how would you characterize these three hidden factors identified by the neural network analysis

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

1- Clustering a. Perform SimpleKMeans clustering with default parameters (2 clusters). How would you describe the two clusters based on the attribute characteristics? Interpret how the identified clusters are different based on average attribute values. Which attributes were more important to differentiate the clusters? b. Perform SimpleKMeans clustering with three clusters. How would you describe the three clusters based on the attribute characteristics? Discuss which subsets of the population each cluster represents.

2- Neural Networks

a. Perform neural network analysis (MultilayerPerceptron) with two hidden layers (“hiddenLayers”=2). What is the overall prediction accuracy? Identify the attributes that significantly impact each of the two hidden nodes. How would you characterize these two hidden factors identified by the neural network analysis?

b. Repeat the same analysis with three hidden layers. What is the new prediction accuracy? Interpret the confusion matrix. Why do you think the accuracy is different? Identify the attributes that significantly impact each of the three hidden nodes. How would you characterize these three hidden factors identified by the neural network analysis?

3- Association Rule Mining Convert all numerical attributes to nominal by using the Unsupervised/Attribute/Discretize filter. Create 3 bins for each of the numerical attributes when converting to nominal, set “useEqualFrequency” parameter to True.

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