Recently, SAP released SAP Predictive Analytics version 3.3. Details on what is in this release can be found in the blog, Announcing the Release of SAP Predictive Analytics. This release touts new support for Python.
Python is one of the most used languages for machine learning and is well equipped in numeric calculation. Embedding predictive analytics libraries into a Python application is a natural progression for SAP Predictive Analytics. In this article, we will take you through setting up the SAP Predictive Analytics Automated Analytics library with a Python environment on Windows.
Details on Python’s use with data science and in general can be found in the links below:
Note: You can also embed the SAP Predictive Analytics library into C++ and Java applications.
Business Benefits of Python
There are many web sites identifying the business benefits of Python. Here are some of my favorites that those of you working in data science may also appreciate.
- Ease of use and readability
- Large community support with many examples to draw upon
- Large list of standard libraries, with many numeric and scientific libraries
- No need for compilation
- Internet of Things (IOT) applications are adopting Python
- Python’s support for procedural, functional and object oriented approaches
- Ability to integrate with Enterprise Applications
I encourage you to do an internet search on “business benefits of using Python.” You may find other reasons that resonate better for your situation.
Additionally, for those who are leveraging the segmenting feature for forecasting in SAP Predictive Factory, using Python could be used to accomplish the same functionality with other types of predictive models.
What Type Of Use-Cases Could Include Predictive Analytics into an Application?
Below are some examples where an application can include predictive analytics as a differentiating feature:
- Recommending products to customers based on prior buying patterns in online web stores
- Scoring customers for the customer service team based a customer’s likelihood to churn in CRM applications
- Forecasting profit, sales growth rates in financial applications
- Alerting maintenance teams when a bearing has an 80% chance of failing to keep operations at running 100% of the time
- Alerting insurance agents on cases that have 90% chance of fraud.
Many SAP Partners embed SAP Predictive Analytics into their application to differentiate their own solution. This article discusses embedding advanced analytics into applications, Predictive Analytics Changing the Game for SAP OEM Partners. Additionally, there are perks for SAP Partners described in the article on Becoming an OEM partner has its benefits.
Steps to Follow
In addition to the prerequisite SAP Predictive Analytics 3.3, you will to get Python. The Anaconda distribution is very popular for Python and you can download it from https://www.anaconda.com/download/ . The default Anaconda install currently installs Python version 3.6. SAP Predictive Analytics requires Python version 3.5. To download this version with Anaconda, download Anaconda version 4.2 which uses Python version 3.5. Anaconda 4.2 can be found on this URL. If you download this version, then you will not need to manage versioning with environments as the instructions below state. Install Anaconda using the graphical installer using the default options.
See the detailed step-by-step guide, including screen shots, at my complete blog (of which this is an excerpt) on SAP Community:
See also my other article, where I take you step-by-step through using Jupyter Notebook with SAP Predictive Analytics. Jupyter Notebook is a user friendly application that many people use with Python. Jupyter Notebook is a browser based tool that enables one to create and share documents that can include live code, equations, visualizations and description text for the code and solutions.