# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 4 Methodology
### Data Preprocessing
#### 4.1.3 Feature Scaling (Standardization and Normalization as Appropriate)  
Scaling is particularly crucial for algorithms that rely on distance metrics (e.g., k-NN, SVM) or gradient-based optimization (e.g., logistic regression, neural networks). In  
Figure 11: Comparison of ‘Mileage per Day Distributions Before (**Left** Plot) and After (**Right** Plot) Outlier Capping. Figure 12: Comparison of ‘Lagged Mileage from Last Service Visit’ Distributions Before (**Left** Plot) and After (**Right** Plot) Outlier Capping. contrast, tree-based ensemble models such as Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost) are inherently invariant to feature scaling, as they rely on threshold-based splitting rather than distance or magnitude comparisons. Although the primary models employed in this study--Decision Tree (DT), Random Forest (RF), LightGBM, and XGBoost--are tree-based and theoretically invariant to feature scaling due to their reliance on threshold-based splitting, feature scaling was still applied for the following practical and methodological reasons:  
\[ \begin{split}\bigcirc&\quad\text{{Cross-Model Compatibility and Reusability}}\\ &\quad\text{During exploratory phases and benchmarking, scaling-sensitive models such as logistic regression and Support Vector Machines were also considered. Applying consistent preprocessing ensures compatibility across different modeling scenarios without requiring duplicate pipelines. \\ \bigcirc&\quad\text{{Visualization and Interpretability}}\\ &\quad\text{Scaled features provide better interpretability and comparability when visualizing feature distributions, detecting anomalies, or interpreting statistical patterns.}\\ \bigcirc&\quad\text{{Future-Proofing and Pipeline Generalization}}\\ &\quad\text{A standardized feature space is essential when developing reusable pipelines, deploying models in production, or integrating with hybrid systems (e.g., deep learning layers or clustering modules) that may require scaled inputs. \\ \bigcirc&\quad\text{{Model Ensemble Readiness:}}\\ \end{split} \]  
Even when ensemble methods are composed of tree-based learners, they can be integrated into meta-modeling (stacking, blending) setups that include scale-sensitive components. Feature scaling supports seamless integration in such architectures. Depending on the distributional characteristics and modeling requirements of each feature, two primary scaling strategies were used:  
* Z-score standardization (also known as standard scaling) was applied to variables that were approximately normally distributed or had symmetrical behavior. This transformation centers the data around zero with a standard deviation of one. * Min-Max normalization was preferred for features that required bounded scaling in the [0,1] range, especially when used in models sensitive to absolute input magnitudes. The choice between these two methods was based on each variable's distribution, statistical properties (mean, median, standard deviation), and the expected influence on model convergence and performance. One illustrative example of this process involves the Car Age (Years) feature. The left plot of Figure 13 shows its original distribution, with a mean of 2.86, standard deviation of 2.11, and median of 2.19, indicating a slightly right-skewed pattern. Figure 13: Effect of feature scaling on ‘Car Age (Years)’. In the middle plot of Figure 13, Z-score standardization is applied, transforming the values to have zero mean and unit variance. Although the core models used in this study--such as XGBoost, LightGBM, Random Forest, and Decision Tree--do not require feature scaling due to their split-based architecture, scaling was nonetheless explored to enhance model interpretability, facilitate potential downstream tasks, and prepare the data for possible hybrid model pipelines involving scaling-sensitive components. The right plot of Figure 13 demonstrates Min-Max normalization, which bounds the values within the [0,1] range.