How to perform feature selection (i.e. pick important variables) using Boruta Package in R ?

Variable selection is an important aspect of model building which every analyst must learn. After all, it helps in building predictive models free from correlated variables, biases and unwanted noise. A lot of novice analysts assume that keeping all (or more) variables will result in the best model as you are not losing any information. Sadly, that is not true! How many times has it happened that removing a variable from model has increased your model accuracy?