Multiple Linear Regression

Same as linear regression, but with MULTIPLE features: `y = w1·x1 + w2·x2 + ... + b`.

Each coefficient tells you how much y changes when that feature increases by 1 (holding others fixed).

Example 1 (python)

from sklearn.linear_model import LinearRegression
# X has multiple columns (features)
m = LinearRegression().fit(X, y)
print(m.coef_)

Output

[2.5, -1.3, 0.7]

One coefficient per feature.

Example 2 (python)

# Check feature importance by absolute coef size

Larger |coef| = more influence.

Key points

💡 Note: Highly correlated features (multicollinearity) make coefficients unstable. Drop or combine them.