Merge pull request #216 from NelGson/master

Readme update - Python

Author: uc-msft

samples/features/machine-learning-services/python/getting-started/rental-prediction/README.md

@@ -1,13 +1,13 @@
 # Build a predictive model with Python using SQL Server 2017 Machine Learning Services
 
-This sample shows how to create a predictive model in Python and operationalize it with SQL Server vNext.
+This sample shows how to create a predictive model in Python and operationalize it with SQL Server 2017
 
 ### Contents
 
 [About this sample](#about-this-sample)<br/>
 [Before you begin](#before-you-begin)<br/>
 [Sample details](#sample-details)<br/>
-[Related links](#related-links)<br/>
+
 
 
 <a name=about-this-sample></a>
@@ -41,17 +41,17 @@ Download a DB backup file and restore it using Setup.sql. [Download DB](https://
 <!-- Examples -->
 1. SQL Server 2017 CTP2.0 (or higher) with Machine Learning Services (Python) installed
 2. SQL Server Management Studio
-3. Python Tools for Visual Studio
+3. Python Tools for Visual Studio or another Python IDE
 
 ## Run this sample
 1. From SQL Server Management Studio or SQL Server Data Tools connect to your SQL Server vNext database and execute setup.sql to restore the sample DB you have downloaded </br>
-2. From SQL Server Management Studio or SQL Server Data Tools, open the Predictive Model Python.sql script </br>
+2. From SQL Server Management Studio or SQL Server Data Tools, open the rental_prediction.sql script </br>
 This script sets up: </br>
 Necessary tables </br>
 Creates stored procedure to train a model </br>
 Creates a stored procedure to predict using that model </br>
 Saves the predicted results to a DB table </br>
-3. You can also try the python script on its own. Just remember to point the Python environment to the corresponding path "C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES" if you run in-db Python Server, or 
+3. You can also try the Python script on its own, connecting to SQL Server and getting data using RevoScalePy Rx functions. Just remember to point the Python environment to the corresponding path "C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES" if you run in-db Python Server, or 
 "C:\Program Files\Microsoft SQL Server\140\PYTHON_SERVER" if you have the standalone Machine Learning Server installed.
 
 <a name=sample-details></a>
@@ -64,9 +64,10 @@ This sample shows how to create a predictive model with Python and generate pred
 The Python script that generates a predictive model and uses it to predict rental counts
 
 ###  rental_prediction.sql
-Takes the Python code in Predictive Model.py and deploys it inside SQL Server. Creating stored procedures and tables for training, storing models and creating stored procedures for prediction.
-
+Takes the Python code in rental_prediction.py and deploys it inside SQL Server. Creating stored procedures and tables for training, storing models and creating stored procedures for prediction.
 
+###  setup.sql
+Restores the sample DB (Make sure to update the path to the .bak file)
 
 
 

samples/features/machine-learning-services/python/getting-started/rental-prediction/Setup.sql

@@ -0,0 +1,13 @@
+-- Before we start, we need to restore the DB for this tutorial.
+-- Step1: Download the compressed backup file (https://deve2e.azureedge.net/sqlchoice/static/TutorialDB.bak)
+--Save the file on a location where SQL Server can access it. For example: C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\Backup\
+-- In a new query window in SSMS, execute the following restore statement, but REMEMBER TO CHANGE THE FILE PATHS
+-- to match the directories of your installation!
+USE master;
+GO
+RESTORE DATABASE TutorialDB
+   FROM DISK = 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\Backup\TutorialDB.bak'
+   WITH
+                MOVE 'TutorialDB' TO 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\DATA\TutorialDB.mdf'
+                ,MOVE 'TutorialDB_log' TO 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\DATA\TutorialDB.ldf';
+GO