## 2 Methodology
### Exploratory Data Analysis (EDA)  
The EDA is used for several reasons, with the following being the most prominent: Exploratory Data Analysis (EDA) is a fundamental step within our methodology that provides critical insights into the dataset's structure, patterns, and anomalies, which are essential for informed feature engineering and model selection [30]. During the EDA phase, we start by concatenating datasets from different vehicle models to create a unified data frame. This is immediately followed by a series of operations aimed at understanding the data's composition to obtain statistical summaries of the features:  
* We pay special attention to missing values across the dataset, as their presence can significantly skew the performance of predictive models. We also identify missing values, duplicates, and inconsistencies in the dataset. This step is crucial for data cleaning and preprocessing before applying machine learning algorithms or statistical modeling. Histograms and boxplots are then meticulously generated for each numerical feature to visualize the data distribution and identify outliers. Histograms give a sense of the data's skewness and kurtosis, while boxplots help spot outliers by depicting the interquartile range. We have dealt with the three attributes (manifold air pressure, engine coolant temperature, and intake air temperature) to depict the data distribution and identify outliers in Figures 3-5. The blue line in these figures is a kernel density estimate (KDE), which provides a smooth, continuous curve representing the distribution of the dataset. This line is crucial for identifying the overall trend and shape of the data distribution beyond the discrete bars of the histogram. In Figure 3, depicting the Engine Coolant Temperature, the KDE line clearly shows a strong peak around 80 degrees, suggesting a common operational temperature range for the majority of vehicles measured. In Figure 4, which illustrates the Manifold Absolute Pressure, the KDE line peaks sharply around 100, indicating that most vehicle engines operate within a relatively narrow pressure range during normal conditions. Lastly, in Figure 5, showing the Intake Air Temperature, the KDE line highlights a dominant peak around 10 to 20 degrees, which likely reflects typical external air temperatures encountered by the vehicles. These plots are vital for recognizing features that may require normalization or transformation before being fed into machine learning models. The EDA also extends to categorical features, where count plots provide a visual representation of the distribution of categorical data.  
Figure 4: Distribution of manifold absolute pressure.  
Figure 3: Distribution of engine coolant temperature.  
* Identify relevant features or variables that have a significant impact on the target variable. It also helps in creating new features by transforming or combining existing ones to improve model performance. This aids in understanding the balance of classes within the dataset, which is important when choosing models and evaluating their performance. Furthermore, we delve into the relationships between features by employing boxplots to compare the distribution of numerical features across different categories. This not only aids in feature selection, but also in tailoring preprocessing strategies to the specific characteristics of the data.
* The EDA results serve as a foundation for communicating findings to stakeholders. Visualizations and summary statistics make complex data more understandable and facilitate decision-making processes. In essence, EDA serves as the guiding light for our preprocessing decisions, model choices, and, ultimately, the interpretability of the model outcomes. Via rigorous analysis and visual exploration, we lay the groundwork for building robust predictive models that are both accurate and understandable.