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It depends. If you are using this data on a linear model it is better to remove correlated features. But some non-linear complex model can use or eliminate these correlated feature automatcially.


Yes you have to remove one of them. For example when you plot a heatmap and notice that 2 features A and B have a correlation value of 0.91, remove one of them as removing both of them will lead to information loss. After removing one of them, again plot a heatmap of the remaining features and you'll notice the correlation values of other features have ...

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