Little Bits of Artificial Intelligence

1. What is Linear Regression:

Linear Regression is like drawing the best straight line through a bunch of points on a graph.
Imagine we’re trying to predict something (like our exam score) based on a related factor (like hours we studied). If we plot those values, they may not form a perfect line — but they may form a pattern. Linear regression finds the best-fitting straight line that explains the relationship between them.
This line helps us make predictions: “If I studied 6 hours, what score might I expect?”

2. Formula:

The equation for the line is: y = mx+c

Where:

• y = predicted value
• x = input value (independent variable)
• m = slope (how much y changes when x increases)
• c = intercept (where the line crosses the y-axis)

3. Real-World Examples:

A. House Price Prediction

• Goal: Predict the price of a house based on its size (square feet).
• Use: Real estate companies use linear regression to set prices for new listings.

Price=m×Size+c

B. Salary Estimation

• Goal: Predict someone’s salary based on their years of experience.
• Use: HR departments use this to forecast pay and hiring budgets.

Salary=m×Experience+c

4. What is Multivariate Linear Regression?

We predict a value using two or more input variables:

y=m1⋅x1+m2⋅x2+⋯+mn⋅xn+c

It’s like extending a 2D line into a multi-dimensional plane or surface.

5. Real-Life Example:

Predict House Price based on:

• size in square feet
• number of bedrooms

We’ll try to predict house_price.