Sale!

Introduction to Data Science Assignment 2 

$30.00

Category:
Rate this product

CSI4142 Introduction to Data Science
Assignment 2
Total: 70 marks
Instructions:
Submit your assignment thought the Virtual Campus. This is an individual assignment.
The goal of this assignment is to explore relevant concepts related to machine learning and data mining.
Answer all the following questions.
For questions 1 and 2, consider the following table that contains the partial data and schema of a
Customer profiling database owned by a Bank. The Mortgage is the target class (or so-called label).
age gender income children mortgage
48 FEMALE $17,546.00 1 NO
40 MALE $30,085.10 3 YES
51 FEMALE $16,575.40 0 NO
23 FEMALE $20,375.40 3 NO
57 FEMALE $50,576.30 0 NO
57 FEMALE $37,869.60 2 NO
22 MALE $8,877.07 0 NO
58 MALE $24,946.60 0 NO
37 FEMALE $2,500,304.30 2 NO
54 MALE $24,212.10 2 NO
66 FEMALE $59,803.90 0 NO
52 FEMALE $26,658.80 0 YES
44 FEMALE $15,735.80 1 YES
66 FEMALE $55,204.70 1 YES
1. Show all the steps you would follow to determine the first attribute to split on for the data
included in the table, using the information gaincriterion. (10)
2. Suppose that we remove the “Mortgage” class label from the dataset. Show all the steps you
would follow when applying the k-means cluster analysis algorithm to the data, with k = 3. (10)
Use the Customer.CSV file attached to this assignment to answer the following questions.
3. Apply the C4.5 (J48 in WEKA) decision tree algorithm to this data. Show your resultant confusion
matrix and comment on the accuracy, precision, recall, sensitivity and specificity of the model
you constructed. (10)
4. This dataset is imbalanced and the initial results are therefore poor. Explain how you would
address this issue during preprocessing, training and model evaluation. (10)
5. Reapply the C4.5 decision tree algorithm to your modified data and determine whether your
modifications have improved the performance. (10)
6. Ensembles of classifiers, or so-called meta-leaners, are often used in order to improve the
accuracy of base learners such as decision trees. Explore whether applying a Boosting ensemble,
such as AdaBoost, to this dataset improve the performance. Show your results and discuss your
findings. (10)
7. Explain what a global outlier is and suggest an algorithm that you could use to identify a global
outlier in the dataset. List one global outlier that you found. (10)

Open chat
Need help?
Hello
Can we help you?