ""}
 
-# Optionally, you can add <your-directory-name> to the source URI of your mount point.
-dbutils.fs.mount(
-  source = dbutils.secrets.get(scope = "data-lake", key = "datalake-fqdn"),
-  mount_point = "/mnt/datalake",
-  extra_configs = configs)
+#mounting the external blob storage as mount point datalake for data storage.
+dbutils.fs.mount( source = "wasbs://" + secrets["container_name"] + "@" + secrets["blob_name"] + ".blob.core.windows.net/", 
+                 mount_point = "/mnt/datalake/", 
+                 extra_configs = configs)           
 
-# COMMAND ----------
+#display the files in the folder
+dbutils.fs.ls("dbfs:/mnt/datalake")
 
-census = sqlContext.read.format('csv').options(header='true', inferSchema='true').load("/mnt/datalake/AdultCensusIncome.csv")
-display(census)
+census = sqlContext.read.format('csv').options(header='true', inferSchema='true').load('/mnt/datalake/AdultCensusIncome.csv')
+census.printSchema()
+display(census.select("age", " fnlwgt", " hours-per-week"))
 
-# COMMAND ----------
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, r2_score
+import pickle
 
-#Race condition between display and mounting in interactive environment.
-#dbutils.fs.unmount("/mnt/datalake")
+x = np.array(census.select("age", " hours-per-week").collect()).reshape(-1,2)
+y = np.array(census.select(" fnlwgt").collect()).reshape(-1,1)
+
+#Split data & Train Scalers
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state = 777, shuffle = True)
+x_scaler = StandardScaler().fit(x_train)
+y_scaler = StandardScaler().fit(y_train)
+
+#Transform all data
+x_train = x_scaler.transform(x_train)
+x_test = x_scaler.transform(x_test)
+y_train = y_scaler.transform(y_train)
+y_test = y_scaler.transform(y_test)
+
+model = LinearRegression().fit(x_train, y_train)
+
+y_predicted = y_scaler.inverse_transform(model.predict(x_test))
+
+mae = mean_absolute_error(y_test, y_predicted)
+r2 = r2_score(y_test, y_predicted)
+
+print(mae)
+print(r2)
+
+#Write Files to local file storage
+import os
+#Also works: "/dbfs/tmp/models/worst_regression/dacrook/"
+prefix = "file:/models/worst_regression/"
+if not os.path.exists(prefix):
+  os.makedirs(prefix)
+with open(prefix + "x_scaler.pkl", "wb") as handle:
+  pickle.dump(x_scaler, handle)
+with open(prefix + "y_scaler.pkl", "wb") as handle:
+  pickle.dump(y_scaler, handle)
+with open(prefix + "model.pkl", "wb") as handle:
+  pickle.dump(model, handle)
+  
+#Create an Azure ML Model out of it tagged as dev
+from azureml.core.workspace import Workspace
+from azureml.core.authentication import ServicePrincipalAuthentication
+from azureml.core.model import Model
+print("Logging In - navigate to https://microsoft.com/devicelogin and enter the code from the print out below")
+az_ws = Workspace(secrets["subscription_id"], secrets["resource_group"], secrets["ml_workspace_name"])
+print("Logged in and workspace retreived.")
+Model.register(az_ws, model_path = prefix, model_name = "worst_regression", tags={"state" : secrets["alg_state"], "created_by" : secrets["created_by"]})
+
+#finally unmount the mount.
+dbutils.fs.unmount("/mnt/datalake")