## 4 Exploratory Data Analysis  
EDA is a critical step in the data analysis process that involves examining datasets to uncover underlying patterns, detect anomalies, and identify relationships between variables [36]. Utilizing statistical techniques and hypothesis testing, EDA provides a valuable means of discovering hidden insights within the data. The process typically includes several key stages: defining the research question, preparing the data, conducting analysis, and presenting results. By performing EDA, analysts gain a comprehensive understanding of the dataset's characteristics, enabling them to identify outliers, spot anomalies, and recognize potential trends or associations among variables. EDA lays the groundwork for more advanced statistical analysis and facilitates data-driven decision-making. The central aim of EDA is to offer a systematic and unbiased approach to understanding the dataset eschewing assumptions and generating insights that are fundamental to rigorous data interpretation [37]. For this study, the dataset was sourced from UCL repositories and made available via ([PERSON] 2022). It includes data collected from a group of 101 individuals diagnosed with COPD, all of whom were invited to participate in a rehabilitation program. The primary objective of the program was to support participants in managing and maintaining control over their COPD condition. The dataset comprises 24 variables encompassing a wide range of information, including patient demographics, disease status, lung function, mental health, and quality of life indicators. Table 1 summarizes these variables. Continuous variables include age, lung function metrics such as FEV1 and FVC, and physical performance indicators such as MWT1 and MWT2. Categorical variables include gender and COPD severity stage (COPD-SEVERITY). Furthermore, the dataset captures mental health status using the Hospital Anxiety and Depression (HAD) scale, which assesses levels of anxiety and depression. The following is a summary of the dataset's measures and features, as revealed through descriptive analysis. To illustrate the distribution of numerical variables, density plots were employed. These density plots facilitate the identification of skewness, modality, and potential outliers in the dataset, providing a foundational understanding necessary for subsequent modeling and hypothesis testing. Density charts were generated for the following key continuous variables, as outlined in Table 1:  
* Age;
* FEV1;
* FVC;
* Best MWT;
* Pack history (smoking history in pack years);
* SGRQ. Table 2 presents the summary statistics and density plot for the age variable, offering a comprehensive overview of its distribution. The data demonstrated 100% validity with no missing values, ensuring a reliable basis for analysis. The density plot displayed in Figure 1 indicates a distribution that approximates normality, although it is slightly right-skewed, with a greater concentration of values toward the higher end of the age spectrum. The mean age of participants was 70.1 years, with a standard deviation (SD) of 7.86, suggesting moderate dispersion around the mean. The minimum and maximum age values were 44 and 88, respectively. The 25 th percentile (Q1) was 65, the median (Q2) was 71, and the 75 th percentile (Q3) was 75. These percentile values suggest that a majority of patients fall within the 65-75 age range, highlighting a central cluster in the dataset and a relatively smaller proportion of patients younger than 65 or older than 75.