renamed folders, renamed python tutorial files, updated readme files

Author: negust-microsoft

samples/features/machine-learning-services/python/getting-started/predictive-model/README.md renamed to samples/features/machine-learning-services/python/getting-started/rental-prediction/README.md

@@ -60,10 +60,10 @@ Saves the predicted results to a DB table </br>
 
 This sample shows how to create a predictive model with Python and generate predictions using the model and deploy that in SQL Server with SQL Server Machine Learning Services. 
 
-### predictive_model.py
+### rental_prediction.py
 The Python script that generates a predictive model and uses it to predict rental counts
 
-### predictive_model_python.sql
+###  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.
 
 

samples/features/machine-learning-services/python/getting-started/predictive-model/predictive_model.py renamed to samples/features/machine-learning-services/python/getting-started/rental-prediction/rental_prediction.py

@@ -8,7 +8,7 @@
 
 
 def get_rental_predictions():
-    conn_str = 'Driver=SQL Server;Server=NELLIELAPTOP\\SQLSERVER20;Database=TutorialDB;Trusted_Connection=True;'
+    conn_str = 'Driver=SQL Server;Server=MYSQLSERVER;Database=TutorialDB;Trusted_Connection=True;'
     column_info = { 
             "Year" : { "type" : "integer" },
             "Month" : { "type" : "integer" }, 

samples/features/machine-learning-services/python/getting-started/predictive-model/predictive_model_python.sql renamed to samples/features/machine-learning-services/python/getting-started/rental-prediction/rental_prediction.sql

@@ -26,9 +26,8 @@ BEGIN
     EXECUTE sp_execute_external_script
       @language = N'Python'
     , @script = N'
-import pandas as pd   
-df = pd.DataFrame(rental_train_data)
-print(df)
+
+df = rental_train_data
 
 # Get all the columns from the dataframe.
 columns = df.columns.tolist()
@@ -84,8 +83,8 @@ BEGIN
 import pickle
 rental_model = pickle.loads(py_model)
 
-import pandas as pd   
-df = pd.DataFrame(rental_score_data)
+  
+df = rental_score_data
 #print(df)
 
 # Get all the columns from the dataframe.

samples/features/r-services/README.md

@@ -1,8 +1,6 @@
-# Samples for SQL Server R Services
+# Samples for SQL Server Machine Learning Services
+
 
-Go to the getting started tutorials to learn more about:
-[Predictive Modeling with R Services](https://www.microsoft.com/en-us/sql-server/developer-get-started/rprediction) 
-[Customer Clustering with R Services](https://www.microsoft.com/en-us/sql-server/developer-get-started/rclustering) 
 
 [Telco Customer Churn](Telco Customer Churn)
 

samples/features/r-services/Getting-Started/Predictive-Modeling/Predictive Model.R renamed to samples/features/r-services/getting-started/rental-prediction/Predictive Model.R

@@ -2,7 +2,7 @@
 ##################### STEP1 - Connect to DB and read data ####################
 
 #Connection string to connect to SQL Server named instance
-connStr <- paste("Driver=SQL Server; Server=", "NELLIELAPTOP\\VNEXTCTP14", 
+connStr <- paste("Driver=SQL Server; Server=", "MYSQLSERVER", 
                 ";Database=", "Tutorialdb", ";Trusted_Connection=true;", sep = "");
 
 #Get the data from SQL Server Table