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I am working on KNIME ANALYTIC PLATFORM as part of my project. I am new to this analytics platform.

Prediction Analysis is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. ... Knime is based on the Eclipse platform and provides a visual programming language based on data-flows to create an easy-to-understand analysis process quickly

My Approach

With an existing data I was trying to form a pattern. Say like ..

There are several customers with pending amount to be paid and few of them paid. My case was they might exist 1 or more number of orders from customers,
Say customer 1,2 and 3 are there. Cust_1 has 3 orders and Cust_2 has 2 orders and Cust_3 had 1 order, with there some orders amount paid and some not paid.

My Question

My question is can we generate a pattern, based on customers.
To know the customers order more than 2 with coloured and arrange them into pattern? What nodes in knime make my pattern?

can anyone please solve this question.

Uma Ramya
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  • This is quite board question, but you should check the association rule learner nodes. An example is demonstrated here: https://www.knime.com/blog/market-basket-analysis-and-recommendation-engines – Gábor Bakos Sep 22 '17 at 17:32
  • Thank you @Gabor Bakos ! But my question is what sought of patterns can be generated using this KNIME. what are those patterns? What is pattern in knime? Can you make it more clear please. – Uma Ramya Sep 23 '17 at 04:32

1 Answers1

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The patterns in this case what customers buy together, which are expressed as association rules. These rules can applied to new data and can be help predicting new buys by suggesting those products when one of them is in the basket.

In case more information is available on the customers, that can be used to cluster them together based on those properties (which case the patterns are the similarity of the customers) and if a new customer fits in one of those clusters, the most common product(s) can be suggested to her/him/it. The nice thing is that KNIME makes this very easy once you have your data and you get familiar with KNIME (which is itself user friendly, there are many free sources available: https://www.knime.com/resources).

Obviously other patterns might be also useful. If you have more data, you might see trends (patterns) in buys of individual customer orders (or the amount of the orders, where the ARIMA nodes might be useful) or in the popularity of different products. These can also be called patterns.

For complex models, you might need to use other tools too, like R or Python or something else. I should emphasize that KNIME has very good PMML support, so you are not tied to a single tool, you can create/train your model in KNIME and use some other tool to make predictions based on that model or the other way around.

Gábor Bakos
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