**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:

`import random`

import string

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics.pairwise import cosine_similarity

import warnings

warnings.filterwarnings(‘ignore’)

import nltk

from nltk.stem import WordNetLemmatizer

nltk.download(‘popular’, quiet=True)…

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…

**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…

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…

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…

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…

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…

**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)…

**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…

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