I have another small #KNIME component post at KNIME Community Hub (https://hub.knime.com/alesso/spaces/Public/latest/Plain%20text%20output~H6MwX6p-jLVZV3rY). This one is for small tables that need to be transformed into plain text in #Markdown and other simple formats.

On #KNIME Analytics Platform, according to
@fsf's #FreeSoftware directory at https://directory.fsf.org/wiki/KNIME:

The #lowcode data analytics platform #KNIME does work well with databases and #SQL (Structured Query Language). I would like to show you several concepts and examples. You can use the workflows with your KNIME desktop installation using #SQLite and #H2 - but also #BigData and some fringe cases from the 90ies - think #Paradox databases 🙂

https://medium.com/p/273e27c9702a

Today 3 articles about #KNIME performance, handling large files and optimal settings

KNIME Snippets (2): Unearthing Hidden Node Gems — Managing Missing Values, Row Numbers and some Quick Java and Paths
https://medium.com/p/3c3c7acb019f

KNIME Snippets (1) — Collect and Restore — or how to handle many large files and resume loops
https://medium.com/p/c57795b65d7e

Mastering KNIME: Unlocking Peak Performance with Expert Tips and Smart Settings
https://medium.com/p/49e2c0dab9cc

#knime is excellent - but there are a few things you might want to know or hints that might help you to boost its performance. And you should also consider things like backup or virus scanners. A blog not for the light stuff but to go behind the scenes. And if your question is not covered: you can always ask on the KNIME forum.

https://medium.com/p/49e2c0dab9cc

#Python graphics are made easy with #KNIME’s #lowcode approach. From scatter, violin and density plots to PNG files and Excel exports, these examples will help you transform your data into actionable insights. Also, find out what it is about Kernel-Density-Plots and Penguins ...

https://medium.com/p/841df87b5563

I do a lot of things with #KNIME but today I explore another #lowcode #DataScience tool called #Orange which is entirely based on #Python and can also do a nice comparison of #machinelearning algorithms. Spoiler alert: the results are mostly the same as with KNIME or 'pure' Python but the tool is fun anyway 🙂

https://medium.com/p/d185214037af

#GitHub repository with additional approach using #vtreat

https://github.com/ml-score/orange/tree/main/orange_machine_learning

Everyone has a #ChatGPT blog these days - so why not do some #analytics tasks with the popular #AI Tool, #KNIME and #Python — add some caution and waffles … (https://medium.com/p/c05709dd3bf5)

Machine Learning with #XGBoost or #LightGBM gets better with #hyperparameter optimisation and a tool like #Optuna is there to help. Also, you can integrate the results with #KNIME - a walkthrough with some code (https://medium.com/p/dcf0efdc8ddf)

zu einem Vortrag über #knime habe ich ein paar Anmerkungen verfasst und Beispiele zusammengestellt. Wer an einem Einstieg in die #lowcode Software Plattform interessiert ist kann sich das ansehen
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Medium Blog: KNIME Einführung — einige High Lights (deutsch)
https://medium.com/p/c3f528944970

#knime , #RStats and #ggplot2 come together to create a powerful graphic that you can quickly adapt with the help of KNIME components - “The beautiful Violin Plot that has it all” (https://medium.com/p/b45464428d19)

#knime and #R / #RStudio are great working together - a few hints like employing #conda package management makes their cooperation even better. Also, some examples of what you can do with KNIME and R (like #graphics).

https://link.medium.com/3DFAdI9Qsxb

You can use #KNIME to quickly create interactive #dashboards with your favourite #lowcode tool. With the bundled #Python integration attractive #graphics are easy to create and configure with minimal additional coding.

https://medium.com/p/41ef794b1467

#LightGBM is a powerful #machinelearning tool. If you combine it with the #lowcode data science platform #knime and some hyper-parameter tuning it is even better. You can read my new medium blog about how to do that. You can download the code and workflow and try it for yourself. I also employed some #chatgpt along the way - for the fun of it 😀

https://medium.com/@mlxl/hyperparameter-optimization-for-lightgbm-wrapped-in-knime-nodes-ddb7ae1d7e2

Before you can do fancy #MachineLearning it does make sense to prepare your data - like deal with missing values, remove highly correlated variables and so on. You can do a lot of this by hand or you could employ a ready made tool like #vtreat which has been implemented in #R and #Python - wrapped in a convenient #KNIME workflow.

You can read more in my #Medium story. Also check out the examples mentioned there on the KNIME Hub

https://forum.knime.com/t/data-preparation-for-machine-learning-with-knime-and-the-python-vtreat-package/58679?u=mlauber71

Just 'wrote' my first small snippets of #Python code for a #KNIME user question using the much hyped #ChatGPT (https://openai.com/blog/chatgpt/) and I must say I am somewhat impressed - the initial suggested code was already good but then 'we' started discussing trouble shooting options which were quite on point. Like having a coding-assistant (that one day might take over ....?).

I tried other things, some answers are very general and some links are broken (old training data).

https://forum.knime.com/t/batch-print-from-xlsx-to-jpg/56177/4?u=mlauber71

If you are interested in #KNIME and #DeepLearning with #Keras and #TensorFlow and how to set that up with the help of #Python and #Conda you can check out my new story on Medium.

KNIME and Python — Setting up Deep Learning Environments for Keras and TensorFlow
https://medium.com/p/4b66003858f4

KNIME and Python are just good friends :-) If you want to take it up a notch and get a grip on the whole #Conda, #Yaml and environment stuff and see how to use #Python with #Knime you might want to check out my article on Medium (https://medium.com/@mlxl/knime-and-python-setting-up-and-managing-conda-environments-2ac217792539)

@juxtacognition I have used #RStats "#tabulizer" to extract tables from a #PDF (https://forum.knime.com/t/automate-pdf-reader-and-convert-data-to-excel-table-with-correct-column-mappings/26384/10?u=mlauber71) and "#pdftools" to extract text (https://forum.knime.com/t/unstructured-text-mining-from-pdf/48625/4?u=mlauber71). Maybe you can adapt this. Then there is a #KNIME node that uses "PDFBox" or another parser (https://kni.me/w/kjy6Q-3szxcH6716) - but I have not used it myself

If you are interested in "Advanced #Graphics with #Python and #KNIME " (and also building a so called "Data App") you can watch my talk on YouTube (https://youtu.be/mG2SZiKG9zo?t=2140) and download the full workflow from the hub an use it yourself (https://kni.me/w/nbfX818PlGRUflhK) - #DataScience #DataAnalytics