Building a basic Model for Churn Prediction with KNIME 

this was a great introduction, thank you very much. Now that I understand the flow, I'm going to explore other options available

Nothing better than that to start with Knime. Excellent

Thanks! This is a good training for a beginner like me! I even learned other thing beside the main topic of using decision tree such as coloring the table and exporting result to reporting environment

Excellent! Thank you for this explanation!

Very well articulated and concise trainig. Thank You

Pre-requisites before the start of the video can be added to help viewer know their level of understanding. Well explained Thank you.

Спасибо, очень полезное видео. Только благодаря вам что-то начинает доходить. Thank you

Great video, very helpful and well explained, thank you!

Great tutorial! Very clear, many thanks!

Thank you for showing deployment process too. 🙏🏻

This is fantastic!!!

Excellent - and so well articulated, thank you!

excellent video... clearly articulated. Thanks so much

Thank you for very good demo.

best explanation what i ever seen

Thank you very much for this excellent tutorial

Precice and simple explanation 👌 Thanks for your efforts in envisioning 👍🙏

Very much informative and thank you for sharing.

Amazing video! Thank you a lot!

Excellent tutorial.

Great tutorial!!

Nice demo!

Great video thanks!!!

Thank you, it's very helpful for me.

Excellent demonstration madam. Thank you from india 👍. More examples in knime please

Amazing

Very useful. I need to apply the same, BUT with time series. I want to predict if a client is going to churn next month, based on its previous monthly historic data. I think the solutions should be similar but involving time series. Not sure exactly on how to combine both. Could you provide some guidance? Thanks.

what's the use of data scientists if we have knime?

what is the extension where you can get the reporting environment ?

great

Very well taught. Thanks for this video. Perhaps the first Knime video I understood properly. Could u plz make one for either neural network or pattern recognition?

I haven't started using KNIME yet, but have watched a few tutorials. I assume the output is a reporting dashboard? It would be nice to see what the output looks like.

👏🏼

nice

JPMML continuously showed errors in my workflow, idk why it wouldn’t work on my dataset :(

is each column in the combined dataset (produced in node 3 - joiner) a variable that this model is using? how you do specify which ones are dependent and independent?

What is the extension where you can get the reporting environment?

Wonderful presentation . I would like to improve performance by testing correlations, functionally transforming some of the predictors (attributes), and eliminating a couple based on their importance. I am new to KNIME. Can someone point me to a video or paper on how to perform these steps pre- or post the Joiner Node step? These techniques would of course carry over to other models.Thanks.

Wonderful presentation! I have a question. All examples that I have seen uses variables in columns. What would be the right approach in a retail scenario where there are more than 10K products(SKU)? You cannot have 10K columns. Ideally, these product purchase transactions should be 2 or 3 variables ( name , quantity, unit_price). Lets assume in this scenario that the product plays a role in the churn behavior. Would a 3 column(variable) approach be suitable in this scenario?

Excellent tutorial! I would like to know how to add nodes because I haven't JPMML in my version of KNIME . Thank you. (Sorry for my English!)

Very useful! Is there a possibility to identify the factors that influenced an individuals predicted churn-probability?

Where is the speaker from ?

I'm very excited with KNIME. In my case, the churn can occur monthly, is is possivel to have KNIME looking in the TIME-evolution of the last 6 months? Perhaps via Recurrent Neural Networks (RNN) / LSTM? Thanks a lot, KNIME Education!

Thanks for sharing the video. It very well articulates about how to build a simple model and I think this model will be used in general by any organization. One question - I am trying to re-create this model for another data set and am unable to see options as per attribute variable. Let me elaborate, for attribute variable, typically the options shown are 1(and we choose a color) and 0(and we choose another color). I am unable to see any option for me to select. Can you let me know where I am going wrong. Well, the coloring apart, I was able to build the model, which is good part, but as you said in the video....coloring, why not? :-)

where we can find the resources of this Videio?

Great Video ) thanks for that , but i have one important question . How can we use the model ( build diagram ) to get answer about churn to each number from the input file. I would like to get answer like this : number | Chur ( prediction) | churn probality 123454 1 90% 234556 1 0% 434444 1 5% 232323 1 95% and so on ....

Where is the dataset?

I could not find JPMML predictor in the current version of KNIME, so what is the alternative to it. Because now, the probability of this instance becoming a churn in my workflow is zero "0".

Hi it's so helpful and the knime is awesome and very easy to use A question: you have used all the other independent variables to predict Churn but what if there is correlation exists among independent variables or between dependent and independent variables? can you please share the same dataset used inthe video to practise?

Is this dataset available for download? Thanks.

sembra ala voce di un italiana, dalla cadenza, dico bene ?grazie cmq del prezioso video, buon lavoro

I love u and love your accent more. Produce video on clustering. Espanol have Glasshour figure.

Why learn Python, R, ... if KNIME does this?

she has Melania Trump's accent