Linear regression is one of the simplest algorithms in machine learning. It is used to predict the value of a dependent variable (Y) based on an independent variable (X).

Consider the problem of predicting weight of a person based on height. We know that the 2 are not a simple relationship where we can say for every additional inch height, the weight will increase by 0.5 lbs. When we take a sample set of this data, we will see a cluster of points.

The model derives the best fit line using these points. Using the best fit line, the slope(a) and intercept(b) are derived. For any new given independent variable (X), the dependent variable (Y) is derived using the equation Y = aX + b.

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