I continue working with machine learning algorithms. In a previous post I talked about linear regression with one variable and I described different algorithms to predict hypothesis.

In this case, I'm playing with linear regression but, with some features. Linear regression only have one input feature and one output feature. For example, you can predict the price of a house give the house's size. But imagine that you want predict the price of a house using size and rooms features. When you have more than one input feature is called 'multi-variable linear regression'.

In the following figure we can see the two input features (size and rooms), the training data (red dots), and the predictions (blue dots). In this case, we can represent the information with a 3D model. If your model have more than three features you must research the way to represent all the data.

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