Linear Regression

Predict with straight lines.

What is Linear Regression?

Finding the best straight line to predict values.

Like predicting house prices based on size!

Simple Example

Data: Houses in Seattle

• 1000 sq ft = $300,000
• 1500 sq ft = $450,000
• 2000 sq ft = $600,000

Pattern: Bigger house = Higher price
Line: Best fit through these points

The Formula

y = mx + b

y = price (what we predict)
x = size (what we know)
m = slope
b = y-intercept

Python Code

from sklearn.linear_model import LinearRegression
import numpy as np

# Data
X = np.array([[1000], [1500], [2000]])  # Size
y = np.array([300000, 450000, 600000])   # Price

# Train model
model = LinearRegression()
model.fit(X, y)

# Predict
new_house = [[1800]]
price = model.predict(new_house)
print(f"Predicted price: ${price[0]:,.0f}")

When to Use

• Predicting numbers
• Clear relationship between variables
• Simple and fast

Limitations

• Only works for linear relationships
• Can't handle complex patterns
• Sensitive to outliers

Remember

• Best for simple predictions
• Fast and easy to understand
• Start here for regression problems