This assignment gives you an opportunity to solve two real-word data mining
problems using the machine learning workbench. In the two questions given below
justification of your answers carries a high proportion of the marks awarded. You are
required to conduct experiments for both case studies and report them according to
the specified requirements.
1. Study Area I (Dataset is bank.csv use the Bank.zip)
This application is concerned with predicting the outcome of direct bank marketing
campaigns (phone calls) of a Portuguese banking. The dataset contains 17 attributes
for which outcomes of subscribing to a term deposit (yes/no) on a term deposit are
You are required to build a model using the Decision Tree Classifier and answer the
following questions based on the model built. Use the data segment on the
subscriptions whose outcomes are known. In building the model, use the 10-fold
cross validation option for testing.
Your answers below need to be supported by suitable evidence, wherever
appropriate. Some examples of suitable evidence are the Confusion Matrices, Model
Visualizations (from Python, Weka, MATLAB, or any other tools) and Summary
a) Describe the pre-processing you have performed to prepare your data.
b) Using an appropriate method identify the top five most influential features in
classifying this dataset. Explain the process of the chosen feature selection
method. [5 marks]
c) Now build a model using the Decision Tree algorithm. By adjusting two
suitable parameters (one at a time) reduce the size of the tree to not more than
10 to 15 nodes in order to improve the interpretability of the model generated.
Which of the two parameters yielded better accuracy while producing smaller
trees? [5 marks
d) Describe the role of the two parameters in the model building that you used
in c) above. Do you expect that manipulating the parameter, in the same way,
will improve accuracy for other types of datasets? Justify your answer.
e) Provide and carefully examine the Confusion Matrix. You will notice that the
client subscribed to a term deposit (yes) outcome is significantly smaller than
the (no) outcome. Why do you think this happens? Will a suitable
visualization help to explain this phenomenon?
2. Study Area II (Dataset is Autism-Child-Data.arff)
This application is from the medical domain and is concerned with the diagnosis of
childhood Autistic Spectrum Disorder Screening (ASDS) for a collection of
individuals from whom relevant medical data has been obtained. The dataset contains
10 behavioural features (AQ-10-Child), 10 individuals characteristics, and the
outcome (effectiveness of detection). The objective is to predict whether the given
individual characteristics are effective in detecting ASD cases. The effectiveness of
ASDS detection is labelled as ‘Yes’ or ‘No’ in this dataset.
For this dataset, you will also use both the Decision Tree classifier and Naïve Bayes
(NB) algorithms to build a predictive model for the ASDS. For both methods use the
10-fold cross validation option for testing.
a) Describe what is an autism spectrum disorder (ASD) and discuss the significance
of the early diagnosis of ASD. Briefly describe the Autism-Spectrum Quotient
(AQ) and include two recent references to support your answer (no more than one
page). [6 Marks]
b) Use an appropriate method of feature selection to identify the top five
significant features. State the method used and list the features produced and
explain why this feature reduction method was used. Discuss the independence
assumption between the features in Naïve Bayes (NB) algorithm and support your
answer with reference to the selected features.
c) Run the Naïve Bayes algorithm with the GaussianNB implementation for the
selected features. Provide the metrics to evaluate the performance of the NB
model and discuss the results. [8
d) Run the Decision Tree Classifier algorithm and compare the list produced in part
(b) with the top five features produced by the Decision Tree model. Identify
similarities and differences. Discuss any differences. [10 marks]
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