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

```python 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