## 4 Data Analysis
### Statistical Analysis  
Data was collected and classified using Microsoft Excel of the Microsoft Corporation. The statistical analysis has made use of a statistical package for the Social Sciences SPSS V28 of IBM Corporation. A \(p\)-value of less than 0.05 was considered statistically significant. Descriptive statistics, performance metrics, normality testing, model configuration, and class segmentation were conducted to answer the hypotheses formulated. To apply data mining classification algorithms such as DTs, NB, and k-NN to the hypotheses, the need for correlation analysis among variables, including the event count, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Column** & **Count** & **Mean** & **Std Dev.** & **Min** & **Max** & **Missing Values** \\ \hline Event count & 660 & 14.87 & 30.37 & 1 & 468 & 0 \\ Sessions & 660 & 1.12 & 0.47 & 1 & 9 & 0 \\ Purchase revenue & 660 & 1.00 & 14.63 & 0 & 328.5 & 0 \\ Transactions & 660 & 0.007 & 0.087 & 0 & 1 & 0 \\ Conversions & 660 & 0.007 & 0.087 & 0 & 1 & 0 \\ Bounce rate & 660 & 0.46 & 0.49 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics. Figure 7: The Euclidean distance. sessions, purchase revenue, transactions, conversions, and bounce rate, is re-quired (Table 4). Table 5 shows the Shapiro-Wilk Test for normality results. A _p_-value below 0.05 indicates strong evidence against the null hypothesis and a deviation from normal distribution, necessitating the use of non-parametric methods for analysis (Ta-ble 5) [33, 34, 35, 36, 37]. Table 6 includes the models' configurations which is essential to describe the performance comparison for the DTs, NB, and k-NN classifiers (Table 6) [33, 34, 35, 36, 37]. Table 7 demonstrates the class segmentation based on the user engagement level from the total number of instances separating the classes into \"low engagement\", \"medium \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Value** & **Description** \\ \hline DTs & Algorithm & Decisiontreoclassifier & \\  & Random\_state & 42 & Ensures reproducibility \\  & Criterion & ‘Gini’ & Measure of split quality \\  & Splitter & ‘Best’ & Strategy used to choose the split \\  & Max\_depth & None & Nodes expanded until all leaves \\  & & & are pure \\  & Min\_samples\_split & 2 & Minimum number of samples \\  & Min\_samples\_leaf & 1 & Minimum number of samples \\ \hline NB & Algorithm & Gaussianb & \\  & Priors & None & Prior probabilities of the classes \\  & Var\_smoothing & \(1\times 10^{-9}\) & Stability added to variances \\ \hline k-NN & Algorithm & k-neighbors classifier & \\  & N\_neighbors & 5 & Number of neighbors to use \\  & Weights & ‘Uniform’ & Weight function used in \\  & Algorithm & ‘Auto’ & Algorithm used to compute \\  & & & nearest neighbors \\  & Leaf\_size & 30 & Leaf size passed to BallTree or \\  & & & KDTree \\  & P & 2 & Power parameter for the \\  & & Minkowski metric & Minkowski’ \\  & Metric & ‘Minkowski’ & Distance metric used for the tree \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model configurations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Net-Statistic** & _p_**-Value** \\ \hline Event Count & 0.384 & 0.000 \\ Sessions & 0.247 & 0.000 \\ Purchase Revenue & 0.042 & 0.000 \\ Transactions & 0.058 & 0.000 \\ Conversions & 0.058 & 0.000 \\ Bounce Rate & 0.660 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Normality test. engagement\", and \"high engagement\" based on the number of completed sessions created by the customers. A \"class\" column that directly reflects the sessions values. Using sessions as the \"class\" column helps classify user engagement in a straightforward way. This solution leverages the number of sessions that denote interactions that a user has with the website. After defining the level of engagement, it is a clear way for useful insights for personalized marketing strategies.