Machine Learning for Data Mining by Jesus Salcedo

Author:Jesus Salcedo
Language: eng
Format: epub
Tags: COM021030 - COMPUTERS / Databases / Data Mining, COM018000 - COMPUTERS / Data Processing, COM037000 - COMPUTERS / Machine Theory
Publisher: Packt Publishing
Published: 2019-04-30T21:34:34+00:00

As you can see, we have the same number of cases for each of these variables. The most important predictor ended up being the TVs variable, as we can see in the following screenshot, and that's the one that's able to differentiate these groups the most.

For example look at the TVs field, we can see that the current customers are the people who we predicted to churn and we thought we're going to lose these customer, on average they bought 2.4 TVs. This prediction is significantly more than the people who were predicted to be going to stay as current customers which bought about one TV on average As we can see, we had a statistically significant result. The opposite is true when it comes to Stereos. You can see that we have a statistically significant result, however you can see that the people that we'd predicted to lose as customers on average, bought 13.2 stereos, whereas the people that we'd predicted were going to stay as customers on average bought 16.2 stereos

The last thing we're going to do is look at the relationship between two continuous variables. Earlier, we used the scatter plot to show the relationship between two continuous variables. Here, we're going to quantify that by calculating a correlation:

Using the bank dataset, go to the Output palette in Modeler and connect the Statistics node to source node:

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