## 6 Discussion  
This study established an ensemble-based hybrid model using supervised and unsupervised learning approaches. This suggested framework produces the following results. Experiment 1 (Figure 2) reveals that k-medoids clustering technique achieved top performance of 75.44% and 75.56% for Orange and Cell2 Cell datasets. Henceforth, in experiment 2 (Figure 3), we applied single classification algorithms are employed and obtain following results, 92.98%, 93.19%, 85.4%, 87.04%, 86.62%, 88.24%, 91.38%, 92.41%, 86.02%, 87.79% for GBT, DT, RF, DL and NB on both Orange and Cell2 Cell datasets, respectively. In single classification algorithms, GBT outperforms other classifiers in terms of different evaluation matrix accuracy. In experiment 3 (Figure 4), a combination of k-medoids and single classification algorithms have been accomplished. k-medoids-GBT achieved the highest results of 94% and 92.25% accuracy on both datasets. k-medoids-DL model perform well and obtained the second position by getting 93.5% and 92.53% accuracy on both datasets. Next, in experiment 4 (Figure 5), a voting-based hybrid classifier with the k-medoids clustering technique has been applied to check the effect on accuracy and other measures. k-medoids-GBT-DT-DL achieved 95.06% accuracy on the Orange dataset and 93.40% accuracy on the Cell2 Cell dataset. The k-medoids-GBT-DT-NB model also gets better results as compared to other hybrid models in experiment 4. In experiment 5 (Figure 6a,b), bagging-based hybrid classifiers with k-medoids ensemble models was evaluated. The k-medoids-GBT-DT-DL model gives a superior performance of 95.12% and 93.43% accuracy on both datasets as compared to all other models. Similar to experiment 4, the k-medoids-GBT-DT-NB model performs better after k-medoids-GBT-DT-DL with accuracies of 93.64% and 92.98% as compared to other combinations. Lastly, in experiment 6, stacking-based hybrid classifiers with k-medoids ensemble models have been developed to enhance the performance. It can be seen from bar graphs Figure 6c,d) similar to experiment 5 k-medoids-GBT-DT-DL combination obtained the highest results as compared to all other combinations applied on Orange and Cell2 Cell dataset. k-medoids-GBT-DT-DL achieved 95.34% accuracy, 77.09% recall, 83.51% precision, 80.17% f-measure for Orange dataset and 93.43% accuracy, 67.45% recall, 79.10% precision, 72.81% f-measure for Cell2 Cell dataset. Furthermore, this study compares existing methods with our proposed ensemble method in the form of Table 2. Table 2 illustrates that the proposed approach for CCP outperforms traditional approaches. Moreover, Figure 7 depicts the accuracy comparison among all methods used in our study. The bar graph shows the lowest performance by single clustering k-medoids algorithm and Hybrid 4(k-medoids-Stacking-GBT-DT-DL) algorithm obtain extremely significant results on both datasets. It is seen that the proposed stacking-based ensemble algorithm achieves the highest accuracy rate among all experiments. The experimental results indicate that the proposed ensemble model evaluated on Orange dataset has achieved 96%, 91.61%, 90.23% of accuracy, recall and F-measure respectively, while this proposed ensemble model evaluated on Cell2 Cell dataset has acquired 93.6%, 85.45%, 83.72% of accuracy, recall and F-measure respectively. To summarize this discussion, the results of all experiments demonstrate that hybrid models have always outperformed single clustering/classifier-based approaches. On both datasets, the proposed model exhibits superior classification performance in terms of accuracy, recall(sensitivity) and F-measure. Multiple existing techniques were applied to both datasets used in this research, however, our model stacking-based ensemble k-medoids-GBT-DT-DL model produced higher outcomes.