Linear Regression Explained in Simple Terms!
Published on March 1, 2025
Linear regression is one of the simplest yet powerful algorithms in machine learning. It is used to predict a value based on the relationship between independent and dependent variables.
How Does Linear Regression Work?
Linear regression finds the best-fitting line that represents the relationship between the input (independent variable) and the output (dependent variable). The equation for this line is:
y = mx + b
where:
- y is the predicted output (e.g., house price)
- m is the slope of the line (coefficient)
- x is the input value (e.g., square feet of the house)
- b is the intercept (constant)
Example: Predicting Home Prices
Suppose we have a dataset of house prices based on square footage:
| Square Feet | Price ($1000s) |
|---|---|
| 750 | 150 |
| 800 | 160 |
| 850 | 170 |
| 900 | 180 |
| 950 | 190 |
By applying linear regression, we can find the equation:
Price = 200x + 50
Using this equation, if we want to predict the price of a 1,000 square foot house:
Price = 200(1) + 50 = 250
So, a 1,000 sq. ft house is estimated to cost $250,000.
Tip: Linear regression is widely used in economics, healthcare, and real estate for making predictions based on past data.
