This is related To Machine Learning)

You will predict survivors of the Titanic disaster (titanic.csv) by comparing the performance of decision trees and neural network algorithms. The dataset is included as titanic-1.csv. The features are the passenger – ticket – class, sex, age, number – of – siblings – in – ship, number – of – parents – in – ship, embarking – port and the label indicates whether they survived or not. You can use either Weka or python sklearn for this (or any other software you may prefer). In Weka, you can use the J48 algorithm and the MultiLayerPerceptron. In sklearn you can use the decisiontreeclassifier and the MLPClassifier. Use 1 hidden layer for the neural network. Vary the pruning parameter in decision trees (in J48, there is a parameter called confidenceFactor and for decisiontreeclassifier you can specify the “max depth ” or the “min samples split”). Use the validation set (in both weka and sklearn) to determine when to stop training the neural network. Report the best 10-fold cross validation results (average precision, recall and F1-score) for both classifiers. Which model performed better overall? Which model took longer to train?