The correct answer is FALSE.
A linear regression algorithm is a statistical method that uses a linear equation to predict the value of a dependent variable based on the values of one or more independent variables. A neural network is a type of machine learning algorithm that can be used to solve a variety of problems, including classification, regression, and clustering.
Linear regression and neural networks are both powerful tools, but they are designed for different purposes. Linear regression is a linear model, which means that it assumes that the relationship between the dependent and independent variables is linear. Neural networks, on the other hand, are non-linear models, which means that they can model relationships that are not linear.
It is possible to design a neural network that can perform linear regression, but it is not necessary to do so. Linear regression is a well-understood and efficient algorithm, and there is no need to use a neural network to solve a linear regression problem.
In fact, using a neural network to solve a linear regression problem can be counterproductive. Neural networks are more complex than linear regression algorithms, and they require more data to train. Additionally, neural networks are more likely to overfit the data, which can lead to poor performance on new data.
Therefore, the correct answer to the question “It is possible to design a Linear regression algorithm using a neural network?” is FALSE.