## 4 Exploratory Analysis
### Histograms  
This type of graphic provides information about the representation of the data distribution, allowing the pattern, shape, and characteristics of the data to be observed. Moreover, the Gaussian distribution, represented by the curve superimposed on the histogram and  
Figure 1: Pairs panel success dataset. Figure 2: Pairs panel failure dataset. also known as the normal distribution, is a continuous probability distribution that reveals the underlying characteristics of the data. The shape of the Gaussian curves allowed the data distribution to be determined. If the curve is bell-shaped, the data are evenly distributed around the mean, with an equal number of values on both sides as determined by the mean (\(\mu\)) and standard deviation (\(\sigma\)) simultaneously. It may also exhibit bimodal forms or skewness in both directions. When the distribution has a longer tail on one side, the skewness indicates a greater frequency of values in the direction of the trend. Positive skewness indicates that the tail reaches higher values, whereas negative skewness indicates that the tail reaches instead lower values. On the other hand, bimodal distributions have two distinct peaks, indicating the existence of two separate groups or phenomena. Referring to the spread of the histogram, the standard deviation, which represents the spread of the distribution, describes the range of values covered by the histogram. The greater the spread, the greater the range of values, indicating that the data tend to be more dispersed. In contrast, a narrower spread indicates a more concentrated distribution, with values clustered closely around the mean. The standard deviation quantifies the dispersion or variability of the data [43; 44]. Each dataset parameter shows some similarity in their respective data behavior. The following parameters described the same data behavior for success and failure datasets, analyzed by Gaussian distribution and data spread: \(\Delta\)MW shows a bimodal distribution, which refers to two different and separate clusters of data and no spread. The C-logP does not exhibit Gaussian distribution and highly spread bars, which indicates that the data are highly spread with significant variability. Referring to \(\Delta\)HAC, the Gaussian distribution is bimodal and low spread, C-AGC does not exhibit a Gaussian distribution and has widely dispersed data. \(\Delta\)HBA and \(\Delta\)HBD, on the other hand, indicate the same Gaussian distribution for both datasets but different data spreads. \(\Delta\)HBA shows a bimodal distribution and low data spread, where the information cluster is low spread. Regarding \(\Delta\)HBD, the data of this parameter exhibit right negative skewness, which indicates that the data distribution is not symmetric. Most of them are located on the left side of the distribution, with fewer values to the right. \(\Delta\)logP, C-HBD, \(\Delta\)PSA, C-CGC, \(\Delta\)CGC, C-CnGC, \(\Delta\)CnG, C-HGC, and \(\Delta\)AGC exhibit different Gaussian distributions expressed in Table 4, as well as low, high, and medium spread. Low means the data are not spread, medium data are regularly spread, and high means the data have a wide range of values. Lastly, \(\Delta\)HGC neither share a similar Gaussian distribution nor a similar spread.