Question 2 (30 pts): Suppose we have the following Rent House dataset (5 attributes and 4 houses): 1 House ID Number of rooms Distance from the city center in meter house age Price Deposit (x1) (x2) (x3) (y1) (y) 3 5 300K 15 2 4 150 10 350K 20 3 2 400 7 150K 65 4 2 450 9 180K 12 100 We want to create a neural network model for predicting both the house price and its deposit in terms of the number of rooms, the distance from the city center and the house Answer the following questions: age. a) What is the kind of this problem, is it a classification problem or it is a regression problem? b) How many input units that are used in the Multilayer neural network for solving this problem? c) How many output units that are used in the Multilayer neural network for solving this problem? d) Write a complete and suitable MATLAB code for creating and training Multilayer neural network that contains 3 hidden layers. Such that: the number of neurons in the first hidden layer is 8, the number of neurons in the second hidden layer is 10, the number of neurons in the third hidden layer is 15, the training data ratio is 80% the error threshold is 0.01 the learning rate is 0.2 Question 2 (30 pts): Suppose we have the following Rent House dataset (5 attributes and 4 houses): 1 House ID Number of rooms Distance from the city center in meter house age Price Deposit (x1) (x2) (x3) (y1) (y) 3 5 300K 15 2 4 150 10 350K 20 3 2 400 7 150K 65 4 2 450 9 180K 12 100 We want to create a neural network model for predicting both the house price and its deposit in terms of the number of rooms, the distance from the city center and the house Answer the following questions: age. a) What is the kind of this problem, is it a classification problem or it is a regression problem? b) How many input units that are used in the Multilayer neural network for solving this problem? c) How many output units that are used in the Multilayer neural network for solving this problem? d) Write a complete and suitable MATLAB code for creating and training Multilayer neural network that contains 3 hidden layers. Such that: the number of neurons in the first hidden layer is 8, the number of neurons in the second hidden layer is 10, the number of neurons in the third hidden layer is 15, the training data ratio is 80% the error threshold is 0.01 the learning rate is 0.2