Explained: Linear Regression with real life scenarios in R

Machine learning is one of the most trending topics at present and is expected to grow exponentially over the coming years. Before we drill down to one of the most common techniques in machine learning that is the'linear regression', let's understand what exactly is regression. Regression analysis is a form of a predictive modelling technique that establishes a relationship between two variables namely a dependent variable and independent variable. In simpler words, a regression analysis involves graphing a line over a set of data points that most closely fits the overall shape of the data or it can be said that a regression shows the changes in a dependent variable on the y-axis to the change in the explanatory variable on the x-axis. Linear regression aims to establish a linear relationship between two variables where one is the independent variable and other is the dependent variable using a linear equation on the data that is put under observation. For example if we consider the weight and height of a person, as the height increases the weight also increases, hence a linear relationship can be established between the height and weight of a person.