Page 296 - Touhpad Ai

Output:
Linear Regression Coefficients:
Feature Coefficient
0 Year 154539.086683
1 Kilometer -1.639439
2 Engine -349.605847
3 Max Power 28687.541309
4 Max Torque 470.296370

Mean Squared Error: 5106706765836.999

R-squared Score: 0.5637750991712115

Let us understand the above output:
Coefficients:
These show how much the car price changes with a unit increase in each feature (while keeping others constant).

Feature Coefficient Interpretation
Year +154,539 Newer cars cost more. Each additional year adds ₹1.5 lakh to price (approx).

Kilometer -1.64 More kilometers reduce price. Every extra km reduces price slightly.
Larger engine size (cc) slightly reduces price — might be due to fuel
Engine -349.61
inefficiency or other market factors.

Max Power +28,687 More powerful engines increase price significantly.
Max Torque +470 Higher torque slightly increases price.

Mean Squared Error (MSE):

5,106,706,765,837 — This is very large because price is measured in rupees, and there may be outliers or
non-linear effects.
R-squared Score (R²):
0.56 — About 56% of the variation in car prices is explained by your model.
This is decent, but not excellent — you can improve it by:

Removing outliers
Trying other models like Random Forest

294 Touchpad Artificial Intelligence - XI