PyCharm is the perfect choice to deploy your Jupyter Notebook chatbot as a web app.

Photo by Alex Knight on Unsplash

Motivation:

Jupyter Notebooks are useful for developing on your local machine. But how can other people access your chatbot if it is only alive on your PC? In this post I am going to show you how to go live with your Jupyter Notebook chatbot using PyCharm.

Solution:

Our Jupyter chatbot’s job is to answer frequently asked questions. For this example, I used the Jupyter Notebook from Parul Pandey. I have only slightly amended that code and logged unanswered user input. Let’s have a look:


Analytic job titles well-defined (part 3.3 of 12)

(Photo by Hunters Race on Unsplash)

The ability to take data- to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill (Hal Varian, Google’s Chief Economist, NYT, 2009).

This data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll…


What SAP tables to join to analyze purchase volume and value

Did we spend too much? (Planet plastic, image by author)

Motivation:

Every company must have a close eye on its spendings. Wouldn’t it be interesting to analyze purchase volume over time, storing that data out of your ERP in a datawarehouse? Below’s SQL shows you how to get there, from a SAP BI point of view. Please note that there is no overall 100% correct and complete solution for this request, since all SAP ERP are corporate unique. But it should definitely guide you the right way.

Solution:

If you are looking for purchase volume, EKPO (purchasing document item) and EKKO (purchasing document header) are the central SAP tables of…


Labeling services concretely explained by sales data step by step (part 3.2 of 12)

I guess I am working for free (fair use screenshot)

This data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll conquer the concepts of data science, explore our data, reflect on data visualization and storytelling, predict future sales, mine market baskets and recommend products to customers. In the end we’ll build a data product in the shape of a complete ready…


Knowledge sharing concretely applied to sales data step by step (part 3.1 of 12)

V formation helps the individual save energy (image by author)

This data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll conquer the concepts of data science, explore our data, reflect on data visualization and storytelling, predict future sales, mine market baskets and recommend products to customers. In the end we’ll build a data product in the shape of a complete ready…


Concretely applied to sales data step by step (part 2.3 of 12)

Even clouds are not heavenly safe so consider seat belts (image by author)

This data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll conquer the concepts of data science, explore our data, reflect on data visualization and storytelling, predict future sales, mine market baskets and recommend products to customers. In the end we’ll build a data product in the shape of a complete ready…


Concretely applied to sales data in Jupyter Notebook step by step (part 2.2 of 12)

(Photo by Tim van Cleef on Unsplash)

This post is not about if you have Domingos excellent book about the master algorithm [1] in your shelf. Also not about why China’s president Xi Jinping does.

But this data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll conquer the concepts of data science, explore our data, reflect on data…


What SAP tables to join to analyze efficiency

It’s all about efficiency (French rocks, image by author)

Motivation:

Even the best manufacturing processes will vary regarding their efficiency. Wouldn’t it be interesting to analyze efficiency over time, storing that data out of your ERP in a datawarehouse? Below’s SQL shows you how to get there, from a SAP BI point of view. Please note that there is no overall 100% correct and complete solution for this request since all SAP ERP are corporate unique. But it should definitely guide you the right way.

Solution:

If you are looking for efficiency, tables AFVV (operation quantity, value and date), AFVC (operation within an order) and CRHD (work center header)…


What SAP tables to join to analyze scrapping costs

Nobody’s perfect (Fondor, with permission from Matthias Böhler)

Motivation:

Even the best manufacturing processes will produce scrap, at least a little bit from time to time. Wouldn’t it be interesting to analyze these scrapping costs over time using, storing that data out of your ERP in a datawarehouse? Below’s SQL shows you how to get there, from a SAP BI point of view. Please note that there is no overall 100% correct and complete solution for this request, since all SAP ERP are corporate unique. But it should definitely guide you the right way.

Solution:

If you are looking for scrap costs, QMFE is the central SAP table…


Concretely applied to sales data step by step (part 2.1 of 12)

You do not need much to get started on Jupyter Notebook (Norwegian woods, image by author)

This data driven dealings development (DDDD) series aims at people who want to learn the concepts of statistical analysis, machine learning (ML), deep learning (DL), artificial intelligence (AI), statistical process control (SPC), data mining and data science (DS) with sales data in practice. It’s meant as a truly exhaustive explanation starting from scratch with easy to adapt data. We’ll conquer the concepts of data science, explore our data, reflect on data visualization and storytelling, predict future sales, mine market baskets and recommend products to customers. In the end we’ll build a data product in the shape of a complete ready…

Jesko Rehberg

Writing Books about Data Analysis using statistical and machine learning models at DAR-Analytics.com.

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