Conveniently tinker with LSTM and Apriori parameters in Excel

Motivation:

We already learned how to code market basket analysis, time series forecasting and recommending engines in Jupyter Notebook in previous posts. We also know how to Pyinstall these Python scripts into exe files and connect with Excel wings. …


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

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…


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

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…


What SAP tables to join to analyze purchase volume and value

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…


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

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…


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

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…


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

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…


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

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…


What SAP tables to join to analyze efficiency

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…


What SAP tables to join to analyze scrapping costs

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…

Jesko Rehberg

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

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