Predicting sales values using some past data (multiple features) by machine learning

[mlauber71]

Here we have a few examples for regression models with KNIME:

Regression models (numeric Target)

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…

Forecasting sales per customer for the next 360 days

@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: We had a discussion about that here: And also further information about ways to determine the quality of a model (mostly 1/0 but check out the link). I have to see if I have an example with a regression targe…

Evaluate a Linear Regression model

Typically you would use the Scorer nodes. Then there are a few entries dealing with numeric targets, you might want to check them. Regression models (numeric Target) predict how many future visitors a restaurant will receive (with H2O.ai)

How to identify the Top 100 features selected from MLP model

I was thinking about feeding the prediction of the model back to a correlation node (linear in this case) and then use the absolute value of the correlation to determine the strongest correlations towards the predicted Target. The data is from a Kaggle competition (https://www.kaggle.com/c/bnp-paribas-cardif-claims-management) with some transformations and only 500 random cases. So the numbers are just there to demonstrate the nodes. The calculation can be quite costly. [31] kn_example_mlp_m…

predict how many future visitors a restaurant will receive (with H2O.ai)

Predict Demand Combining the Power of KNIME & H2O | KNIME