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I'm working on a machine learning problem where I need to guess which customers will churn and which of them will continue to be customers.

I have $X_0, X_1, X_2, X_3, X_4, X_5$ and $X_6$ attributes representing if they have credit cards, if they are active customers, if they have money in their accounts, etc. So, according to these multiple $X$ values and the target value $Y$, which is either $0$ or $1$, I need to develop a model that can do the prediction.

I have always worked with only one $X$ attribute and one target value $Y$. Right now, I'm confused about how I should work with multiple $X_n$ values.

Any help is appreciated.

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    $\begingroup$ What have you tried so far? How does your model work right now? For a neural network, all you have to do is increase the number of input neurons. $\endgroup$
    – S2673
    May 11 at 10:29
  • $\begingroup$ Thank you @S2673, but since I didn't know how to use multiple X values I was doing a research on how to do it. And I haven't tried neural networks. $\endgroup$ May 11 at 10:51
  • $\begingroup$ So how does your model work right now? $\endgroup$
    – S2673
    May 11 at 11:06
  • $\begingroup$ I don't have a model because I don't get the idea of how to use multiple input attributes and obtain one target attribute. If I can understand what technique I should use theoretically I can build a model. $\endgroup$ May 11 at 11:26
  • $\begingroup$ You should probably be using a neural network. There are a bunch of places online where you can learn about it. How did your model using one X and one Y work? $\endgroup$
    – S2673
    May 11 at 12:10

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