# How to train a machine learning model with multiple attributes and one target value?

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.

• 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. May 11 at 10:29
• 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. May 11 at 10:51
• So how does your model work right now? May 11 at 11:06
• 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. May 11 at 11:26
• 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? May 11 at 12:10