COMP809代写、代写c++,Java程序

日期：2022-08-15 02:06

COMP809: Data Mining & Machine

Learning

Assignment 1 (weight: 40%)

Semester 2 2022

Data Mining Data Exploration and Analysis

This is an individual assignment.

Submission: A soft copy needs to be submitted through Canvas. Include your actual clean code (no

screenshot) in an Appendix with appropriate comments for each task.

Due date: Sunday 28 August 2022 at midnight NZ time.

Late penalty: maximum late submission time is 24 hours after the due date. In this case, a 5% late

penalty will be applied.

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AIMS

This assignment provides an opportunity to solve two real-world 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. Your answers below need to be supported by suitable evidence,

wherever appropriate. Some examples of suitable evidence are the Confusion

Matrices, Model Visualizations and Summary Statistics.

Study Area I ( bank.csv use the Bank.zip)

This application is concerned with predicting the outcome of direct bank marketing

campaigns (phone calls) of Portuguese banking. The dataset contains 17 attributes for

which outcomes of subscribing to a term deposit (yes/no) on a term deposit are known.

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.

a) Describe the pre-processing steps you have performed to prepare your data and

perform initial data exploration by analysing the summary statistics of the dataset

attributes. [5 marks]

b) Using an appropriate method to identify the most influential features in

classifying this dataset. Explain the process of the chosen feature selection

method and use the top four features for building your model. Use a

‘breakdown’ analysis for selected features by the class and describe their

distribution using appropriate plot(s). [10 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 15 nodes

to improve the interpretability of the model generated. Analyse your findings

and discuss the results. Visualise the final generated decision tree and describe it.

[10 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. [5 marks]

e) Provide and carefully examine the confusion matrix. Generate and provide a

classification report, showing precision, recall, F1 and overall accuracy, to

evaluate your model performance. Describe your findings, is there any

significant finding? [5 marks]

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Study Area II (Autism-Child-Data)

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 individual 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 are required to use both the Na?ve Bayes (NB) and Decision Tree

classifier algorithms to build a predictive model for the ASDS.

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). [5 Marks]

b) Describe the pre-processing steps and perform initial data exploration. 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 selection method was used. Use a ‘breakdown’ analysis for selected

features by the class and describe their distribution using appropriate plot(s).

[15 marks]

c) Discuss the independence assumption between the features in Na?ve Bayes (NB)

algorithm and support your answer concerning the selected features.

[5 marks]

d) Run the Na?ve Bayes algorithm with the GaussianNB implementation for the

selected features. Provide the evaluation metrics including the confusion matrix

showing the performance of the NB model. Discuss the results. [10 marks]

e) Run the Decision Tree Classifier algorithm and compare the top five features

produced by the Decision Tree model with the list selected in part (b). Identify

similarities and differences. Discuss any differences. [10 marks]

f) Provide the evaluation metrics including the confusion matrix showing the

performance of the Decision Tree model. Compare the performance of your

models (NB and Decision Tree) and discuss your findings. [5 marks]

There will be 5 marks for the presentation of the assignment including spelling and

grammar, layout, formatting, and readability of the figures.