How to remove correlated features?

It depends on your goal. Prediction or inference? If you want to make good predictions, you can leave this as it is because correlation does not affect prediction. However, if you are interested in inference then it is a problem and you'll have to address it. Let's say you are building logistic regression model with highly correlated variables. Estimated coefficients will be unstable, have a big variance and thus hard to interpret correctly. You could then used penalized logistic regression with Lasso or Ridge penalty (or a mix of both - elastic net). Lasso performs feature selection while Ridge regression is more stable and can be useful in terms of interpretation.

There is also another way. Because all of your variables are continuous, you can use PCA for dimensionality reduction. The tradeoff here is that you end up with principal components which are just mathematical constructs. Generally I am not an advocate of just throwing away one of the highly correlated variables. You don't know whether only one of them can be related to the outcome. Throwing away variables is not always the good approach to solving collinearity problems.