An MLP is used to classify the training points into $$blue$$ and $$orange$$ classes. Input features $($x$1,$x$2)$ are transformed into y$=$sqrt$($x$1^2+$X$2^2)$

$.$ The MLP accepts y as the input data. What is the minimum number of hidden layers needed to classify the data accurately?What are the different features of Big Data Analytics?