Linear regression

Linear regression fits a straight line: prediction = weight × feature + bias.

• The weight (slope) says how much the prediction changes per unit of the feature.
• The bias (intercept) is the prediction when the feature is 0.
• Training picks the line with the smallest squared errors.

w, b = 5.0, 40.0          # marks = 5 × hours + 40
hours = 6
print(w * hours + b)

70.0

Remember: One feature: y = m·x + c. More features: one weight each.

Common mistake: Trusting the line far outside the data you trained on.