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I have to run.
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And welcome in this new video.
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In this video, we're going to see how to do some surprise prediction using a linear regression.
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First, we're going to concatenate or extend and use tests to create a new variable in our dataset,
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which is a prediction.
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So.
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We need to give a tipple of aura and the axis in which we want to do the concatenation, which is zero,
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because we want to get by the role.
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So for example, if a prince.
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I printed in that a frame to her really better visualization, so we can see that we have had extreme
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here and each test you now we need to create a new variable prediction in order to frame.
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And in this venerable.
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We put the prediction of each day for the.
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It is not event, but to have a dataframe.
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We know missing value.
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It's better to add the train prediction, also to don't do some interference or issue in our data.
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So as we can see, we have some prediction, and now we just need to print it to see if
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we don't have, for example, only positive value or negative value.
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And if their predictions are correct.
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As we can see, we have some projection around minus two percent and plus two percent.
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So.
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We cannot say already that this prediction are true, but at least this prediction are realistic.
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