Programming in C. See attached images please. Thank you.

This assignment is designed to provide you some experience writing programs with the C programming language and to get a glimpse of how machine learning works. There is significant hype and excitement around artificial intelligence (AI) and machine learning (ML). For this assignment, you will write a C program that implements a simple machine learning algorithm for predicting house prices based on historical data The price of the house (y) can depend on various attributes: number of bedrooms (x), total size of the house (x2 ), number of baths (x3 ), and the year the house was built (x4). If these are the only variables that affect price, then the price of the house can be computed by the following equation Given a house, we might know the attributes of the house (i e., x1, x2, x3 , x4 ), however, we don't know the weights for these attributes: w0, wl, w2, w3 and w4. For instance, having 2 baths may be more important to some people than others, so the weight of that factor can change based on the value people attach to it The goal of our machine learning algorithm is to learn the weights for the attributes of the house from lots of training data. Training data is data that is known (or presumed) to be fully diagnostic and representative of what we want to test that is used to 'teach' the machine algorithm. The algorithm will then take what it has 'learned' from the training data and later apply it to testing data, data where some of the factors and variables are not known, and will estimate the most likely values for those factors and variables Presume we have N examples in a training data set that provide the values of all attributes for N houses and the price. Let's say there are K attributes. We can represent the attributes from all the examples in the training data as a N x (K + 1) matrix as follows, which we call X x0,0 x0,1 x0,2 x0,3 x1,0 x1,1 x1,3 x1,3 x2,0 x2,1 x2,2 x2,3 x3,0 x3,1 x3,2 x3,3 xn,0 xn,1 xn,2 xn,3 where n is N 1. We can represent the prices of the house from the examples in the training data as a Nxl matrix, which we call Y yl yn This assignment is designed to provide you some experience writing programs with the C programming language and to get a glimpse of how machine learning works. There is significant hype and excitement around artificial intelligence (AI) and machine learning (ML). For this assignment, you will write a C program that implements a simple machine learning algorithm for predicting house prices based on historical data The price of the house (y) can depend on various attributes: number of bedrooms (x), total size of the house (x2 ), number of baths (x3 ), and the year the house was built (x4). If these are the only variables that affect price, then the price of the house can be computed by the following equation Given a house, we might know the attributes of the house (i e., x1, x2, x3 , x4 ), however, we don't know the weights for these attributes: w0, wl, w2, w3 and w4. For instance, having 2 baths may be more important to some people than others, so the weight of that factor can change based on the value people attach to it The goal of our machine learning algorithm is to learn the weights for the attributes of the house from lots of training data. Training data is data that is known (or presumed) to be fully diagnostic and representative of what we want to test that is used to 'teach' the machine algorithm. The algorithm will then take what it has 'learned' from the training data and later apply it to testing data, data where some of the factors and variables are not known, and will estimate the most likely values for those factors and variables Presume we have N examples in a training data set that provide the values of all attributes for N houses and the price. Let's say there are K attributes. We can represent the attributes from all the examples in the training data as a N x (K + 1) matrix as follows, which we call X x0,0 x0,1 x0,2 x0,3 x1,0 x1,1 x1,3 x1,3 x2,0 x2,1 x2,2 x2,3 x3,0 x3,1 x3,2 x3,3 xn,0 xn,1 xn,2 xn,3 where n is N 1. We can represent the prices of the house from the examples in the training data as a Nxl matrix, which we call Y yl yn