1. df_smf.csv
   Sample output of 1_BatchJob_SMF30Extract.ipynb, which is batch job run time metrics, the most important data input for later batch job analytics.
   In z/OS operation log, SMF provides a common interface to extract system operation measurements. SMF Type 30 includes records of batch job operation. 
   In real client environments, such data could be also collected by other 3rd party softwares.

2. MasterBatchJob.csv
   Elapsed time of MBJ in one year, sample data simulated for demo.
   The elasped time of MBJ is the minutes between the start time of first job in MBJ and the end time of last job in MBJ, could be calculated from df_smf.csv

3. TxnVolume.csv
   Transaction Volumns of various business type one year, sample data simulated for demo.   

4. calendar_join.csv
   Calendar data with calenday elements like weekday, day, month and etc.

5. widetable_MBJ.csv
   A joined wide table of MBJ elapsed time, transaction volumes and calendar data, ready for model training for 3_BatchJob_MBJ_Prediction.ipynb.

It includes source code of 4 Python notebooks. You could open them in Jupyter web environment and run them one by one following the instruction inside notebook.

0_readme.ipynb
  This notebook provides an overview of this sample project.

1_BatchJob_SMF30Extract.ipynb
  This notebook extracts batch job operation data from SMF dta set with type 30 records. You could modify SQL statements in notebook to extract information that you’re interested in.

  Please refer to IBM Knowledge Center about SMF type 30 record, to get know more about mainframe job metrics definition according to your z/OS version.
  SMF data set with one year SMF30 data is too large to fit in a sample project so only the output from this notebook - df_smf.csv is included for your information.

2_BatchJob_MBJ_DataExploration.ipynb
  This notebook explores MBJ's elpased time to get insight on trend in timeline, correlation to daytime business volumes and periodicity on week/day/month.

3_BatchJob_MBJ_Prediction.ipynb
  This notebook applies 3 methods to predict MBJ elapsed time based on historical data, calendar information and business transaction volume data, and then emsemble them as final results.

Note:
    In notebook cell, when read local dataset on Waston Machine Learning for z/OS, will need a token for authetication, the expiration period is configurated by adminitrator, when you run cell failed and error message shows that token expired, please click on top right button to get a new up-to-date token to replace the old project context.

This project has 2 flows which you need to open in WMLz.

4_BatchJob_MBJ_TSPredict.str
  This flow applies time series algorithm to predict elapsed time.
  
5_BatchJob_MBJ_AnomalyDetect.str
  This flow detects anomaly in elapsed time and business transaction volumes.

2 zip files with same content, BatchJobAnalytics.zip and BatchJobAnalytics.tar.gz, used for Windows/Mac and linux respecitive.
In web browser with IBM Waston Machine Learning for z/OS, you could "Add Project" from file with the zip file. It will add a new project with previous python notebooks and all datasets at one time. And then you could enjoy to explore the sample project as you like.

Before you run and explore the demo process of analytics, you could find the demo output here, easy and straight forward.  
4 readonly view of html files, with output result of python notebooks are included.
4 screenshot from flow output, with predicted result and abnormal records detected.