Have you remembered the formula "y = ax + b"? This is called Linear Regression Model.

Linear regression is a way to find a rule that helps us predict something based on another thing. It draws a straight line to show how one thing changes when another thing changes. 

You want to predict the price of a house based on its size (square footage). Here’s how the linear regression model would work:

The price of house = a*Square_Feet + b.

Let's say from the data, we can see that for every additional 100 square feet, the price increases by $50,000. So the slope (a) is $50 per square foot.

The y-intercept (b), which represents the base price of a house with no square footage, is $100,000.

The model: y=50x+100,000

This means: If the house is 500 sq. ft., the price is 50×500+100,000=150,000.

Pros and Cons of Linear Regression Model in AI Applications

- Linear regression is a great model when the relationship between input and output is simple and linear,   with minimal noise or outliers.

- It’s useful for predictive analysis, provides interpretable results, and is computationally efficient.

- However, it’s limited in handling non-linear relationships, complex interactions, and large amounts of noise or outliers.

- In more complex scenarios, other models like decision trees, support vector machines, or neural networks may be more suitable.