## 5 Data Analysis and Results
### Machine Learning Techniques' Validation and Predictions  
The study evaluates five machine learning (ML) classification techniques, which take inherited information from a dataset's input and convert it into a desired output pattern [81]. The five ML models utilized to create and assess models for the YouTube dataset application were ANN [82], linear regression [83], SMO [84], bagging using the REFTree model [85], and random forest [86]. The back-propagation approach is used by the ANN to estimate the error values between the prediction and actual output values. The error is then used to change the weights and bias settings of the ANN architecture, bringing the predicted and real values closer together. The linear regression model is a polynomial function with weighted coefficients for the predictor factors and an outcome that is target dependent. The training phase updates the coefficients of the linear function from the training dataset. The SMO updates the weighted vectors of the SVM model using the sequential minimal optimization algorithm. From a random sampling of the objects and characteristics in the training set, the bagging technique builds several REFTree models, with the average value of the trees providing the final predicted value. The random forest (RF) is a decision \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **(I)** **Education** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **(J)** **Education** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **Std. Error** \\ **Difference (I-J)** \\ \end{tabular} & \
\begin{tabular}{c} **Sig.** \\ **Lower Bound** \\ \end{tabular} &
\begin{tabular}{c} **Upper Bound** \\ \end{tabular} \\ \hline \multirow{2}{*}{Bachelor} & Master & \(1.23413\)* & \(0.06736\) & \(0.000\) & \(1.0759\) & \(1.3923\) \\ \cline{2-7}  & PhD & \(0.40629\) & \(0.23306\) & \(0.190\) & \(-0.1411\) & \(0.9537\) \\ \hline \multirow{2}{*}{Master} & Bachelor & \(-1.23413\)* & \(0.06736\) & \(0.000\) & \(-1.3923\) & \(-1.0759\) \\ \cline{2-7}  & PhD & \(-0.82785\) & \(0.23321\) & \(0.001\) & \(-1.3756\) & \(-0.2801\) \\ \hline \multirow{2}{*}{PhD} & Bachelor & \(-0.40629\) & \(0.23306\) & \(0.190\) & \(-0.9537\) & \(0.1411\) \\ \cline{2-7}  & Master & \(0.82785\)* & \(0.23321\) & \(0.001\) & \(0.2801\) & \(1.3756\) \\ \hline \hline \end{tabular}  
* The mean difference is significant at the \(0.05\) level.  
\end{table} 
Table 11: Multiple comparisons analysis of the information adoption (IA) attributed to education. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **(I)** **Previous** \\ **Experience** \\ \end{tabular} & \begin{tabular}{c} **(J)** **Previous** \\ **Experience** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Difference (I-J)** \\ \end{tabular} & \begin{tabular}{c} **Std. Error** \\ **Difference (I-J)** \\ \end{tabular} & \
\begin{tabular}{c} **Sig.** \\ **Lower Bound** \\ \end{tabular} &
\begin{tabular}{c} **95\% Confidence Interval** \\ **Upper Bound** \\ \end{tabular} \\ \hline \multirow{2}{*}{Low} & Good & \(1.18695\)* & \(0.07342\) & \(0.000\) & \(1.0145\) & \(1.3594\) \\ \cline{2-7}  & Excellent & \(-0.59948\)* & \(0.07084\) & \(0.000\) & \(-0.7659\) & \(-0.4331\) \\ \hline \multirow{2}{*}{Good} & Low & \(-1.18695\)* & \(0.07342\) & \(0.000\) & \(-1.3594\) & \(-1.0145\) \\ \cline{2-7}  & Excellent & \(-1.78642\)* & \(0.06610\) & \(0.000\) & \(-1.9417\) & \(-1.6312\) \\ \hline \multirow{2}{*}{Excellent} & Low & \(0.59948\)* & \(0.07084\) & \(0.000\) & \(0.4331\) & \(0.7659\) \\ \cline{2-7}  & Good & \(1.78642\)* & \(0.06610\) & \(0.000\) & \(1.6312\) & \(1.9417\) \\ \hline \hline \end{tabular}  
* The mean difference is significant at the \(0.05\) level.  
\end{table} 
Table 12: Multiple comparisons analysis of the information adoption attributed to previous experience. tree (DT) model with a random sampling of training data items and random attribute subsets for each subtree.