Forecasting sales per customer for the next 360 days

[mlauber71]

@ScottF provided us with simple workflow that shows how you would set up a regression model and do an evaluation using the RMSE ( Root-mean-square deviation) => the lower the better. The KNIME numeric scorer node has you covered there.

You could switch out the type of model used:

Multiple Linear Regression

Hi @GK1724 - You’ll want to use the Linear Regression Learner node, along with the Regression Predictor node. Here’s a very simple example using the Iris dataset, predicting petal width based on the other available variables. The Learner node is where you find the model coefficients and associated p-values, and the Predictor node contains the predictions for the test set. The Numeric Scorer node outputs some simple summary statistics (R^2, RMSE, etc). [2018-04-09%2009_31_53-KNIME%20Analytics%…

We had a discussion about that here:

Predictive analytics for Sales

I can think of a few examples that might show you the possible directions This site has an introduction to model building with KNIME and uses an example that could be similar to your question: Concerning the predictions you want you will have to think about how to set up the analysis and prediction. Would you make a prediction for every single product and dealer and what role does the time play in your analysis. Sales within a month or a week. Then the quality of a model will very much de…

And also further information about ways to determine the quality of a model (mostly 1/0 but check out the link).

Random Forest Model Not Working

I attached a working example of a Random Forest with H2O.ai nodes and a 0/1 Target from a Kaggle competition (please note the example only uses the first 1.000 lines, you should remove this restriction or use your own data for your own purpouses). In order to get an idea about the quality of you model you might want to read this article: I always advise not just to use a Scorer but rather a metric like Gini/AUC to see the relative quality of the model. In the end the ‘score’ is not a magic…

I have to see if I have an example with a regression target and H2O somewhere. But this Blog already should cover a lot of that.

Further metrics I have explored are Correlation coefficients like Pearson and Spearman to see how your prediction and your real data do align.

Also I have toyed around with the concept of Bland-Altman plot but I am not sure yet if that gives me any more insight. Basically it should combine the question of correlation (if your score hints in the right direction) and agreement (if you actually hit the numbers).

Bland–Altman plot