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End to end model of Data Analysis & Prediction using Python on SAP HANA Table data: This blog helps to connect with SAP HANA DB (Version 1.0 SPS12) then extract the data from HANA table/View and analyze the data using Python Pandas library. Then you can clean and select independent variables/features data to feed the Machine learning algorithms to predict dependent variables or find insights. In today’s digital economy, businesses cannot take action on stale insights, thus a true in-memory data platform should support real-time processing for transactions and analytics for all of a company’s data. SAP HANA helps to manage data in a single in-memory platform, so you can take action in the moment. Accelerate the pace of innovation and run live in this new digital economy. SAP HANA Capabilities include database services, advanced analytics processing, app development, data access, administration, and openness. Python is becoming popular in analytics and data science. It
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Market Basket Analysis of SAP HANA table (invoices) using R script . Objective:   If you have SAP HANA data base which stores all the enterprise transaction data and want to apply predictive/machine learning algorithms on the HANA data base tables or views using R. This blog gives you the steps to connect SAP HANA data base from R and retrieve tables/views/procedures data and apply R statistical algorithms or machine learning techniques to get the insights of data. Back Ground and use case: R is a data science scripting language for teams that unites data prep, machine learning, text mining and predictive model deployment.   It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization. SAP HANA is an in-memory, column-oriented, relational datab
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