Q) Write the necessary python code (spyder python) to read the dataset file given and perform classification using the stacking ensemble learning. Note that the weak learners are Decision Tree, Support Vector Machine and KNN, while the meat learner is a Neural Network. Finally draw the confusion matrix and compute the Accuracy, Recall, Precision and F1-Measure.

show the output

\begin{tabular}{|c|c|c|c|c|} \hline variance & skewness & curtosis & entropy & class \\ \hline 3.6216 & 8.6661 & -2.8073 & -0.44699 & 0 \\ \hline 4.5459 & 8.1674 & -2.4586 & -1.4621 & 0 \\ \hline 3.866 & -2.6383 & 1.9242 & 0.10645 & 0 \\ \hline 3.4566 & 9.5228 & -4.0112 & -3.5944 & 0 \\ \hline 0.32924 & 4,4552 & 4.5718 & -0.9888 & 0 \\ \hline 4.3684 & 9.6718 & -3.9606 & -3.1625 & 0 \\ \hline 3.5912 & 3.0129 & 0.72888 & 0.56421 & 0 \\ \hline 2.0922 & -6.81 & 8.4636 & -0.60216 & 0 \\ \hline 3.2032 & 5.7588 & =0.75345 & =0.61251 & 0 \\ \hline 1.5356 & 9.1772 & -2.2718 & -0.73535 & 0 \\ \hline 1.2247 & 8.7779 & -2.2135 & -0.80647 & 0 \\ \hline 3.9899 & -2.7066 & 2.3946 & 0.86291 & 0 \\ \hline 1.8993 & 7.6625 & 0.15394 & -3.1108 & 0 \\ \hline-1.5768 & 10.843 & 2.5462 & -2.9362 & 0 \\ \hline 3.404 & 8.7261 & -2.9915 & =0.57242 & 0 \\ \hline 4.6765 & -3.3895 & 3.4896 & 1.4771 & 0 \\ \hline 2.6719 & 3.0646 & 0.37158 & 0.58619 & 0 \\ \hline 0.80355 & 2.8473 & 4.3439 & 0.6017 & 0 \\ \hline 1.4479 & -4.8794 & 8.3428 & -2.1086 & 0 \\ \hline 5.2423 & 11.0272 & -4.353 & -4.1013 & 0 \\ \hline 5.7867 & 7.8902 & -2.6196 & -0.48708 & 0 \\ \hline 0.3292 & -4.4552 & 4.5718 & -0.9888 & 0 \\ \hline 3.9362 & 10.1622 & -3.8235 & -4.0172 & 0 \\ \hline 0.93584 & 8.8855 & -1.6831 & -1.6599 & 0 \\ \hline 4.4338 & 9.887 & -4.6795 & -3.7483 & 0 \\ \hline 0.7057 & -5.4981 & 8.3368 & -2.8715 & 0 \\ \hline 1.1432 & -3.7413 & 5.5777 & -0.63578 & 0 \\ \hline-0.38214 & 8.3909 & 2.1624 & -3.7405 & 0 \\ \hline 6.5633 & 9.8187 & -4.4113 & -3.2258 & 0 \\ \hline 4.8906 & -3.3584 & 3.4202 & 1.0905 & 0 \\ \hline \end{tabular}