So far, we have explored the Flights dataset from Seaborn, analyzed trends, and visualized these trends using various charts such as line plots and heat maps. Today, we focus on leveraging this historical data to predict future trends using a technique known as Linear Regression.

Linear regression is a powerful tool used to predict an outcome (dependent variable) based on one or more predictor (independent) variables, forming a linear relationship. For instance, we might wish to forecast future passenger numbers based on past trends using our air travel dataset.

Why is this important? Anticipating future trends is key to strategic decision-making. Airlines, for example, use these predictions for numerous operational and strategic decisions, such as scheduling flights, capacity planning, resource allocation, and strategic expansions. Let's see how we can make such predictions!

Linear regression assumes a linear relationship between the dependent and predictor variable(s), which can be represented as: $y = a + b * x$. Here, is the dependent variable we want to predict, is our predictor variable, is the y-intercept, and is the slope of the line. In the context of our Flights dataset, can represent the number of passengers, and can represent time (years or months).