The following dataset is given, and you are asked to classify data point (15,C) using variants of kNN classifier. The dataset contains continuous and categorical attributes and a binary class label. 1. [5 points] Normalize the X1 attribute and rewrite the table of the dataset using the normalized attribute X1. The goal of normalization is to bring the attribute to N(0,1), normal distribution of mean 0 and standard deviation 1. Show your work. Note: After normalizing, when we refer to X1, we are referring to the normalized attribute, not the original attribute. Note: You can round the numbers to one decimal point for simplification. 2. [10 points] When we have a mix of categorical and continuous attributes, we have to define a new distance metric that works for all attributes. Here, we defined our distance as the following: dij(xi,xj)=(x1ix1j)2+1[x2i=x2j], where xi is i th data point and 1[.]isanindicatorfunctionthatreturnsoneiftheargumentistrue and zero otherwise. Find the class label of the unseen point (15,C) using 1-NN and 3-NN. Note: when you calculate the distance you need to normalize X1 of the unseen point using the mean and standard deviation that you find in