Linear Regression
*In the context of this project, I created a model which aim was to predict profits of a food company considering of branching out to a new city. This was done based on populations from various cities and the profits of outlets established there. In cases like this one, regression machine learning algorithms are utilised.
A visualisation of the input data can be seen in Figure 1.
We see that there is a linear relationship between the population of cities and the profits that a restaurant make. That’s why I decided to apply the Linear Regression algorithm. The linear fit is illustrated in Figure 2.
The values of cost function J, with respect to the parameter values of theta which are generated by the optimisation algorithm of gradient descent, are presented in Figure 3.
The lowest point of this surface corresponds to theta values producing minimum cost value. This point can also be found by a contour plot of cost function in Figure 4.
Finally, I tested the model by calculating the model’s prediction for two different cities population. As expected, a city with a higher population is going to make a higher profit.
A more detailed description of this project’s implentation in Matlab can be seen in this github repository: Link to Github repository