After preliminary diagnostics, exploration and cleaning I am going to start with a multiple linear regression model.

The variables/features I am using for the models are: Engine displacement (size), number of cylinders, transmission type, number of gears, air inspired method, regenerative braking type, battery capacity Ah, drivetrain, fuel type, cylinder deactivate, and variable valve.

There are 1253 vehicles in the dataset (does not include pure electric vehicles) summarized below.

```
fuel_economy_combined eng_disp num_cyl transmission
Min. :11.00 Min. :1.000 Min. : 3.000 A :301
1st Qu.:19.00 1st Qu.:2.000 1st Qu.: 4.000 AM : 46
Median :23.00 Median :3.000 Median : 6.000 AMS: 87
Mean :23.32 Mean :3.063 Mean : 5.533 CVT: 50
3rd Qu.:26.00 3rd Qu.:3.600 3rd Qu.: 6.000 M :148
Max. :58.00 Max. :8.000 Max. :16.000 SA :555
SCV: 66
num_gears air_aspired_method
Min. : 1.000 Naturally Aspirated :523
1st Qu.: 6.000 Other : 5
Median : 7.000 Supercharged : 55
Mean : 7.111 Turbocharged :663
3rd Qu.: 8.000 Turbocharged+Supercharged: 7
Max. :10.000
regen_brake batt_capacity_ah
No :1194 Min. : 0.0000
Electrical Regen Brake: 57 1st Qu.: 0.0000
Hydraulic Regen Brake : 2 Median : 0.0000
Mean : 0.3618
3rd Qu.: 0.0000
Max. :20.0000
drive cyl_deactivate
2-Wheel Drive, Front :345 Y: 172
2-Wheel Drive, Rear :345 N:1081
4-Wheel Drive :174
All Wheel Drive :349
Part-time 4-Wheel Drive: 40
fuel_type
Diesel, ultra low sulfur (15 ppm, maximum): 28
Gasoline (Mid Grade Unleaded Recommended) : 16
Gasoline (Premium Unleaded Recommended) :298
Gasoline (Premium Unleaded Required) :320
Gasoline (Regular Unleaded Recommended) :591
variable_valve
N: 38
Y:1215
```

```
Call:
lm(formula = fuel_economy_combined ~ eng_disp + transmission +
num_gears + air_aspired_method + regen_brake + batt_capacity_ah +
drive + fuel_type + cyl_deactivate + variable_valve, data = cars_19)
Residuals:
Min 1Q Median 3Q Max
-12.7880 -1.6012 0.1102 1.6116 17.3181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.05642 0.82585 43.660 < 2e-16 ***
eng_disp -2.79257 0.08579 -32.550 < 2e-16 ***
transmissionAM 2.74053 0.44727 6.127 1.20e-09 ***
transmissionAMS 0.73943 0.34554 2.140 0.032560 *
transmissionCVT 6.83932 0.62652 10.916 < 2e-16 ***
transmissionM 1.08359 0.31706 3.418 0.000652 ***
transmissionSA 0.63231 0.22435 2.818 0.004903 **
transmissionSCV 2.73768 0.40176 6.814 1.48e-11 ***
num_gears 0.21496 0.07389 2.909 0.003691 **
air_aspired_methodOther -2.70781 1.99491 -1.357 0.174916
air_aspired_methodSupercharged -1.62171 0.42210 -3.842 0.000128 ***
air_aspired_methodTurbocharged -1.79047 0.22084 -8.107 1.24e-15 ***
air_aspired_methodTurbocharged+Supercharged -1.68028 1.04031 -1.615 0.106532
regen_brakeElectrical Regen Brake 12.59523 0.90030 13.990 < 2e-16 ***
regen_brakeHydraulic Regen Brake 6.69040 1.94379 3.442 0.000597 ***
batt_capacity_ah -0.47689 0.11838 -4.028 5.96e-05 ***
drive2-Wheel Drive, Rear -2.54806 0.24756 -10.293 < 2e-16 ***
drive4-Wheel Drive -3.14862 0.29649 -10.620 < 2e-16 ***
driveAll Wheel Drive -3.12875 0.22300 -14.030 < 2e-16 ***
drivePart-time 4-Wheel Drive -3.94765 0.46909 -8.415 < 2e-16 ***
fuel_typeGasoline (Mid Grade Unleaded Recommended) -5.54594 0.97450 -5.691 1.58e-08 ***
fuel_typeGasoline (Premium Unleaded Recommended) -5.44412 0.70009 -7.776 1.57e-14 ***
fuel_typeGasoline (Premium Unleaded Required) -6.01955 0.70542 -8.533 < 2e-16 ***
fuel_typeGasoline (Regular Unleaded Recommended) -6.43743 0.68767 -9.361 < 2e-16 ***
cyl_deactivateY 0.52100 0.27109 1.922 0.054851 .
variable_valveY 2.00533 0.59508 3.370 0.000775 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
standard error: 2.608 on 1227 degrees of freedom
Multiple R-squared: 0.8104, Adjusted R-squared: 0.8066
F-statistic: 209.8 on 25 and 1227 DF, p-value: < 2.2e-16
```

The fitted MSE is 6.8 and predicted MSE of 6.83. Some of the below residuals are too large. The extreme large residual is a Hyundai Ioniq which none of the models predict very well as it is unique vehicle (versus the other data points).

Let's try a decision tree regression model.

```
#regression tree full
m_reg_tree_full <- rpart(formula = fuel_economy_combined ~ .,
data = train,
method = "anova",)
#regression tree tuned
m_reg_tree_trimmed <- rpart(
formula = fuel_economy_combined ~ .,
data = train,
method = "anova",
control = list(minsplit = 10, cp = .0005)
)
#rpart.plot(m_reg_tree_full)
plotcp(m_reg_tree_full)
pred_decision_tree_full <- predict(m_reg_tree_full, newdata = test)
mse_tree_full <- RMSE(pred = pred_decision_tree_full, obs = test$fuel_economy_combined) ^2
pred_decision_tree_trimmed <- predict(m_reg_tree_trimmed, newdata = test)
mse_tree_trimmed <- RMSE(pred = pred_decision_tree_trimmed, obs = test$fuel_economy_combined) ^2
plotcp(m_reg_tree_trimmed)
```

After tuning the decision tree the predicted MSE is 6.20 which is better than the regression model.

Finally let's try a random forest model. The random forest should produce the best model as it will attempt to remove some of the correlation within the decision tree structure.

```
#random forest
m_random_forest_full <-randomForest(formula = fuel_economy_combined ~ ., data = train)
predict_random_forest_full <- predict(m_random_forest_full, newdata = test)
mse_random_forest_full <- RMSE(pred = predict_random_forest_full, obs = test$fuel_economy_combined) ^ 2
which.min(m_random_forest_full$mse)
#random forest tuned
m_random_forest <- randomForest(formula = fuel_economy_combined ~ ., data = train, ntree = 250)
plot(m_random_forest)
predict_random_forest <- predict(m_random_forest, newdata = test)
mse_random_forest <- RMSE(pred = predict_random_forest, obs = test$fuel_economy_combined) ^ 2
plot(tmp$test.fuel_economy_combined - tmp$r.predict_random_forrest., ylab = "residuals",main = "Random Forest")
varImpPlot(m_random_forest)
```

The error stabilizes at 250 trees. randomForest() by default uses 500 trees which is unnecessary.

The random forest also has an r-squared of .9

Engine size, number of cylinders, and transmission type are the largest contributors to accuracy.

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